Grand Challenge Project Proposal for the iPlant Collaborative
Fundamental Understanding of Photosynthesis through Integration of
Complex Molecular Data and Modeling Tools
Principal
Leaders
Thomas Brutnell
(Contact Person)
Richard
Bruskiewich
Xinguang Zhu
Christopher
Myers
Martin Gent
Pankaj Jaiswal
Shaoying Kathy
Lu
Title, Affiliation & Contact Details
Expertise/Role
Associate Scientist, Boyce Thompson
Institute, Cornell University, Ithaca, NY;
Ph: +1 (607) 254-8656;
Email: [email protected]
Senior Scientist, Computational &
Systems Biology, Crop Research
Informatics Laboratory and Applied
Photosynthesis and Systems Modeling
Laboratory, International Rice Research
Institute (IRRI; www.irri.org)
Ph: +1 (650) 833-6620
Email: [email protected]
Group leader, Plant systems biology,
photosynthesis research, CAS - MPG
Partner Institute of Computational
Biology (PICB; www.picb.ac.cn), SIBS,
Shanghai, China
Ph: +86 (21) 549-204-58
Email: [email protected]
Plant molecular biology/genetics.
Source of primary high-throughput leaf
transcriptome and metabolome data
sets. Outreach efforts.
Genomics, bioinformatics,
computational & systems biology /
Development of infrastructure for
functional genomics data analysis &
integration; software engineering for
crop information systems; source of
rice and sorghum mutant data sets,
with photosynthesis measurements.
Research Scientist, Institute for Genomic
Biology, National Center for
Supercomputing Applications, University
of Illinois at Urbana Champaign, IL,
61801
Email: [email protected]
Senior Research Associate,
Computational Biology Service Unit, Life
Sciences Core Laboratories Center,
Cornell University, Ithaca, NY
Ph: +1 (607) 255-5894
Email: [email protected]
Agricultural Scientist, Connecticut
Agricultural Experiment Station,
New Haven, CT
Ph: +1 (203) 974 8489
Email: [email protected]
Assistant Professor, Department of
Botany and Plant Pathology; Center for
Genome Research and Biocomputing,
Oregon State University, Corvallis, OR
Ph: +1 (541) 737-8471; Email:
[email protected]
Research Scientist, Department of
Bioengineering, University of Illinois at
Urbana Champaign, 61801
Email: [email protected]
Computational & systems biology /
Development of modeling framework
for photosynthesis; Source of some
high throughput photosynthesis
measurements
Computational physics & systems
biology / Development of modeling
framework for photosynthesis and
linking to infrastructure for data
analysis of high throughput data sets
Whole plant physiology / Data and
models for leaf and canopy
photosynthesis; transport of water and
metabolites, composition of plants as
affected by light and temperature
Comparative plant genomics, genome
annotation, genome and pathway
databases / Development of
infrastructure for high throughput leaf
sequence, transcriptome, proteome
and metabolome data sets
Applied mathematics & high
performance computing / Development
of 3 dimensional simulation package of
reaction diffusion system
Project Summary
The success of the green revolution in the 1960s and 1970s led to dramatic increases in world food
production. However, to ensure that agricultural systems can meet future production needs it is imperative to
overcome physiological barriers to crop yield and improve the resiliency of crops to adverse conditions such as
climate change. The most obvious physiological limitation to crop yield is imposed by the fundamental process by
which green plants harvest sunlight to capture carbon dioxide in the atmosphere and bind it into sugar, which the
plant then uses as a substrate for further synthesis of its biomass, the process called “photosynthesis”.
Project Goals and Objectives
The proposed project will have the goal of providing innovative cyberinfrastructure for scientific research
into the process of photosynthesis, thus greatly advancing our capacity to solve two global challenges in plant
biology:
a) Enhancing photosynthetic efficiency in crop plants of agronomic importance; and
b) Identifying a strategy to convert agronomically important plants, such as rice and wheat, to use the more
efficient variant of photosynthesis which is found in species like maize and sorghum.
To achieve the above scientific objectives, we envision the following five key project deliverables:
1. A community portal for photosynthesis research
2. A public data repository for photosynthesis research.
3. A knowledge discovery environment for photosynthesis systems biology.
4. A simulation environment for systems modeling of photosynthesis.
5. An education, outreach and training portal for photosynthesis science.
Outreach and Training for the Plant Science Community
The outreach component of this project will strive to develop capacity for computational science in
photosynthesis research within plant scientific community with a number of activities:
•
•
•
Develop training materials and host training workshops (in the USA) for photosynthesis modeling.
Incorporate international exchanges (targeting photosynthesis research scientists, in particular, from
developing countries) into GC project activities, with photosynthesis research team members such as
photosynthesis modeling experts, graduate students and postdoctoral scientists from within the US and
abroad.
Develop (US) national curriculum resources (high school and undergraduate level) for training in plant
cyberinfrastructure relating to computational systems biology modeling and photosynthesis research.
The project will also broaden public understanding of science through the following activities:
•
•
•
Education and information specifically targeting photosynthesis: developed and delivered for many cognitive
levels, such as primary, secondary, and post-secondary schools, for general information by an educated lay
public (e.g. wiki content); and as a tool for policy makers.
Development of a simplified simulator of leaf and/or whole plant photosynthesis, as a means for science
education.
“Summer schools” in plant photosynthesis modeling for senior high school and undergraduate students to
participate in some of the laboratories developing or applying the cyberinfrastructure from this project.
Introduction: Grand Challenge of Photosynthesis
The success of the green revolution in the 1960s and 1970s led to dramatic increases in world
food production. However, with the exception of maize, production increases have been extremely
limited in cereal crops over the past ten years. Increasing world population, global climate change and
increasing energy costs are together creating a crisis in the global food supply. Recent estimates are
that world wheat stockpiles are at a 30 year low [1] and in April 2008, rumors of a rice shortage resulted in
riots in 30 countries throughout the world. This tenuous situation has been brought about in large part by
global population growth that is expected to exceed 7 billion within the next ten years. The transformation
of the large Chinese and Indian economies resulted in never-before-seen demands on grain supplies
accompanying growth of the middle class. Indeed, despite the recent collapse of global financial markets,
commodity prices for rice, wheat and soy remain at near historic highs [2, 3]. To ensure that agricultural
systems can meet future production needs it is imperative to overcome physiological barriers to crop yield
and improve the resiliency of crops to adverse conditions such as climate change [4].
Yield in crop plants may be generally modeled by the equation [5]:
Y = H ∫ ε Iint(t) dt
where
Y
H
ε
Iint
yield
harvest index
radiation use efficiency (a function of development or time)
intercepted photosynthetic active radiation (PAR)
and the integration of the function is over the time
from germination to harvest. Intercepted PAR is
primarily a function of climate and weather during
growth, but is also influenced by plant architecture
(e.g. erectness of leaves). In many elite crops,
morphological traits relative to efficient PAR
interception have been relatively well optimized. The
timing of development from germination to
senescence has some genetic diversity, but in
general, is not a convenient variable to be
maximized: farmers generally desire shorter rather
than longer growing seasons, to reduce risk of crop
loss. Although significant efforts have been made in
breeding for superior yield components, harvest
index is relatively invariant within species. Indeed,
Figure 1. Photosynthetic Energy Efficiency
recent observations suggest that crops like rice are
source not sink limited [6]. This analysis leads to the
conclusion that fundamental improvements to radiation use efficiency, that is, enhanced efficiency of
photosynthetic capture of CO2 into sugar, is likely the ultimate target for manipulation to increase yield [5].
One of the major barriers to yield in many crop plants is the relative inefficiency of solar energymediated capture of CO2 into sugar [5, 7, 8]. Theoretical analysis suggested that the maximal radiation
use efficiency, εc, for C3 and C4 plants are 4.6% and 6%, respectively; however, actual efficiency barely
exceeds 0.5% in the field over an entire growing season (Figure 1; [9]. Current models of
photosynthesis suggest several avenues of biological engineering to increase photosynthesis energy
conversion efficiency (Table 1).
A full understanding of photosynthesis would allow improvement of existing photosynthetic
processes in crops, and to possibly enable genetic engineering of C3-type photosynthetic systems toward
the more efficient C4-type photosynthesis. This will require the efficient integration and sensible
interpretation of voluminous and complex data and models, spanning a range from genomic data through
crop eco-physiological responses. Now is the time to achieve such an understanding through powerful
comparative genomics approaches. Experimental approaches are rapidly evolving for the functional
1
characterization of completed genomes, such as the model dicot, Arabidopsis thaliana, and model
monocot, Oryza sativa. These data are being supplemented by significantly completed genome
sequences for at least two major of C4-type photosynthetic species, Sorghum bicolor [10] and Zea mays
(maize).
Table 1 – Potential Alterations to Crop Photosynthesis to Enhance Energy Conversion Efficiency
Projected
Range of %
increase in εc
Speculated
Time
Horizon (yr)
Citation
Improved canopy architecture
0 - 40%
0-10
[11]
Rubisco with decreased oxygenase activity
5 - 60%
?
[12]
Increased rate of recovery from
photoprotection of photosynthesis
6 - 40%
5
[13]
Introduction of higher catalytic rate foreign
forms of Rubisco
17 - 30%
5-10
[12]
Altered allocation of resources within
photosynthetic apparatus
0 - 60%
0-5
[14]
25% - 50%
15-30
[9]
Alteration
C4 photosynthesis engineered into C3 crops
GC Project Goal
To develop a novel discovery environment (“DE”) of computational tools to assist scientific research
directed toward the improvement of photosynthesis to enhance crop yields. The above Grand Challenge
(“GC”) project goal will support two major scientific objectives:
1.
To develop photosynthesis process models and simulations at multiple scales of spatial and
temporal resolution and establish their underlying genotype-to-phenotype linkages, in order to
identify opportunities for genetic manipulation or breeding to enhance energy conversion
efficiency in existing C3 and C4 systems.
2.
To identify strategies to convert the photosynthetic system of C3 crops into the more efficient C4
type photosynthetic system.
These scientific objectives require the systematic development and application of a sophisticated
plant photosynthesis research cyberinfrastructure (“CI”) that would allow multidisciplinary teams of plant
scientists to collaborate through models of the process and differentiation of photosynthesis, in order to
gain a quantitative understanding of the system efficiency and its formation.
GC Project Objectives
To achieve the above scientific objectives, the project will develop the following five CI resources:
1. A community portal for photosynthesis research.
2. A public data repository for photosynthesis research.
3. A knowledge discovery environment for photosynthesis systems biology.
4. A simulation environment for systems modeling of photosynthesis.
5. An education, outreach and training portal for photosynthesis science.
Scientific Approach
Enhancement of Existing Photosynthetic Processes
Models of photosynthesis have proven useful to predict photosynthetic performance and provide
opportunities for engineering higher efficiency. For example, detailed simulation model of carbon
metabolism suggested an opportunity to increase photosynthesis output by increased expression of some
2
genes, such as sucrose-bis-phosphatase (SBPase) (Figure 2a). These predictions were realized in
tobacco plants overexpressing SBPase (Figure 2b).
Figure 2. a) (Left) Model of Carbon
Metabolism [14]. The red box denotes a
reaction catalyzed by SBPase for which
model simulations indicated increased
activity could increase the amount of
carbon capture.
b) (Below) Raines and colleagues [15]
transformed tobacco with an SBPase
enzyme. High expression of the
transgenic enzyme increased biomass
growth relative to a control plant.
Critical milestones to the achievement of the first objective of
enhancing photosynthetic systems are:
1. Enumeration and quantification of the contribution of
different factors (biochemical, biophysical or anatomical)
that control the efficiency of photosynthetic energy
conversion.
2. Determination of how the major developmental events in photosynthesis are coordinated in the
differentiation of leaf tissues.
3. Identification of the regulation of metabolite levels in photosynthetic pathways, both during
development and differentiation, and also to maintain homeostasis in mature tissues.
4. Identification of the genotype-phenotype linkages underlying regulatory and metabolic pathways of
photosynthesis, and their response to environmental stress related to water and temperature.
5. Identification of genetic options to incrementally improve the energy conversion efficiency of
photosynthesis and robustness of photosynthesis under stress.
To achieve these goals we envision the following critical modeling needs:
1. Establish parameterized reference models of photosynthesis:
a. Develop a baseline photosynthesis model at the cellular level, using existing expertise and
knowledge at the levels of genome, metabolism, and cell anatomy.
b. Estimate model parameters for selected plant species based on photosynthetic
measurements of these species.
c. Conduct ab initio parametric sensitivity analysis of integrated model to identify parameters
that have biggest impact on photosynthetic performance
2. Use structural and functional genomic approaches - e.g. mutant analysis and available functional
annotation in model plants like Arabidopsis and rice to dissect the genetic control (“candidate genes”
and regulatory elements) of leaf anatomy and photosynthetic biochemistry.
3
3. Integrate photosynthesis performance measurements, and apply high-throughput parameter
estimation, to additional selected collections of plant germplasm; domesticated and wild plant
1
species, and well-characterized (i.e. QTL or genotyped) core collections of plant germplasm .
Measure photosynthesis for such germplasm under various stress treatments. Molecular variation
(“alleles”) in candidate photosynthesis genes and their regulatory elements should be associated with
model parameters.
4. Confirm parametric sensitivity analysis of integrated models using results from the above analysis
across diverse germplasm, including model response under stress. Assess candidate genes and
alleles for impact on photosynthetic performance of measured plants.
Through the development of these models, it will be possible to identified genes and alleles conferring
superior photosynthetic performance. Promising alleles could then be incorporated into breeding
programs for further testing and possible deployment and to the specific engineering of proteins and
pathways for crop improvement.
Conversion of C3-type to C4-type Photosynthesis
In C3 plants carbon entering a cell is fixed by the chloroplast localized enzyme ribulose-1,5bisphosphate carboxylase oxygenase (Rubisco) into two three-carbon sugars. However, oxygen
competes with CO2 for the active site of Rubisco and when conditions are unfavorable for gas exchange
with the ambient air (i.e. hot, arid environments) stomates close, resulting in an internal O2 increase while
CO2 levels drop. The result is a loss of photosynthetic capacity in the form of photorespiration. As
temperatures increase, the capacity for Rubisco to interact with O2 increases and a dramatic reduction is
exhibited in CO2 fixation and photosynthetic efficiency. Calculations show that C3 plants can lose nearly
o
30% of potential carbon fixation due to photorespiration at temperatures higher than 30 C [16].
In the C4 pathway, atmospheric CO2 is initially fixed into C4 acids via phosphoenolpyruvate
carboxylase – PEPC, an enzyme that is insensitive to O2. Later, CO2 is released from the C4 acids for
fixation by Rubisco. The two stages are spatially separated in morphologically distinct photosynthetic
cells, mesophyll cells and bundle sheath cells. The resulting overall leaf architecture is called “Kranz
anatomy”. The active transport of C4 metabolites between cells and barriers to diffusion of CO2 are
critical to C4 photosynthesis. In C4 grasses such as maize and some C4 dicots, enlarged bundle sheath
(BS) cells surround the veins (V) and the BS cells are then surrounded by mesophyll (M) cells. M cell
chloroplasts support the capture of CO2 by PEPC, whereas BS chloroplasts support fixation of CO2 by
Rubisco. This separation allows a metabolic “CO2 pump” to develop a high concentration of CO2 in the
vicinity of Rubisco. The buildup of CO2 by this process requires extra energy, but the overall reduction in
energy loss is compensated by the fact that photorespiration is greatly reduced in C4 plants [17, 18].
Importantly, nearly all of the world’s most productive food, feed and bioenergy crops - including
maize, sorghum, sugarcane, Miscanthus and switchgrass – utilize C4 photosynthetic systems. The
physiological and morphological adaptations that accompany C4 photosynthesis also improves water use
and nitrogen efficiencies relative to C3 plants [19]. In nature, C4 appears to have evolved out of C3
lineages more than 50 times in a wide range of flowering plants and over 18 times in the grasses [20].
Evidence indicates that there is one consistent evolutionary pathway from C3 to C4 photosynthesis,
based on genera that have C3 species, C4 species, and species with intermediate traits [18]. The
frequency and apparent ease with which C4 evolves suggests plasticity inherent to the C4 syndrome. It is
important to point out that key enzymes of the C4 pathway are present in C3 plants, but their activities
and expression patterns are different [21]. Perhaps, a change in anatomy leads to changes in
biochemistry or vice versa. Identifying such mutations would provide alternative ways to C4 rice—
duplicating the evolutionary event. However, knowledge about the evolution and developmental genetics
of C4 photosynthesis is not yet sufficient to achieve this feat artificially, and this task remains a GC of
plant science.
1
For example, the OryzaSNP collection of genotyped rice germplasm.
4
Little is known about communication between M and BS cells in C3 or C4 plants. There are two
general hypotheses: either a signal emanates from veins and is perceived differently in cells closer to the
vein, or a signal is mediated by physical contact between BS and M cell walls [22]. To investigate the
mechanisms underlying C4 development several groups are conducting transcriptome, proteome,
metabolome and phenome surveys of maize, rice and sorghum (e.g. these and related studies are now
generating extensive data sets that can be mined to identify control points in photosynthetic
differentiation). However, the CI to fully exploit these data sets is currently not available.
To convert C3 to C4 photosynthesis, scientists will need to identify the genes and networks that
control the following:
1. Cell number between veins and the size and number of bundle sheath cells.
2. Chloroplast number, differentiation and distribution in mesophyll and bundle sheath cells.
3. The different metabolism in mesophyll and bundle sheath cells.
4. Subcellular features such as plasmodesmata density and structure, chloroplast membrane transport
systems and CO2 permeability that mediate the flux of metabolites between the mesophyll and bundle
sheath cells.
These research questions will require computational and systems biology methodology to synthesize a
large body of functional genomics data relating to C4 photosynthetic processes. Assuming that a
baseline C4 photosynthesis model is specified as a part of objective 1 above, then a plausible research
strategy to C4 photosynthesis will require the following:
1. Integrate, visualize and interpret comparative structural and functional genomics data to dissect C4
Kranz anatomy and biochemical pathways.
a. Compare -omics datasets of “C3 reverting” mutants of C4 plants with wild-type
b. Compare coding and regulatory genomic sequences in C3 and C4 species
c. Compare transcriptome, proteome, metabolome and phenome expression across developmental
stages and tissue (cell) types in reference genotypes in C3 and C4 species.
2. Use the integrated photosynthetic model to identify key genetic targets for modification, then design
and apply a suitable strategy for engineering of C4 photosynthesis into C3 crops.
The challenge of converting C3 -type photosynthesis into the more metabolically efficient C4 type has been taken up by the international scientific community (http://seeds.irri.org/C4rice). Through
Gates-Foundation funding, an international consortium will generate extensive data sets from –omics
level biological experiments. The main thrust of the funded research initially focuses on the elucidation
of Kranz anatomy underlying multi-cellular C4 pathway and engineering the C4 carbon cycle into rice
plants. Importantly, the long-term success of this project will depend, in large part, on defining key
regulatory points for manipulation and on a sophisticated analysis of molecular data sets to fill large gaps
in knowledge of this process. This demands a CI environment that is envisioned but is only partially
resourced within the Gates-funded consortium.
Current Barriers in Photosynthesis Research
Substantial barriers prevent an integrated discovery environment for photosynthesis research.
One major challenge is to develop a new modeling framework that includes a greater variety of processes
than has been developed to date. First, most existing cellular models generally only describe a single
cell. The C4 photosynthetic process requires two kinds of cells work together, so the model must handle
the multi-cellular nature of this system. In both C3 and C4 leaves, a gradient of light and CO2 across the
leaf requires integration of leaf tissue geometry and simulation of photosynthetic processes across
multiple layers of cells. Second, the model must handle multiple types of processes simultaneously.
Genetic control, biochemical metabolism, and trans-membrane transport and diffusion all play a role in
operation of the C4 photosynthetic system. We need to maximize ease-of-use of this modeling framework
to enhance the willingness of plant biologists to use modeling as a tool in their research. Most molecular
biologists cannot access and use quantitative modeling tools. There are several reasons for this:
5
•
Quantitative models are usually developed by scientists with a mathematics, physics, and
computational background. Hence, experimentalists find it difficult to understand and use them.
•
Models often lack methods to estimate the robustness and sensitivity of results to input errors.
•
Parameter estimation is difficult. Data sets are diverse, including many species and environments,
and the data are stored with multiple formats and diverse semantics.
•
Most plant biologists are reductionists, not accustomed to systems-oriented thinking.
•
Genetic regulation of anatomical parameters has not yet been related to photosynthesis.
Now is the time to assemble these different components in a quantitative model to analyze
system performance and the interaction between different components of the whole system. It is also
critical to develop a curriculum to train scientists to conduct integrative photosynthesis research based on
this cyber-infrastructure.
Scope of the Grand Challenge Project
The initial short-to-medium term scope of our project is limited to the development of CI to
support generic, parameterized, leaf-centric models focused on processes related to photosynthesis, for a
selected set of plant species. In future years the CI framework should ideally be extended to canopy level
analyses. Whole plant level processes such as the interaction of stem, root, and flowers - and extension
of models to other crops are beyond the scope of this proposal.
Discovery Environment (DE) for Photosynthesis Research
As indicated above, we envision five key project development objectives that characterize a
Discovery Environment (DE) for photosynthesis research:
1. To establish a community portal for photosynthesis research.
2. To establish a public data repository for photosynthesis research.
3. To build a knowledge discovery environment for photosynthesis systems biology.
4. To develop a simulation environment for systems modeling of photosynthesis.
5. To develop for education, outreach and training resources relating to photosynthesis science.
Web Portal for Photosynthesis Research
Although some early efforts toward this goal exist in the scientific community, there is not yet a
comprehensive web-based community portal for photosynthesis research. The lack of such a web portal
for plant biologists in general and the global photosynthesis research community in particular, may be a
hindrance to rapid advances in the field. This GC project therefore proposes the creation of such a web
portal as a specific output deliverable on the project. The proposed web portal will take advantage of socalled Web 2.0 collaborative technology to assist community wide coordination of photosynthesis
research activities. First, the portal will provide the primary interface to a comprehensive index and
repository of cross-referenced public molecular and physiological data for photosynthesis research
(project objective 2). Second, the portal will be the public face to a workbench of web-based tools for data
analysis and integration – the Knowledge Discovery Environment for Photosynthesis Systems Biology
(project objective 3) and provide access to a Simulation Environment for Photosynthesis Systems (project
objective 4). Third, portal will be the primary vehicle for dissemination of project educational outreach and
training resources to the community (project objective 5).
Data Repository for Photosynthesis Research
Primary data, derived information (i.e. structured, cross-referenced, analyzed data sets) and
scientific knowledge (literature) relevant to photosynthesis research are not currently consolidated and
accessible from any one single site globally. The lack of such a unified repository hampers efficient data
mining and achievement of an integrated understanding of photosynthesis. Computational experiments
such as model building, parameter estimation and simulation cannot be efficiently undertaken. Thus, a
key objective of this GC is to develop a comprehensive data repository for photosynthesis research.
6
A wide variety of biological data sets will be consolidated into the data repository and an ontology
framework for semantic integration specified. These will include key discrete –omics data and also,
continuous physiological response data. The repository will also index publicly available photosynthesis
systems models (see Appendix A1, Objective 2). An enumeration of public data sets potentially available
for the GC is provided in Appendix C. The key technical activities with respect to the photosynthesis data
repository would include:
1.
2.
3.
4.
Enabling continuous discovery of available data specific to our scientific queries
Promoting ease of access and use of this discovered data
Compilation of associated annotation and meta-data (especially, semantic linkages).
Enabling facilities for comparative scientific analysis and integration of data.
Tools to determine the quality of source data will be important. For example, availability of
accurate gene structures for rice/sorghum/maize genes will be critical to the task of clarifying gene
function, C4 versus C3 genetic variation, and for the identification of potential cis regulatory motifs in
promoters, enhancers, chromatin, etc. upstream of genes. The data management framework should also
allow distributed community curation of data to increase accuracy of gene structural predictions and
functional annotation.
Knowledge Discovery Environment for Photosynthesis Systems Biology
These data sets may be cross-referenced using CI approaches to facilitate a systems biology
understanding of photosynthesis. The above criteria suggest that the DE for this GC would require a
sophisticated software framework for automated ad hoc queries of internet distributed data sources, in
order to alleviate the tedium of manual “hunter-gatherer” compilation. A number of available approaches
to achieve this objective are under consideration by iPlant (e.g. ontology, web services, semantic web,
etc. see [23, 24]). Such a semi-automated framework must provide a solid “audit trail” of evidence
relating to the criteria of where, how and why the data or information was collected. This GC project
therefore needs the following bioinformatics tools:
a. A comprehensive semantic and software integration framework (“middleware”) to cross-link diverse
local and remote data sources, for efficient access by diverse CI analytical applications.
b. Text mining tools to mine public scientific literature for knowledge related to photosynthesis and
related plant metabolic processes. The results of such text mining can be used to populate a
comprehensive knowledge-base for photosynthesis.
c.
A general data-mining tool to discover, browse and synthesize available photosynthesis data sets.
d. General for sequence analysis of diverse coding and regulatory sequences related to photosynthesis.
e. Facilities to undertake genome (sequence level) and functional (expression level) alignments
between diverse C3 and C4 sequenced genomes such as Arabidopsis thaliana, Oryza sativa,
Sorghum bicolor, Zea mays, Panicum virgatum (switchgrass) to identify and transfer molecular
biology knowledge of model species (e.g. Arabidopsis and rice) to non-model species and also, to
facilitate recognition of genes related to both C3 and C4 photosynthesis. In particular, such a facility
will help identify similarities and differences in gene and associated regulatory sequences
(promoters).
f.
Comparative tools that access to available databases cataloging molecular sequence diversity of
genes within C3 and C4 crop germplasm, to identify molecular variants in the coding and regulatory
regions of photosynthetic genes for targeted conventional “marker assisted” breeding to improve crop
photosynthetic productivity.
g. Computational tools to identify metabolic pathways, genetic regulatory networks, protein-protein
interactions, and signal transduction pathways implied by high throughput data and comparative
genomic analyses.
h. Facilities to combine time-series data of metabolites with gene and protein expression data. One
possible approach would combine the ordinary differential equation modeling approach with a
statistical inference approach.
7
Various levels of data need to be cross-linked to one another using widely understood basic
biological relationships. For example, genome sequences should be connected to their associated
transcriptome. Transcripts should then be tied to associated RNA or peptide products (i.e. codons should
to specific amino acid residues, and possibly, to their inferred impact on protein structure and function.).
Known functional information about those gene products, including participation in pathways, protein
complexes, processes, cellular structures and regulatory networks, should be readily available.
Particular emphasis needs to be placed on DE tools for comparative biology, to elucidate
structure/function similarities and differences across species like C3 rice or wheat, against C4 organisms
like sorghum or maize. Given the continued public characterization of such diverse genomes,
comparative genome alignments of such species will become an increasingly more powerful and
essential tool to understand the photosynthetic differences between them.
The DE should also provide a simple and intuitive navigation interface to a complex and rich set
of possible workflows and operational modes. The common data analysis workflows will form a toolkit that
can be easily used by users to analyze genomic data. Perhaps each such workflow or mode may be
represented as a graphical “view” of one underlying common conceptual framework integrating the
available data. The design of such views should apply “best practices” for graphical user interface
design. They would provide the ability to hide/show or filter information selectively, to discriminate
different data points using differential symbolic, color and textual clues (including dynamic clues like
popup “tool tips”). At any point, the user should be able to explore related data, or review the audit trail of
available evidence underlying the specific data displayed on the screen.
The DE should allow researchers to collect harvest data into a session-persistent private local,
semantically well structured project databases, seamlessly linked to public data sources. Researchers
should be able to locally compile (“shopping cart” style) and visualize collections of data based on the
research-question driven queries. These data collections may be used in formalized bioinformatics
workflows (e.g. specified using tools like Taverna; http://taverna.sourceforge.net/), or as a source of
empirical parameters for simulation models.
The DE should provide statistical tools to integrate different kinds of genomic information for new
knowledge generation. The DE should provide facilities for the generation of annotated graphics derived
from the various visual representations of available data and models. For example, the generation of
annotated two and three-dimensional diagrams of leaves, tissues, cells, organelles, molecular pathways
and networks (regulatory, metabolic, etc.) and gene structures should be possible. The generation of
graphical plots of variables over a time course should be possible for various model simulations.
Formatted tables of qualitative and quantitative information from the system, exportable to worksheet
(e.g. Excel) tools should also be possible. Linkage of all such “views” to underlying data should be
dynamic so as to reflect the latest available data.
The initial data set that will be used for the model building will be published photosynthesis
literature and associated data sets, crop and leaf photosynthetic physiology datasets, data set for general
model plant species (Arabidopsis, rice, maize), leaf level expression, protein, metabolic dataset for these
model species (in particular Arabidopsis and rice), the "rice atlas" data at Yale University; whole genome
sequence maps and annotation for Arabidopsis, rice, maize. Some unpublished data that will be
available to be used in this project include photosynthesis data IRRI wild rice measurements (CO2
compensation points, vein spacing). In the near future, the following data set will be obtained : NSFfunded rice/maize/sorghum leaf transcript, protein, and metabolic data available from Yale and Cornell
University; the leaf anatomy, biochemistry, metabolite flux, and physiology data from Shanghai Institute of
Biological Sciences. Mutant data, expression and transgene molecular data for C4 and C3 plants from
IRRI projects funded by the Gates foundation. Appendix C provides a more extensive summary of
several available or pending data sets.
Simulation Environment for Systems Modeling Of Photosynthesis
The key, long-range output of this GC for photosynthesis research will be the development of a
sophisticated new simulation environment for photosynthesis research. The technical purpose of this
simulation environment will be two-fold:
8
1. To specify and simulate three-dimensional (geometric, anatomical) models of photosynthesis
metabolism and differentiation
2. To identify and predict genes that could be targets for crop improvement
Given the complexity of the task, the development of software supporting this output will be spread over a
longer period of time than the previously described project outputs. Likely, this task will be subdivided into
multiple stages:
•
Stage 1: Integration of currently available kinetic/metabolism models and auxiliary algorithms into a
common framework and their generalization to incorporate emerging molecular data sets
•
Stage 2: Development of reference models (3D models of single cell; 3D model of multi-cellular leaf
photosynthesis ) using existing packages
•
Stage 3: Develop a generic simulation environment for modeling 3 dimensional reaction diffusion
system using existing algorithms to enable user-defined photosynthesis model to be easily built
Broadly speaking, stage 1 is thought to be tractable within a 2-year project period; stage 2 may
extend to 5 years; stage 3 will likely be a significant research and development exercise over 10 years.
Development of Stage 3 will build on the experiences gained during stage 1, 2.
A natural conceptual framework for a DE for photosynthesis research would be a structured
spatial-temporal visualization and modeling of leaf morphology. That is, a DE could provide spatial
integration and visualization of data centered on the morphological models of (monocot) leaf anatomy and
physiology. This is needed at a variety of interrelated levels: from whole plant level, through whole leaf,
through leaf organ/tissue level (MC, BSC, epidermal cells, stomata, leaf veins, etc. inclusive of cell-cell
junctions), through whole cells, down to subcellular organelles (cell walls, chloroplasts, mitochondria,
cytosol, nucleus), down to the genomic level (chromatin, nucleoli, etc.).
The DE should be a “plug and play” toolkit permitting public and semi-public sharing of “Lego
Blocks” for building such morphological models. The design of such a toolkit might borrow ideas from
analogous “computer assisted” parametric geometry-driven 3-dimensional modeling environments, in
fields such as architecture (e.g. Autodesk Revit Architectural software, http://www.autodesk.com/revit)
and engineering design. On the molecular level, the framework should provide analysis and visualization
tools to represent models of gene and protein function, and should provide for visualization of available
genome annotation and experimental data, such as expression, proteomic, and metabolic data. It should
allow visualization of protein complexes important to photosynthesis. The geometric models should allow
representation of diverse phenotypic variants, for example, disrupted leaf tissue anatomy observed in
mutagenized plants (e.g. leaf structures from C3 reverting mutants).
In addition to spatial detail, the toolkit should also provide time-dependent kinetic modeling of
several kinds. First, the framework should support modeling the spatial-temporal trajectories of leaf
development, or the leaf morphogenesis events. That is, researchers should be able to simulate the
sequential series of developmental steps to the mature leaf stage. Such developmental modeling should
include the gross processes of leaf organization, such as the development of asymmetrical chloroplast
localization in C4 leaves. The framework should also provide for a specified proliferation and
differentiation in time and space for chloroplasts and mitochondria as well as cellular localization, and
changes in membrane and cell wall function and transport (Figure 3).
This kinetic modeling may have to scale from nanoseconds through hours to provide a
quantitative simulation of the ordered progression of physiological, cellular, metabolic and genetic
biological processes during development. This would include processes from the – sub-cellular or
organelle level, through multi-cellular (leaf) tissue level.
The modeling environment would allow users to define the reaction mechanisms and
corresponding physical and chemical constants, with automatic generation of the resulting systems of
differential equations. A series of metabolic reaction sequences focusing on the key processes related to
C4 photosynthesis will be provided as a pilot model for the platform. Results of such kinetic models
should be displayed in three dimensions super-positioned on a graphical representation of the anatomy of
9
the leaf. The framework will simulate not only the metabolic flux, but also the genetic regulation by both
the organelle and nuclear genomes.
The DE needs to simulate multiple phenomena in a continuous or discrete/stochastic fashion.
This framework will not only incorporate chemical reactions and gene expression events; it will
incorporate the physical processes of gas and metabolite diffusion, energy exchange at the leaf surface
etc. All these processes occur within a defined 3-dimensional leaf anatomy. Therefore, the framework
must consider spatial dimensions of these various processes. Quantitative physical simulations should
be coupled to biological development phenomena such as cellular proliferation, differentiation and cellcell interactions, including especially cell membrane and wall transport and function.
Figure 3.
Geometrical spatial integration and visualization of data within morphological models of
leaf anatomy and physiology, at a variety of interrelated levels: from whole plant level,
through whole leaf, through leaf organ/tissue level (i.e. MC, BSC, epidermal cells,
stomata, leaf veins, etc. inclusive of cell-cell junctions), through whole cells, down to
subcellular organelles (cell walls, chloroplasts, mitochondria, cytosol, nucleus), down to
the genomic level (e.g. gene structure and allelic variation)
The model outputs need to be linked to laboratory results or quantitative physiological indices
relevant to C4 photosynthesis, such as quantity and location of transporters across cell and organelle
membranes; CO2 compensation point; CO2 permeability across membranes; C4 enzyme kinetics; leaf gas
exchange parameters etc. These quantitative indices can be obtained from experiments. They can be
used to validate, invalidate and improve the predictive models. Once this model framework is developed,
it will allow testing “what if” hypotheses. For example, it can be used in sensitivity analyses to study the
effects of changing any underlying component in the system, hence, providing an in silico tool for
predictive modeling of the genetic basis for C4 characteristics. To be more specific, there is a genetic
basis for each of the determinants of either kinetic properties (enzyme kinetics, protein synthesis and
degradation rates, mRNA synthesis and degradation rate etc) or structural properties (anatomical,
metabolic network structure, genetic regulatory network etc). The effect of altering the genes for any
determinant could be modeled in terms of its impact on C4 photosynthesis.
Second, the DE needs to combine mechanistic models with statistical models. Statistical models
include network connectivity models that describe the relationship between different reactions in the
metabolic network, or the characteristics of cellular morphology at different locations in leaf tissue. The
relationships are usually statistically inferred from high throughput genomic level data. On the other hand,
mechanistic models are based on specific hypotheses concerning the biochemistry of metabolic steps.
These two types of models have been developed independent of each other and correspondingly are
10
separated from each other, although research into connecting mechanistic models at one scale to
statistical models at other scales may ultimately be required.
Third, the DE should enable comparison of structure and function relationships between diverse
C3 and C4 species, between C4 species, and for different tissues of a given plant, or same tissue under
different conditions. The facility for comparisons should be available both on the level of genomic
sequences – gene structures and regulatory regions like promoters – and at higher levels of biology –
transcript, protein and metabolic and phenotypic (e.g. mutant plant) profiles. These analyses will help
identify potential genes responsible for the generation of C4 characteristics. Large biological data sets are
available within the C4 research community for such analyses (Appendix C).
Fourth, a sensible indexing of candidate gene networks should integrate all available gene
expression data; transcriptome, proteome, metabolome and phenome. Such genetic networks may
themselves be modeled visually as connected graphs (nodes plus edges) annotated with proper
evidence, and with qualitative and quantitative indications of the nature of the identified genetic
interactions. The metabolic reaction networks within each cell type should be provided in a similar
framework. This network will be updated dynamically using the most recent data available. The
confidence level of each edge in this network will be explicitly given. A number of auxiliary tools need to
be developed to enable effective use of the resulting CI.
1. Computer programming standards to enable plug-and-play style quantitative model building.
2. Optimization routines to identify the best model parameters to fit to given data sets including the
goodness of fit to multiple sources of photosynthesis measurements.
3. Facility to use time series metabolic, transcript profiling, and proteomics data for parameter estimation
in mechanistic models. This capacity is critical because we are still quite limited in estimation of the
kinetic parameters used in mechanistic models.
4. Graphical interface to support construction of comprehensive 3-dimensional models of multi-cellular
C3 or C4 photosynthetic systems, with linkage to experimental data sets.
5. Simulation (“evolutionary algorithm”) and sensitivity analysis tools to identify and quantify key
parameters and components. Identify parameter spaces that are stable, and regions of parameter
spaces that are highly sensitive to perturbations.
This platform will provide synthetic biology tools to identify the optimal sequential engineering
changes needed to gain desired traits, such as engineering C4 photosynthetic properties from C3
photosynthesis. Considering that most of the complex traits in plants are evolved via multiple steps, the
platform will enable evaluation of the fitness and difficulty of each step in genetic engineering,
correspondingly suggesting the most feasible option.
Several algorithms applicable to a plug-and-play model need to be developed:
1. Integration of processes occurring at drastically different time and spatial scales. In general the
smaller the spatial scale the faster the time scale. The DE framework needs to simulate processes
ranging from chloroplast level all the way up to leaf level. This may require approximation or response
analysis of lower level processes when used as inputs for higher levels operating on time scales of a
different order of magnitude.
2. Simulation of diverse physical phenomena within the 3-dimensional leaf anatomy. Various physical
processes occur in a leaf. Gasses such as CO2 and water diffuse to and from ambient air through
stomata into intercellular spaces, and ultimately to Rubisco. Active transport processes create
concentration gradients from one cell type to another. Diffusion of metabolites dissipates these
gradients. Events at leaf surface, such as stomatal opening, change the turgor pressure and
mechanical forces within the leaf. The enlargement of bundle sheath cells requires changes in turgor
pressure and cell wall deformation. The modeling framework must deal with all these processes
simultaneously.
3. Network-based genetic interactions must be combined with a kinetic and mechanistic description of
metabolism. Although metabolic reactions have a continuous response to inputs, gene expression
can be discrete and stochastic. There is a strong interaction between these two processes. Genetic
regulation constrains the potential capacity of metabolism. Metabolites affect gene expression and
correspondingly the function of the gene interaction network. Although we have no automatic
11
methods to combine these processes now, we must incorporate genetic regulation into a model of
metabolism.
We anticipate that iPlant can provide the software engineering support for these developments,
as well as hardware support for high performance computing facilities.
Education, Outreach and Training
Outreach and Training for the Plant Science Community
This GC project can strive to develop computational and systems modeling skills plus increase
the depth of quantitative understanding of photosynthesis within plant science community with a number
of activities:
•
•
•
•
Develop training materials and host training workshops (in the USA) for photosynthesis modeling.
Provide travel funds for US scientists to international project-affiliated meetings (e.g. PICB, C4 rice
consortium meetings, international photosynthesis conferences).
Fund shuttle science exchanges into GC project development activities for international
photosynthesis research team members such as photosynthesis modeling experts and graduate
students/postdocs from outside the US.
Develop (US) national curriculum resources for training in plant CI relating to computational systems
biology modeling and photosynthesis research
International collaboration between iPlant and photosynthesis research consortia like the C4 Rice
Consortium (described previously, above) could include the sharing of expertise funded within the
Consortium, at several levels of scientific exchange, including perhaps shuttle visits to iPlant development
sites, subsidized by iPlant.
Public Outreach and Training
Green plants are pervasive on the surface of the planet, in all but the most arid or forbidding of
locations. Despite the importance of plants to the basic human necessities of food, shelter and clothing
few students currently give plants much thought. Aside from a cursory mention of photosynthesis in most
high school science classes, few students enter university with the intent of studying plant science. At
Cornell and many of our countries top universities, plant science and plant breeding departments are
suffering a drop in student enrollments and decreases in federally-funded grant support for plant science
research. This trend is particularly distressing at this time as the challenges of global warming, energy
security and population growth demand that the best and brightest students choose plant science as a
career path. It is also critical that the general public becomes more informed about plants when
considering government policies on bioenergy, the costs (both economic and environmental) on chemical
fertilizers and pesticides and the benefits and risks associated with genetically modified foods.
The diversity of biology underlying photosynthesis is unknown to the great majority of people.
Few are aware of the incremental increases in agricultural crop productivity. That there may be intrinsic
limits to such productivity is a rarer idea still. The recent volatility in cereal crop prices, in particular for
rice, renewed anxieties of the world of global food shortfalls. Parts of the world with large and growing
populations still harbor significant pockets of poverty, and they demonstrate continuing, and in some
cases growing, demands for cereals like rice and wheat. The prospect of C4-type photosynthesis
increasing the productivity of key crops like rice and wheat is likely to fire the imagination and increase
expectations. Concurrently, as the world continues to embrace “green” environmental ethics, one might
foresee an increased curiosity of the biology of plants and photosynthesis. On a negative note, general
backlash to the perceived shortfalls of the first so-called “Green Revolution” of the 1960’s, coupled with
general wariness of “Genetically Modified Organisms” could be potential challenges to the success of a
C4-type photosynthesis project. It will be recognized that the only available means of introducing C4-type
photosynthesis into C3-type photosynthetic species is through transgenic technology.
This proposed GC theme should embrace such interest, expectations, and perceived threats, by
carefully crafting “CI-enabled” education and information specifically targeting photosynthesis. This would
include a comparison C3–type versus C4–type photosynthesis; and suitable background to explain the
12
experimental and biotechnical processes being undertaken to achieve the project’s goal. These materials
would be developed and delivered for many cognitive levels, such as primary, secondary, and postsecondary schools, for casual consumption by an educated lay public; or for serious consideration by
potential critics. Part of DE development of this project could focus on creating a simplified simulator of
leaf and/or whole plant photosynthesis, as a means for education. Summer workshops would be
organized at US institutions to provide high school teachers with training in the tools developed through
this DE (e.g. http://outreach-pgrp.cornell.edu/educationTeacherPrograms.php). With versions of
increasing sophistication, such a simulator could be used as to train students in model building. “Summer
schools” in plant photosynthesis modeling could be offered for high school or undergraduate students to
participate in some of the laboratories developing or applying the iPlant CI for this GC project.
Computer Science Needs for the Photosynthesis GC
We have identified some general project needs that require targeted computer science expertise.
First, many existing models of photosynthesis are specified in commercial packages, such as Matlab.
Though these packages proved extremely useful in developing the reference model, they are usually
expensive and not generally available to many bench scientists. Conversion of reference models into
open source versions would increase access of the models to the wider community and students. In this
project, we will use a two step process: i.e. first using existing packages such as MATLAB to develop
reference models, and then develop generic packages to enable easy and efficient model developments.
Second, parameter estimation algorithms (and their implementations) must be improved and modified for
high-throughput data, such as the vast amount of transcript, proteomic and metabolomic data now
available. Third, the modeling effort would benefit greatly from a 3-dimensional graphic representation of
photosynthesis model inputs and outputs. Fourth, error estimation/summation and statistical analysis of
the simulation results should be improved. Fifth, scaling model simulations from genome or cell to higher
levels such as leaf segment to whole leaf may require execution on remotely hosted high performance
cluster and/or grid computing facilities. Porting of the models to these facilities will require significant
“hard core” computing expertise. Possible computational expertise (colleagues of GC proponents)
available to assist with the development of the proposed photosynthesis DE includes: Mike Heath (NCSA,
UIUC), Eric de Sturler (Virginia Tech), James Myers (NCSA, University of Illinois).
Projected Bottlenecks and Difficult Steps
In a second phase of this project, 3D multi-phenomenal and multi-scale simulation environments
could be developed. This is an extremely ambitious CI challenge, thus we would expect the research and
development effort required a substantial bottleneck to this GC. Experienced applied mathematicians
and computer scientists will be invited to join the team to ensure the successful overcome of this difficulty.
Project Implementation and Management Plan
Appendices A1 and A2 provides a tabular log frame of the anticipated major project objectives,
outputs, activities, stages, milestones and timelines for the project, with indications of core GC team
member responsibilities.
Project proponents will generally be available to various degrees to work with the iPlant team for
all aspects of the CI software development cycle. Assuming that iPlant funding is provided for travel and
local hosting costs, selected members of the GC team could undertake shuttle science visits to iPlant
implementation sites (i.e. University of Arizona and/or Cold Spring Harbor Laboratory) which could
include extended sabbatical visits for “hands-on” development of the GC project CI in partnership with
core iPlant staff.
For overall coordination of the project, we will hold monthly teleconferences to coordinate the
work and convene annual meetings of project participants to review project progress, future work plans,
including monitoring committee needs and plans. A scientific advisory committee (tentatively proposed
to be composed of Mike Heath, Steve Long and Jim Myers) will monitor the progress of the project and
advise the research of this overall project.
13
Intellectual Property Constraints
We will develop open source software that will be freely available and used by research
community. Data sets accessed and integrated in the project will be public and unencumbered by IP.
Project Completion
The timeline for this project is presented in Appendix A2. We envision a three different target time
scales to reach different level of functionalities for the DE. Early work within the first 2 years of the
project will focus on CI development using currently available “off-the-shelf” (bio)informatics and modeling
tools and technologies. Within this scope, the outputs under objective 1 – web portal & data repository–
should be significantly commissioned, and a substantial degree of analytical functionality should be
available from outputs of objective 2 – the knowledge driven workbench. By that time, we should also
have developed basic resources for education, outreach and training (objective 4).
Objective 3 of building a simulation environment that can effectively simulate 3 dimensional
reaction-diffusion systems, coupled with various biochemical and biophysical processes in one single cell,
will require substantial cutting-edge research and development effort and is only targeted for substantial
progress toward the end of a proposed five-year period. Beyond the end of the five-year funding period,
we will start to move technology prototyped in the initial phases of the project, toward our vision of a
simulation environment for multi-cellular system to serve the engineering of C4 photosynthesis.
Summary: Projected Impact of the Photosynthesis GC
The CI will greatly advance our capacity to solve two challenging problems in plant biology: a)
enhancing photosynthetic efficiency in crop plants of agronomic importance; and b) converting
agronomically important C3-type photosynthetic plants, such as rice and wheat, to use C4
photosynthesis. The CI we propose could be adapted to simulate other plant physiological processes,
such as nitrogen fixation, respiration and other metabolic processes, dramatically improving the capacity
of plant biology research in general. The C3 and C4 photosynthesis models developed here could form
the basis for a generic plant primary metabolism model, which can be coupled to whole plant models to
trace photosynthate utilization and simulate the metabolic processes of the whole plant. These models of
photosynthesis could be used to evaluate how plant yield is limited by the response to external variables
such as water and temperature stresses, changes in global CO2 and different local environments and
crop management practices. In general, these models will contribute to our understanding of the
responses of plants to abiotic stresses (cold, heat, drought etc) since photosynthesis is sensitive to these
different environmental factors.
Appendix A – Project Objectives, Activities, Outputs, Milestones and Timeline
Appendix B – Additional Expert Resources for this Project
Appendix C – Biological Data Sets Available to this Grand Challenge Project
Appendix D – Curriculum Vitae of Project Proponent
14
References Cited:
1.
FAOReport: The state of food and agriculture. (Weibe K ed. Rome, Italy: Food and Agriculture
Organization of the United Nations; 2008.
2.
von Braun J: The food crisis isn't over. Nature 2008, 456:701-701.
3.
IFPRI Report [http://www.ifpri.org/pubs/fpr/pr20.pdf]
4.
Dawe D: Agricultural research, poverty alleviation and key trends in Asia's rice economy.
In Charting New Pathways to C4 Rice. Volume International Rice Research Institute. Edited by
Sheehy JE, Mitchell PL, Hardy B. Los Banos, Philippines; 2007: 37-55
5.
Sheehy JE, Ferrer AB, Mitchell PL, Elmido-Mabilangan A, Pablico P, Dionara MJA: How the rice
crop works and why it needs a new engine. In CHARTING NEW PATHWAYS TO C4 RICE.
Edited by Sheehy JE, Mitchell PL, Hardy B. Los Banos, Philippines: International Rice Research
Institute; 2007: 3-26
6.
Peng S, Laza RC, Visperas RM, Sanico AL, Cassman KG, Khush GS: Grain yield of rice
cultivars and lines developed in the Philippines since 1966. Crop Science 2000, 40:307-314.
7.
Mitchell PL, Sheehy JE: Supercharging rice photosynthesis to increase yield. New Phytol
2006, 171:688-693.
8.
Mitchell PL, Sheehy: The case for C4 rice. In CHARTING NEW PATHWAYS TO C4 RICE.
Edited by Sheehy JE, Mitchell PL, Hardy B. Los Banos, Philippines: International Rice Research
Institute; 2007: 27-36
9.
Zhu XG, Long SP, Ort DR: What is the maximum efficiency with which photosynthesis can
convert solar energy into biomass? Curr Opin Biotechnol 2008, 19:153-159.
10.
Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, Haberer G,
Hellsten U, Mitros T, Poliakov A, et al: The Sorghum bicolor genome and the diversification
of grasses. Nature 2009, 457:551-556.
11.
Long SP, Zhu XG, Naidu SL, Ort DR: Can improvement in photosynthesis increase crop
yields? Plant Cell Environ 2006, 29:315-330.
12.
Zhu X, Portis AR, Jr., Long SP: Would transformation of C3 crop plants with foreign Rubisco
increase productivity? A computational analysis extrapolating from kinetic properties to
canopy photosynthesis. Plant Cell Environ 2004, 27:155-165.
13.
Zhu X-G, Whitmarsh J, Ort DR, Long SP: Study of the effects of photoinhibition on dynamics
of carbon uptake using reverse ray-tracing algorithm. J Ex Bot 2004, 55:1167-1175.
14.
Zhu XG, de Sturler E, Long SP: Optimizing the distribution of resources between enzymes
of carbon metabolism can dramatically increase photosynthetic rate: a numerical
simulation using an evolutionary algorithm. Plant Physiol 2007, 145:513-526.
15.
Raines CA: The Calvin cycle revisited. Photosynth Res 2003, 75:1-10.
16.
Sage RF, Pearcy RW: The physiological ecology of C4 photosynthesis. In Photosynthesis:
physiology and metabolism. Edited by Sharkey TD, von Caemmerer S. Dordrecht: Kluwer
Academic Publishers; 2000: 497-532
15
17.
Ogren WL: Photorespiration: Pathways, Regulation, and Modification. Annual Review of
Plant Physiology 1984, 35:415.
18.
Sage RF: The evolution of C4 photosynthesis. New Phytol 2004, 161:341-370.
19.
Sage RF: Why C4 photosynthesis? In Sage RF and Monson RK (eds) C4 Plant Biology, pp
3--14. Academic Press, San Diego. 1999.
20.
Vicentini A, Barber JC, Aliscioni SA, Giussani LM, Kellogg EA: The age of the grasses and
clusters of origins of C4 photosynthesis. Global Change Biology 2008, 14:1-15.
21.
Hibberd JM, Quick WP: Characteristics of C4 photosynthesis in stems and petioles of C3
flowering plants. Nature 2002, 415:451-454.
22.
Nelson T, Langdale J: Developmental genetics of C4 photosynthesis. Ann Rev Plant Physiol
Plant Mol Biol 1992, 43:25-47.
23.
Stein LD: Towards a cyberinfrastructure for the biological sciences: progress, visions and
challenges. Nat Rev Genet 2008, 9:678-688.
24.
Bruskiewich R, Senger M, Davenport G: The generation challenge programme platform:
semantic standards and workbench for crop science. Inter J of Plant Genomics 2008,
2008:6.
16
Appendix A – Project Management Plan
Appendix A1 – Project Objectives, Activities, Outputs, Milestones and Resources
Project
iPlant Grand Challenge Project for Photosynthesis Research
Vision of Success
Within 10 years, crop improvement is significantly accelerated from advances in photosynthesis research made
feasible by enhanced CI for photosynthesis research
Objective 1
To establish a community portal for photosynthesis research
Objective Leader: T. Brutnell
Activity 1.1
Establish project Web 2.0 web portal.
Activity Leader: T. Brutnell
Outputs
Milestones
Outcome
a. Web server commissioned.
1.1.1 Project web portal.
b. Web portal software for prototype
portal installed and configured.
c. Photosynthesis web portal structure
and content publicly reviewed by project
photosynthesis experts.
d. Web portal structure and content
edited and maintained throughout
project.
Required Resources
•
“One stop shop” for
photosynthesis information
integrates and accelerates
photosynthesis research
globally.
•
•
•
Host location and hardware
infrastructure for web server.
Web 2.0 portal software.
Programmer for web portal
software deployment (6
programmer x months)
Web portal manager (60 IT
specialist x months)
Objective 2
To establish a public data repository for photosynthesis research
Objective Leader: P. Jaiswal
Activity 2.1
Establish repository for photosynthesis molecular (“discrete”) data
Activity Leader: P. Jaiswal
Outputs
Milestones
Required Resources
2.1.1 Discrete
photosynthesis data
repository infrastructure.
a. Database server hardware
commissioned.
b. Database platform software specify
and installed.
c. Overall photosynthesis database
architecture, specified and
Outcome
•
Discrete photosynthesis data
repository established online for
24 x 7 research access by
project stakeholders.
•
•
Host location and hardware for
database server.
Database platform software.
Information systems consultant
(60 systems administrator x
months)
implemented.
2.1.2 Semantic standards for
integration of photosynthesis
research data.
2.1.3 Genomic map,
sequence and candidate
gene data module.
2.1.4 Transcriptomics data
module.
2.1.5 Proteomics data
module.
2.1.6 Metabolomics data
module.
2.1.7 Project-related
germplasm, phenotype and
genotype (molecular
variation) data module.
2.1.8 Research literature
module.
a. Public domain standards – domain
models and ontology - for knowledge
integration of photosynthesis research
data, reviewed with community
feedback.
b. Baseline public ontology for
photosynthesis, specified and
published.
c. Process established for continued
community development and review of
photosynthesis ontology.
•
Community semantic standards
enables productive data mining
of photosynthesis research data
and information.
a. Database schema and archives
specified for raw and analyzed data, for
discrete data input module.
b. Standard operating procedures
developed for data curation and quality
assurance, annotation and exchange
formats with Activity 3.1.1.
c. Initial test data sets entered into data
module.
d. Initial normalized data sets created
by reducing redundancy in principal
objects being annotated/used in the
analysis and cross-linked by creating
links between different objects and
entities by way of cross-references,
orthology, parent-child relationships
(lineage especially for germplasms) with
Identifiers mappings back to entries
inmajor public global repositories.
c. Design and implement Web query
and display interfaces.
Enhanced research access to
cross-linked data sets provides
raw inputs to accelerate
photosynthesis research.
a. Schema and data loading tools
specified and implemented for research
literature module of the photosynthesis
Photosynthesis research
literature integrated with
repository facilitates
•
•
•
•
•
•
Web site for publication and
review of semantic standards
(collaboration with Output
1.1.1)
Knowledge management
consultant (6 bioinformatician x
months)
Workshop funding to convene
discussions with
photosynthesis researchers to
validate project semantic
standards.
Information architect (24
bioinformatician x months)
Programmer for development of
data loading scripts (12
programmer x months)
Data entry personnel (60
biologist x months)
Systems analyst/programmer to
develop research literature
module (6 computer scientist x
data repository.
b. Research literature module of the
photosynthesis data repository
populated with an initial set of relevant
scientific literature.
c. Procedures established for
community curation of additional
publications into the research literature
module of the photosynthesis data
repository.
photosynthesis research and
knowledge management.
•
months)
Librarian/manager for curation
(24 person x months).
Activity 2.2
Establish repository for photosynthesis physiological response
(“continuous”) data.
Activity Leader: M. Gent
Outputs
Milestones
Outcome
Required Resources
2.2.1 Physiological response
photosynthesis (leaf) data
repository structure.
a. Data schema for continuous data
input module specified.
b. Repository populated with initial data
sets.
c. Web query and display interfaces
designed and implemented.
Photosynthesis (leaf)
physiological response data
research data integrated with
repository facilitates
photosynthesis research and
knowledge management.
•
•
•
Information architect (12
bioinformatician x months).
Programmer for development of
data loading scripts (12
programmer x months).
Data entry personnel (24
biologist x months)
Activity 2.3
Establish repository for photosynthesis system models.
Activity Leader: X. Zhu
Outputs
Milestones
Required Resources
Outcome
•
a. Data schema specified for process
model repository.
Photosynthesis process models
indexed in the photosynthesis
repository to facilitate
collaboration using such
models in photosynthesis
research.
2.3.1 Photosynthesis system
model repository.
b. Web query and display to browse
models and parameter sets, designed
and implemented.
Objective 3
To build a knowledge discovery environment for photosynthesis systems
biology.
•
•
Information architect (6
bioinformatician x months).
Programmer for development of
data loading scripts (6
programmer x months).
Programmer for development of
web query and display
interfaces (6 programmer x
months).
Objective Leader: R. Bruskiewich
Activity 3.1
Develop a semantic and software framework for development of a
knowledge discovery environment for photosynthesis systems biology.
Activity Leader: R. Bruskiewich
Outputs
Milestones
Required Resources
Outcome
•
3.1.1 Semantic standards
integrated into knowledge
discovery environment.
Methodology and strategy specified for
embedding and using semantic
standards (developed in Objective 2) in
the knowledge discovery environment.
Photosynthesis community
semantic standards embedded
in knowledge discovery
environment enable productive
data mining of photosynthesis
research data and information.
•
•
•
3.1.2 Data source access
standards for photosynthesis
data repository modules.
a. Publicly available application
programming interface (API) standards
for data source access, reviewed for
possible application to project.
b. Project-specific data access
standards and software libraries
developed for photosynthesis data
repository (developed in Objective 2).
•
Adoption of common project
data source access standards
enables efficient and
interoperable access to
photosynthesis repository data.
•
•
3.1.3 Access protocols to
key (remote) online public
biological (plant) data
sources.
a. Publicly available protocols for
(remote) access to key public online
biological (plant) data sources,
reviewed for possible application to
project.
b. Project-specific access protocols and
Adoption of project data source
access standards enables
efficient and interoperable
access to relevant online
(remote) public biological
(plant) databases.
•
•
Web site for publication of
semantic integration into
knowledge discovery
environment (collaboration with
Output 1.1.1)
Systems analyst to design API
(2 computer scientist x months)
Programmer/designer to
implement semantic aware
software (6 programmer x
months)
Web site for publication and
review of data source API
(collaboration with Output
1.1.1)
Systems analyst to design API
(2 computer scientist x months)
Knowledge management
consultant (bioinformatician) to
apply API for target query use
cases against photosynthesis
data modules (6
bioinformatician x months)
Programmer/designer to
implement data source access
on data repository (24
programmer x months)
Web site for publication and
review of (remote) biological
data sources access protocols
(collaboration with Output
1.1.1)
Systems analyst to design and
software libraries specified and applied
to key (remote) online public biological
(plant) data sources.
•
3.1.4 Data mining engine for
project data.
a. Publicly available data mining
technology reviewed for application to
photosynthesis repository and related
public data sources.
b. Project-specific data mining engine
designed and implemented on top of
the photosynthesis repository and
related public data sources.
3.1.5 Application
framework(s) specified for
the development of a
photosynthesis systems
biology knowledge discovery
environment.
a. Open source software application
development framework(s) reviewed for
use in the development of
photosynthesis knowledge discovery
environment.
b. Application framework(s) specified
and implemented for the development
of the photosynthesis knowledge
discovery environment.
3.1.6 Web-based user
interfaces to photosynthesis
repository data.
a. Publicly-available web technologies
for data query and visualization,
reviewed for possible use in web user
interface to photosynthesis repository
data.
b. Design and implement baseline web
interfaces for query and visualization of
photosynthesis repository data.
Web access to query and
visualization tools for crosslinked photosynthesis
repository data sets, and other
available online public data,
accelerates photosynthesis
research.
3.1.7 Web-service
publication of photosynthesis
repository data.
a. Publicly-available web-service
publication protocols and technology
reviewed for use in providing
Programmatic web-service
access to photosynthesis
repository data allows linkage
Project-specific data mining
technology enables discovery,
structuring and interpretation of
available photosynthesis
research data.
Adoption of a standard software
application framework will
accelerate development of the
photosynthesis systems biology
knowledge discovery
environment.
implement protocols (12
computer scientist x months)
Knowledge management
consultant to apply protocols for
target query use cases against
target public databases (6
bioinformatician x months)
•
Information architect to design
data mining software (6
computer scientist x months x
months)
•
Web site for publication and
review of project-specific
application development
framework(s) (collaboration
with Output 1.1.1)
Systems analyst/programmer to
review technology options and
implement selected framework
(6 computer scientist x months)
•
•
•
•
Web site for publication and
review of web technologies for
data query and visualization
(collaboration with Output
1.1.1)
Systems analyst/programmer to
review technology options and
implement selected framework
(12 computer scientist x
months)
Web site for publication and
review of web service
publication protocols and
programmatic web-service access to
photosynthesis repository data.
b. Web-service access to the
photosynthesis data repository,
designed and implemented.
of analytical tools to
photosynthesis research data
and information.
•
technology (collaboration with
Output 1.1.1)
Systems analyst/programmer to
review technology options and
implement selected
framework(s) (12 computer
scientist x months)
Activity 3.2
Develop text mining tools for photosynthesis knowledge discovery.
Activity Leader: R. Bruskiewich
Outputs
Milestones
Outcome
Required Resources
Text mining of photosynthesis
research literature enables
discovery of novel biological
facts and relationships that
accelerate photosynthesis
research.
•
3.2.1 Text mining module for
photosynthesis knowledge
capture from scientific
literature.
a. Text mining query and display engine
operating on photosynthesis literature
module (Output 2.1.7) and other related
(online, remote) databases, using
photosynthesis-specific ontology
(Output 3.1.1), designed and
implemented.
b. Quality assurance methodology
defined, for manual validation of results
from text mining
•
Systems analyst/programmer to
develop publications module (6
computer scientist x months)
Biologists to read and validate
the key data obtained from
literature text mining.
c. Pilot manual validation undertaken, of
initial data sets obtained from the
literature mining.
Activity 3.3
Develop a data mining tool for photosynthesis knowledge discovery.
Activity Leader: R. Bruskiewich
Outputs
Milestones
Required Resources
3.3.1 General data mining
query engine for
photosynthesis data
repository
a. General data mining engine to
operate on photosynthesis data
repository (Activity 2.1), design and
implemented.
b. Statistical tools for comparative
analysis of published data sets,
designed and tested.
c. Web interface for manual curation of
new biological facts and relationships,
Outcome
•
Data mining of photosynthesis
data repository enables
discovery of novel biological
facts and relationships that
accelerate photosynthesis
research.
•
Information architect
(bioinformatician) to specify
baseline design (6
bioinformatician x months)
Programmer/designer to
implement data mining software
(12 programmer x months)
designed and implemented.
Activity 3.4
Develop analytical tools for photosynthesis knowledge discovery.
Activity Leader: R. Bruskiewich
Outputs
Milestones
Required Resources
3.4.1 Sequence analysis
tools.
3.4.2 Comparative genome
(sequence level) and
functional (expression level)
alignment tools.
3.4.3 Inference tools for
metabolic pathways, genetic
(regulatory) network, proteinprotein interactions, and
signal transduction
Outcome
a. Available open source sequence
analysis and visualization tools and
libraries reviewed for application to the
knowledge discovery workbench.
b. Sequence analysis and visualization
tools specified and implemented
accessing the photosynthesis data
repository.
a. Data schema for comparative
genomic alignments, sequence diversity
analysis and functional genomics,
specified and implemented.
b. Comparative query and visualization
tools, specified and implemented
c. Comparative query and visualization
tools deployed on data sets developed
from key completed genomes:
Arabidopsis, Oryza sativa, Sorghum
bicolor, Zea mays, Panicum virgatum
(switchgrass).
d. Comparative query and visualization
tools deployed on molecular diversity
(e.g. crop SNP) data sets.
e. Comparative tools deployed on other
available comparative data sets (e.g.
plant transcriptome datasets)
a. Data schema for pathway and
network data module, designed and
implemented.
b. Analytical, inference and data
visualization tool for metabolic
•
•
Bioinformatics discovery of
photosynthesis coding,
regulatory motifs and biological
functions in available sequence
data.
Enable inference of metabolic
pathways, genetic (regulatory)
network, protein-protein
interactions, and signal
transduction pathways
•
•
•
•
Information architect
(bioinformatician) to specify (2
bioinformatician x months)
Programmer/designer to
implement (6 programmer x
months)
Information architect
(bioinformatician) to specify (6
bioinformatician x months)
Programmer/designer to
implement (24 programmer x
months)
Information architect
(bioinformatician) to specify
representation (12
bioinformatician x months)
Programmer/designer to
pathways.
pathways, genetic (regulatory) network,
protein-protein interactions, and signal
transduction pathways, designed and
implemented.
3.4.4 Facilities to combine
time-series data of
metabolites with gene and
protein expression data.
Ordinary differential equation modeling
approaches, integrated with a statistical
inference approaches, specified and
implemented.
Objective 4
Develop a simulation environment for systems modeling of photosynthesis
metabolism and differentiation.
Objective Leader: X. Zhu
Activity 4.1
Develop the simulation environment for kinetic modeling of photosynthesis
metabolism and differentiation (will not consider the spatial differentiation)
Activity Leader: X. Zhu
Outputs
Milestones
Required Resources
4.1.1 Simulation application
for kinetic modeling of
photosynthesis metabolism.
a. Programming technology selected for
dynamic modeling of metabolism.
b. Tools developed to enable graphic
representation of metabolic processes
and define the kinetic information.
c. Simulation environment implemented
for automatic generation and solving
differential equations.
d. Visualization tools designed and
implemented to show the dynamic
changes of metabolic activities on a
pathway.
e. Evolutionary algorithms developed to
identify optimize photosynthesis
4.1.2 Simulation application
for kinetic modeling of
differentiation of
photosynthetic machinery
a. Simplified model of photosynthetic
machinery differentiation, designed and
implemented.
b. Plug-and-play capacity develop to
explore variations of photosynthetic
properties under different environments
implement analytical tool (24
programmer x months)
underlying photosynthesis.
•
•
Outcome
•
•
Development of a common
simulation platform to simulate
photosynthesis will integrate
and accelerate photosynthesis
research and help identify gene
targets for crop improvement.
•
•
Mathematician/bioinformatician
to help design algorithms (6
bioinformatician x months)
Programmer/designer to
implement data mining software
(12 programmer x months)
Postdoctoral photosynthesis
scientist
Programmer/designer (s) to
develop model environment,
simulation environment and
plug and play functionality (15
programmer x months)
Postdoctoral photosynthesis
scientist
Programmer/designer (s) to
develop model environment,
simulation environment and
plug and play functionality (6
programmer x months)
4.1.3 Simulation integrated
model of photosynthesis
metabolism and
differentiation events
•
a. A photosynthesis model developed
combining model from Output 4.1.2 with
metabolism and genetic regulatory
networks for photosynthesis
b. Photosynthesis model developed
with signal transduction pathway
integrated with metabolism and genetic
networks
•
•
Mathematicians to help design
algorithms for control circuit
identification
Biologists to help extract the
initial genetic network and
regulatory pathways
Programmer/designer (s) to
develop model environment,
simulation environment (12
programmer x months)
Activity 4.2
Develop the auxiliary algorithms to facilitate application of the systems
models and link models to databases
Activity Leader: C. Myers
Outputs
Milestones
Required Resources
4.2.1 Analytical support
functions applied to the
photosynthesis simulation
environment.
a. Parameter estimation algorithm for
simulation environment, specified and
implemented.
b. Optimization algorithms applied for
the simulation environment.
c. Software developed to visualize the
ordinary differential equation solution
space and identify regions with high
biological relevance
d. Methods specified and applied to
conduct bifurcation analysis of kinetic
models.
Systems biology application
tools available to enable plant
scientists connect models with
experiments for hypothesis
testing.
4.2.2 Develop kinetic
modeling framework.
Kinetic models linked with physiological
response data and time series
metabolomics data to estimate model
parameters.
Model predicting evolution over
time or development of
metabolites involved in
photosynthesis.
4.2.3 Link photosynthesis
discrete database with
kinetic modeling framework.
a. Genome transcriptome and
metabolome level data link to
parameters in integrated model of
Direct linkage of the modeling
environment with data
assembled in the
Outcome
•
•
•
•
•
Applied mathematician skilled
in non-linear parameter
estimation and least squares
minimization algorithms.
Programmer/designer to
integrate existing tools for
parameter estimation,
optimization, and visualization,
and bifurcation analysis (12
programmer x months)
Principal leaders and applied
mathematician debate/review
methods for this linkage.
Programmer/designer to
implement linkage tool (6
programmer x months)
Principal leaders and applied
mathematician debate/review
methods for this linkage.
photosynthesis metabolism and
regulation.
b. Kinetic modeling combined with
genomic data to evaluate significance of
genomic variations to photosynthetic
efficiency and differentiation
photosynthesis data repository,
enables identification of genetic
basis for photosynthetic
metabolism and regulation.
•
Programmer/designer to
implement linkage tool (24
programmer x months)
•
Principal leaders and applied
mathematician debate/review
methods for this section.
Programmer/designer to
implement selection tool (12
programmer x months)
4.2.4 Manage dynamic
model development to
maintain tractability and ease
of use.
a. Model reduction methods developed
to select and eliminate parameters
and/or network interactions to maintain
a relatively robust dynamic model
b. Algorithms developed to define
response surface of interacting
networks to use model upscaling.
4.2.5 Cross-link
photosynthesis models to
data repository for model
validation.
a. Interface designed to cross-link
models in model repository (Output
2.3.1) with discrete, continuous and
anatomical data sets, to enable direct
comparison of model results with data
sets elsewhere in the repository.
Activity 4.3
Develop the basic 3 dimensional reference models for photosynthetic
metabolism and differentiation
Activity Leader: X. Zhu
Outputs
Milestones
Required Resources
4.3.1 Implement a reference
3 D photosynthesis model
a. A 2 dimensional C3 photosynthesis
model implemented for single cell
b. A 3D C3 photosynthesis model
implemented for single cell
c. Tools developed to visualize the 3D
leaf, cell and sub-cellular compartment,
with defined physical and chemical
properties.
Development of for a reference
3D model of photosynthesis.
4.3.2 Develop a reference
model integrating C3 carbon
fixation process and
differentiation of
photosynthetic apparatus
A detailed photosynthetic carbon
metabolism network developed,
incorporating subcellular
compartmentalization and organelle
structure.
Develop a model of
photosynthesis metabolism and
differentiation with detailed
consideration of the 3
dimensional anatomical
Kinetic models that incorporate
multiple discrete data yet
maintain enough simplicity for
use by biological scientists.
•
•
Researchers can compare
model expectations against
experimental realities.
Outcome
•
•
•
•
•
Principal leaders to
debate/review methods for this
section.
Programmer/designer to
implement selection tool (12
programmer x months)
Mathematicians to help develop
of the 3D photosynthesis model
Programmer/designer to
implement the model (12
programmer x months).
Mathematicians to facilitate the
design of the multiscale
modeling framework
Programmer/designer to
implement the modeling
structure of organelle
environments (12 programmer
x months).
4.3.3 Develop a reference
3D leaf photosynthesis
model to simulate the
photosynthetic processes
and differentiation
a. A 3D photosynthesis model
implemented for a typical leaf, including
the energy balance and gas exchange
at the leaf surface
b. Methodology developed to dissect
contribution of anatomical and kinetic
parameters in the regulation of
photosynthesis
c. Existing modeling packages used to
implement a reference 3D
photosynthesis differentiation model
d. Potential approaches identified to
differentiate photosystem with C4
characteristics
Activity 4.4
Develop reference models to simulate metabolite transport, cell
development and expansion in a photosynthetic leaf tissue and develop
connection of models to genomic databases
Activity Leader: M. Gent
Outputs
Milestones
Required Resources
4.4.1 Simulation environment
to simulate leaf
photosynthesis process with
detailed consideration of
metabolite transport
a. Algorithms designed to enable design
of cells with different physical
properties, shapes, chemical reactions
and detailed consideration of metabolite
transport
b. Algorithms designed to link
discrete/stochastic processes with
continuous processes
c Visualization tools developed to
visualize simulation results in a 3 D
space.
A realistic simulation
environment for a 3D multicellular system with detailed
metabolite transport properties
4.4.2 Simulation environment
for differentiation,
development and cell
expansion in leaf tissues
a. Algorithms developed to link physical
processes, cell wall expansion and
osmotic pressure, to expansion of cells.
b. Cell development and expansion
processes included in kinetic models
Simulation of the process of
growth and differentiation within
leaf tissue.
•
Development of a reference
leaf photosynthesis and
differentiation model.
Outcome
•
•
•
•
•
Mathematicians to help develop
the 3D leaf photosynthesis
model
Programmer/designer to
implement the model (24
programmer x months).
Applied mathematician to
consult on coupling processes
in different cells.
Programmer/designer to
implement resulting algorithms
(24 programmer x months)
Applied mathematician to
consult on physics of
expansion.
Programmer/designer to
implement resulting algorithms
simulation environment.
c. Algorithms for influence of cell-cell
contact and geometry on cell
development/expansion, designed and
implemented.
(12 programmer x months)
•
4.4.3 Link photosynthesis
discrete database with
modeling framework for cell
structure and development.
a. Genome, transcriptome and
metabolome level data linked to
parameters in physical model to
facilitate functional genomics of cell
development and expansion
b. Interface developed to allow asking
“what if” questions
Direct linkage of the modeling
environment with data
assembled in the discrete
photosynthesis. data repository,
enables identification of genetic
basis for photosynthetic
processes and provides targets
for crop improvement.
•
•
Principal leaders and applied
mathematician debate/review
methods for this linkage.
Biologist/bioinformatician to
assist with linkage of genomic
information to the models (12
bioinformatician x months)
Programmer/designer to
implement resulting algorithms
(24 programmer x months)
4.4.4 Manage dynamic cellbased model development to
maintain tractability and ease
of use.
a. Methods developed to select and
eliminate parameters and/or network
interactions to maintain a relatively
robust dynamic model
b. Algorithms designed and
implemented to define response surface
of interacting networks for use in higher
levels of integration.
Activity 4.5
Develop a generic 3D multi-scale and multi-phenomena modeling
framework enable plug-and-play design of photosynthesis systems
Activity Leader: S.K. Lu
Outputs
Milestones
Outcome
Required Resources
4.5.1 Simulation environment
for modeling a reaction
diffusion system in a 3D
space
a. Major algorithms identified for 3
dimensional modeling of reaction
diffusion system.
b. Tools implemented to design 2
dimensional objects with defined
physical properties
c. Basic modeling platform implemented
for 3 dimensional reaction diffusion
system that will enable input of user
designed reaction equations in the
Development of a basic
modeling platform for 3
dimensional modeling of typical
reaction diffusion systems
assists in research
understanding of spatial
aspects of photosynthesis.
•
Cell models that incorporate
multiple discrete data yet
maintain enough simplicity for
use by biological scientists.
•
•
•
Principal leaders and applied
mathematician debate/review
methods for this selection.
Programmer/designer to
implement selection tool and
response surface algorithms (6
programmer x months)
Mathematicians to facilitate the
design of the simulation
environments
Programmer/designer to
implement the modeling
environments (24 programmer
x months)
particular objects;
d. Generic system implement for the
reference model of leaf photosynthetic
metabolism.
e. Effective visualization tools
implemented to show the 3 D structure
and anatomy.
4.5.2 Develop support
capacities to enable
simulation of complex
reaction diffusion systems by
biologists without
computational expertise
a. Simulation package parallelized to
use high performance computers
b. Simulation interface designed to
enable users to design leaf with
different cell shapes, distribution,
organelle shapes and distribution,
physical properties,
c. Algorithm designed for parameter
estimation based on model and time
series measurement data
d. Generic algorithms developed to
connect 3D models to genomic
databases
The implement of the
photosynthesis simulation
environment is supported by
high performance computers
and enables users to efficiently
simulate a system with userdefined structure and kinetic
properties
•
•
Mathematicians to help
parallelize the algorithms
developed in 3.5.1
Programmer/designer to
implement the simulation
environments (24 programmer
x months).
Objective 5
Develop resources for education, outreach and training relating to
photosynthesis science.
Objective Leader: T. Brutnell
Activity 5.1
Establish online web portal site for education, outreach and training.
Activity Leader: T. Brutnell
Outputs
Milestones
Outcome
Required Resources
5.1.1 Establish project web
portal section for education,
outreach and training
Design and implement baseline Web
site for photosynthesis GC education,
outreach and training.
“A one-stop-shop” for GC
education, outreach and
training about photosynthesis
science is a valuable resource
for public and academic
science education.
Outreach Coordinator with masters
or PhD in education to interface
with project team members (36
months)
Activity 5.2
Implement a capacity building strategy for photosynthesis research.
Activity Leader: T. Brutnell
Outputs
Milestones
Required Resources
5.2.1 Online training
a. Develop and publish training
Outcome
•
PhD-level plant scientists
•
Web site for publication.
materials and training
workshops (in the USA and
international partner
institutes) for photosynthesis
modeling.
materials for photosynthesis modeling.
b. Convene annual training workshop
for photosynthesis modeling.
•
Graphic designer?
Educator to organize
workshops.
Travel expense subsidies
(participants could/should also
contribute from their own
budgets)
•
•
Travel expenses.
Meeting expenses
•
•
•
trained in computational
and systems biology.
Computer science biology,
mathematics and physics
students recruited to the
field plant science
A new generation of
graduate students will be
trained who will be defined
by multidisciplinary skills in
computational biology and
plant science.
5.2.2 US scientists’
participation in international
project-affiliated meetings
and training workshops (e.g.
PICB, C4 rice consortium
meetings, international
photosynthesis
conferences).
US scientists participate in international
project-affiliated meeting.
5.2.3 Meetings of
international photosynthesis
research team members,
photosynthesis experts, and
graduate students/postdoc
from outside the US.
Shuttle science exchange undertaken
for GC project development.
5.2.4 National high school
curriculum resources for
introduction to
photosynthesis research.
a. Design and publish national high
school curriculum for photosynthesis
research.
b. Pilot high school curriculum at
teacher training workshop
c. Publish national high school
curriculum for broad dissemination.
High school students
encouraged to experience plant
biology as a technologically
exciting and relevant career
choice.
5.2.5 National college-level
curriculum resources for
university training in plant CI
relating to computational
systems biology modeling
and photosynthesis
research.
a. Design and publish national collegelevel curriculum for computational
systems biology modeling and
photosynthesis research.
b. Pilot high school curriculum at
selected university sites.
c. Publish national college curriculum
College level graduates of
biology are better equipped to
apply computational techniques
and knowledge about
photosynthesis, in their
professional career.
•
Travel and meeting expenses.
•
•
•
•
•
•
Educators to specify/design
curriculum in concert with
Principal leaders.
Graphic designer?
Editor/publisher for initial and
final product.
Educators to specify/design
curriculum in concert with
Principal leaders.
Graphic designer?
Editor/publisher for initial and
final product.
for broad dissemination.
Activity 5.3
Develop and dissemination resources for public education, outreach and
training about photosynthesis.
Activity Leader: T. Brutnell
Outputs
Milestones
Outcome
Required Resources
5.3.1 General public
information resource on
photosynthesis and the
project.
a. Establish general public information
resource on photosynthesis, populated
with baseline information.
b. Annually update public information
resource on photosynthesis and this
project.
•
•
5.3.2 A simplified
educational simulators of leaf
and/or whole plant
photosynthesis, targeting
elementary and secondary
school level students.
Design and implement a public interface
to get access to the photosynthesis
simulation environment developed via
this project for educational purposes.
Inform public on GMOs
Provide public interface for
questions on
photosynthesis and crop
productivity (food, feed,
fiber and bioenergy)
K – 12 students and general
public learn about
photosynthesis and
computational biology by simple
and entertaining hands on
computational experimentation.
•
•
•
•
Web site to present information
to the general public.
Web designer/manager to
maintain Web site.
Web site to host educational
simulator for public access.
Programmer/designer to
implement simulator and web
interface (12 programmer x
months)
Appendix A2 – Project Timeline
Activity
Year 1
Objective 1
To establish a community portal for photosynthesis research
1.1. Establish project Web 2.0
web portal
(Lead T. Brutnell)
Objective 2
2.1. Establish repository for
photosynthesis molecular data
(Lead P. Jaiswal)
Web server established and software
installed
Objective 3
3.1. Develop a semantic and
software framework for
development of a knowledge
discovery environment
(Lead R. Bruskiewich)
Web server populated and
reviewed by community
Year3
Implement web portal for
public outreach
Web portal structure and content edited and maintained throughout project
To establish a public data repository for photosynthesis research
Overall database architecture,
specified and implemented
-Omics database modules
developed
Research literature module developed
Germplasm, phenotype and
genotype (molecular variation)
data modules developed
Semantic standards for integration of
photosynthesis data implemented
2.2. Establish repository for
photosynthesis physiological
response data
(Lead M. Gent)
2.3. Establish repository for
photosynthesis system models
(Lead X. Zhu)
Year2
Design and implement a database for
physiological response data with
steps as in 2.1
Develop algorithms to extract
kinetic parameters from response
curves
Develop methodology and strategy for
embedding and using semantic
standards in the knowledge discovery
environment
Implement access protocols to
key (remote) online public
biological (plant) data sources
Develop data source access
standards for photosynthesis data
repository modules
Implement project-specific data
mining technology
Initiate integration of
germplasm, phenotype and
genotype (molecular
variation) data modules with
omics modules
Integrate physiological
response database with –
omics databases,and model
repository
Data schema specified for process
Web query and display to browse Continue development of
model repository
models and parameter sets,
integration of each repository
designed and implemented
defined under objective 2
To build a knowledge discovery environment for photosynthesis systems biology
Continue development of
open source software
framework to support
photosynthesis systems
biology knowledge discovery
environment
Continue development of
web-service access to
repository data for linkage of
analytical tools to research
data
Develop data source access
standards for photosynthesis data
repository modules
Develop framework for integrating
discrete and continuous data in
repositories and models
Develop text mining query and display
engine operating on literature module
and related databases
Define quality assurance methodology
Implement project-specific data
mining technology
3.3. Develop data mining tools
for photosynthesis knowledge
discovery
(Lead R. Bruskiewich)
3.4. Develop analytical tools for
photosynthesis knowledge
discovery
(Lead R. Bruskiewich)
Design and develop general data
mining engine to operate on
photosynthesis data repository
Develop and integrate statistical
tools for comparative analysis of
data sets
Review, integrate, and develop
sequence analysis and visualization
tools
Develop schema for comparative
genomics, sequence diversity, and
functional genomics data
Develop schema for pathway and
network data module
Develop and deploy comparative
query and visualization tools
Objective 4
To develop a simulation environment for systems modeling of photosynthesis metabolism and
differentiation
4.1. Develop the simulation
environment for kinetic
modeling of photosynthesis
metabolism and differentiation
(Lead X. Zhu)
4.2. Develop the auxiliary
algorithms to facilitate
application of the systems
models and link models to
databases
(Lead C. Myers)
Develop a simulation application for
kinetic modeling of metabolism and
photosynthetic machinery
Develop a simulation model that
integrates models of machinery
and metabolism
Cross-link photosynthesis
models to data repository for
model validation
Optimize parameter estimation
algorithms based on discrete data.
Integrate existing tools with simulation
environment.
Develop algorithms for linking
discrete/stochastic processes with
continuous processes
Develop software for linking
discrete databases with kinetic
modeling framework to allow for
automated model construction as
new data become available.
Explore strategies for model
reduction based on
parameter sensitivities and
response surfaces.
3.2. Develop text mining tools
for photosynthesis knowledge
discovery
(Lead R. Bruskiewich)
Continue development of
web-service access to
repository data for linkage of
analytical tools to research
data
Pilot manual validation
undertaken, using initial data sets
used from literature mining
Develop analytical, inference and
data visualization tool for
biological networks
Develop web interface for
manual curation of new
biological facts and
relationships
Continue deployment of
comparative tools on other
available data sets
Develop and integrate tools
for combining time-series
data of metabolites with gene
and protein expression data
4.3. Develop basic 3dimensional reference models
for photosynthetic metabolism
and differentiation
(Lead X. Zhu)
Implement 2-and 3-dimensional C3
photosynthesis models for a single
cell
4.4. Develop simulation model
for leaf photosynthesis with
consideration of metabolite
transport
(Lead M. Gent)
Integrate algorithms for membrane
transport in kinetic models of
metabolism
4.5. Develop a generic 3D
multi-scale and multiphenomena modeling
framework enable plug-andplay design of photosynthesis
systems
(Lead S. K. Lu)
Objective 5
5.1. Establish online web portal
site for education, outreach and
training
(Lead T. Brutnell)
5.2. Implement a capacity
building strategy for
photosynthesis research
(Lead T. Brutnell)
Identify major algorithms for 3D
modeling of reaction-diffusion
systems, and develop tools for basic
2D and 3D simulation
Develop tools for 3D, multiscale leaf
visualization
Develop a model of
photosynthesis metabolism and
differentiation with detailed
consideration of the 3
dimensional anatomical structure
of organelle
Development of a reference
leaf photosynthesis and
differentiation model
Generate a realistic simulation
environment for a 3D multicellular system with detailed
metabolite transport properties
Generate a model to simulate
the process of growth and
differentiation within leaf
tissue
Develop generic system
implementation for reference
model of leaf photosynthetic
metabolism
Develop capabilities for
simulations of complex
reaction diffusion systems,
including parallelization,
visualization, parameter
estimation, etc.
Develop algorithms for linking
discrete/stochastic processes with
continuous processes
To develop resources for education, outreach and training related to photosynthesis science
Hire outreach coordinator and begin
Design and implement baseline
Release public simulator for
development of photosynthesis
Web site for photosynthesis GC
modeling photosynthesis (see
curriculum modules
education, outreach and training
Objective 1)
Develop and publish training materials
for photosynthesis modeling
Convene annual training workshop for photosynthesis modeling
Convene high school teacher training workshops and develop
curriculum modules distributed over web portal
Support US scientists’ participation international project-affiliated meetings and training workshops, and
meetings of international photosynthesis research team members from outside the US
5.3. Develop and disseminate
resources for public education,
outreach and training about
photosynthesis.
(Lead T. Brutnell)
Develop and annually update general public information resource on photosynthesis and the project
outreach and training about
photosynthesis.
(Lead T. Brutnell)
Develop and refine simplified educational simulators of leaf and/or whole plant photosynthesis, targeting
elementary and secondary school students
Appendix B – Additional Expert Resources for this Project
Participant
Joseph Berry
Ken Boote
Jerry Edwards
Alistair Fernie
Bob Furbank
Bill Green
Vincent Gutschick
Mike Heath,
Julian Hibberd
Title & Affiliation
Professor, Department of Global Ecology
Carnegie Institution of Washington, USA
Professor, Agronomy Department, University of Florida,
Gainesville, FL
Professor
Biological Sciences
Washington State University
Pullman, WA, USA
Group Leader, Max Planck Institute of Molecular Plant
Physiology (MPIMP), Golm, Germany
Senior Principal Research Scientist/Project Leader,
CSIRO Plant Industry, Canberra, Australia
Professor, Massachusetts Institute of Technology, USA
Professor, Department of Biology, New Mexico State
University, Las Cruces, NM
Professor, Department of Computer Science, University
of Illinois at Urbana Champaign, IL
Senior Lecturer, Department of Plant Sciences,
University of Cambridge, Cambridge, UK
Raquell Holmes
Assistant Professor of Cell Biology, University of
Connecticut Health Center, Farmington, CT
Peter Horton
Department of Molecular Biology and Biotechnology,
Universitiy of Sheffield, Sheffield UK
Andrew Leakey
Assistant Professor
Department of Plant Biology, University of Illinois at
Urbana Champaign,USA
Expertise/Role
Physiological means by which plants adapt to environmental
stress and climactic change, and photosynthetic
mechanisms used by higher plants and algaes to fix carbon
dioxide.
Measurement and modeling of canopy photosynthesis and
crop growth, effect of environment and water stress.
C4 photosynthesis, single-cell C4 photosynthesis, C4
pysiology
Central metabolism
Genetic manipulation of carbon partitioning and
photosynthesis in crop plants, plant phenomics and imaging
plant performance, mechanisms of drought tolerance in
cereals.
Analysis methodology for error management, robustness,
range and sensitivity of results to errors (error bars) in the
input.
Plant water use, drought physiology and ecology,
photosynthesis, process based mathematical models.
Numerical computing
Evolution of C4 photosynthesis; Role of proteins that have
been co-opted into the C4 pathway in C3 species; Alterations
in gene expression associated with the C4 pathway
Cell & Computational Biologist / Development and
application of dynamic simulators to understand biological
systems – geometry, networks and pathways - at the cellular
level.
Photosynthetic electron transport
Plant responses in natural and agricultural ecosystems to
global climate change and abioitic stress. Genomic
regulation of plant ecological strategy.
Richard Leegood
Hua-Ling Mi
Steve Long
ZengRong Liu,
Congming Lu
Jim Mattson
Andrew Millar
Erik Murchie
Timothy Nelson
Christoph
Peterhänsel
Richard Peterson,
Donald Ort
Rowan Sage
Tom Sharkey
Senior Lecturer, Department of Animal and Plant
Sciences, University of Sheffield
Professor, Institute of Plant Physiology and Ecology,
Shanghai Institutes for Biological Sciences, Chinese
Academy of Sciences, USA
Professor of Crop Sciences, Robert Emerson
Professor, and Resident Scientist for the National
Center for Supercomputing Applications, University of
Illinois
Professor, Department of Mathematics, Shanghai
University, China
Professor, Photosynthesis Research Center, Institute of
Botany, Chinese Academy of Science, China
Assistant Professor, Simon Fraser University, Burnaby,
BC, Canada
Professor & Chair of Systems Biology, Institute of
Molecular Plant Sciences
School of Biological Sciences
University of Edinburgh,
Edinburgh, Scotland, UK
Division of Plant and Crop Sciences, School of
Biosciences, University of Nottingham, Sutton
Bonington, UK
Professor, Yale University
Research Group Leader, University of Aachen,
Germany
Agricultural Scientist, Department of Biochemistry &
Genetics Connecticut Agricultural Experiment Station,
Professor of Plant Biology, USDA/ARS Photosynthesis
Research Unit, Adjunct Professor of Crop Sciences,
Department of Plant Biology, University of Illinois, USA
Professor and Chairman, Department of Botany (Plant
and Microbial Biology), University of Toronto, Ontario,
Canada
Professor and Chair
Dept. Biochem. Mol. Biol.
Regulation and control of photosynthetic carbon and nitrogen
metabolism
Protein-protein Interactions in chloroplasts
Plant Physiology, Environmental Science
Applied mathematics
Photosynthesis of rice and wheat, Photoinhibition and
photoprotection, Molecular physiological mechanisms of
photosynthesis under environmental stress
Genetic regulation of vascular tissue development in plants
Molecular & Systems Biologist / Image and related
measurements of transcription and signal transduction in
Arabidopsis plants wild type and mutant (transgenic) relative
to circadian rhythms
Rice photosynthesis and crop productivity
Developmental genetics for plant leaf development and
photosynthesis.
Genetic engineering of photorespiration in Arabidopsis,
regulation of photosynthetic gene expression in maize
Measurement of gas exchange and photorespiration,
biophysics of photosynthetic electron transport.
The effect that specific environmental factors and abiotic
stresses have on the photosynthetic performance of crop
plants.
C4 plants and their evolution; plant physiological ecology;
mechanisms of plant response to global climate change;
comparative photosynthesis of C3 and C4 plants; stress
physiology; water relations; mechanisms affecting
productivity, competition, and species distributions
Plant-atmosphere interactions; isoprene emission;
photosynthesis, especially carbon metabolism; plant
Bob Turgeon
Klaas van Wijk
Susanne von
Caemmerer
Peter Westhoff
Andreas Weber
Sheng Zhong
Michigan State University
Professor, Department of Plant Biology, Cornell
University, USA
Associate Professor, Department of Plant Biology,
Cornell University
Professor of Molecular Plant Physiology, Research
School of Biological Sciences, The Australian National
University, Canberra, Australia
Dean of the Faculty of Science, Department of Biology
– Botany, Heinrich Heine University Düsseldorf,
Düsseldorf, Germany
Professor and Chair
Institute of Plant Biochemistry
Heinrich-Heine-University, Germany
Assistant Prof. Bioengineering Department, University
of Illinois at Urbana Champaign, USA
biochemistry and biophysics
Long-distance nutrient transport in the phloem, leaf
development, and the structure and function of
plasmodesmata, the fine pores that connect plant cells
Plant Physiology, Plant Molecular Biology, Plant
Biochemistry
Physiology and biochemistry of photosynthesis;
mathematical modeling of photosynthetic processes;
analysis of photosynthesis and stomatal physiology by
genetic manipulation; stable isotope fractionations in plants
Evolution of genes and enzymes related to C4
photosynthesis; genetic transformation of C4 plants
The study of transport processes that connect the metabolic
pathways in plastid and cytosol, in particular those
transporters involved in carbon- and nitrogen-metabolism,
using biochemistry, molecular biology, and molecular plant
physiology.
systems modeling, machine learning, statistical methods.
Appendix C - Biological Data Sets Available to this Grand Challenge Project
Category
Type
Specific Available Data
Specific Sources
•
Annotated rice genome
assembly
Annotated
Reference
Genomic
Assemblies
Structural
Genomics
Molecular
Variation
Annotated sorghum genome
assembly
•
•
JGI (Phytozome) Sorghum Genome Assembly
(http://www.phytozome.net/sorghum.php)
SorghumDb (http://algodon.tamu.edu/sorghumdb.html)
Annotated maize genome
assembly
•
•
Maize Sequencing Project (http://www.maizesequence.org)
Maize Assembled Genomic Island
(http://magi.plantgenomics.iastate.edu/)
Sequence variation in rice
•
OryzaSNP (www.oryzasnp.org)
Sequence variation in sorghum
•
Sorghum Diversity Database
(http://sorghumdiversity.org/sorghum/research.html)
Sequence variation in maize
•
Panzea (http://www.panzea.org/)
•
Gramene’s diversity module
(http://www.gramene.org/db/diversity/diversity_view)
•
Gramene Comparative Plant Genome Database
(http://www.gramene.org/genome_browser/index.html )
•
•
Arabidopsis Information Resource (TAIR ; www.arabidopsis.org)
Gramene Comparative Plant Database
(http://www.gramene.org/genome_browser/index.html )
Genetic diversity and Alleleic
polymorphism
Comparative
Sequence
Alignments
•
•
NSF-funded Rice Genome Annotation project
(http://rice.plantbiology.msu.edu/)
Rice Annotation Project (http://rapdb.dna.affrc.go.jp/)
Rice Information System
(http://rice.genomics.org.cn/rice/index2.jsp)
Whole plant genome
comparison, genome and
genewise alignments,
comparison to genetic maps, –
rice, maize, sorghum, poplar,
grape, Arabidopsis, wild rice, sequence alignments
Model Plants
•
Rice gene expression
Molecular
Expression
Comparative transcriptome,
proteome and metabolome for
rice, sorghum and maize
(C4 leaf cell specific assays)
Plant Expression datasets
Functional
Genomics
Survey for C4 photosynthesis
characteristics in wild rice
species and cultivated rice
subtypes
Phenotypic
analyses
•
•
Yale “rice atlas” transcriptome data
NSF-funded project, Comparative Analysis of C3 and C4 Leaf
Development in Rice, Sorghum and Maize
(http://C3C4.tc.cornell.edu/ and
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=
0701736)
•
PlexDB (www.plexdb.org )
•
International Rice Research Institute (IRRI) project data set
measuring leaf vein spacing, CO2 compensation point and other
characteristics in a large collection of wild rice genetic resource
accessions
OryzaSNP genotyped Oryza sativa germplasm set
(www.oryzasnp.org )
•
•
Sorghum C3 revertant mutants
Photosynthesis
Physiology
•
•
Gene
Regulation
Plant gene promoter databases
Plant
Physiology
Leaf anatomy, biochemistry,
metabolic flux data, physiology
data
NSF Rice Oligonucleotide Array Project
(http://www.ricearray.org/)
Rice MPSS (http://mpss.udel.edu/rice/)
Rice Expression Database (http://red.dna.affrc.go.jp/RED/)
•
International Rice Research Institute (IRRI) project data set
screening EMS mutants for disrupted vein spacing and
photosynthesis
USDA-ARS Plant Stress and Germplasm Development Unit,
EMS Sorghum Mutant Population
(www.lbk.ars.usda.gov/psgd/sorghum/till)
•
http://www.arabidopsis.org/portals/genAnnotation/genome_annot
ation_tools/cis_element.jsp
•
Published photosynthesis literature and associated crop and leaf
physiology data sets
Shanghai Institute of Biological Sciences
IRRI wild rice measurements (CO2 compensation points, vein
spacing)
•
•
Thomas P. Brutnell
[email protected]
Professional Preparation:
University of Connecticut
Yale University
University of Oxford, UK
Biology
Biology
Plant Sciences
Appointments:
Associate Scientist
Adjunct Professor
Assistant Scientist
BBSRC David Phillips Research Fellow
Postdoctoral Associate
NIH Research Assistantship
University Honors Scholar
Phi Beta Kappa awarded
Boyce Thompson Institute
Cornell University
Boyce Thompson Institute
University of Oxford
University of Oxford
Yale University
University of Connecticut
University of Connecticut
B.S., magna cum laude 1989
Ph.D. 1995
1995-1999
Jan.2006 – present
2000 - present
Dec.1999 – 2005
1997 -1999
1995 -1997
1993 -1995
1989
1989
Related Publications:
Zelitch, I., N.P. Schultes, R.B. Peterson, P. Brown and T.P. Brutnell (2009) High glycolate oxidase activity
is required for survival of maize in normal air. Plant Physiol 149:195-204.
Covshoff, S., W. Majeran, P. Liu, J.M Kolkman, K.J. van Wijk and T.P. Brutnell (2008) Deregulation of
maize C4 photosynthetic development in a mesophyll cell-defective mutant. Plant Physiol 146:14691481.
Sawers, R.J.H., P. Liu, K. Anufrikova, J.T.G. Hwang and T.P. Brutnell (2007) A multi-treatment
experimental system to examine photosynthetic differentiation in the maize leaf, BMC Genomics 8:12.
Sawers R.J.H., P.R. Farmer, P. Moffett and T.P. Brutnell (2006) In planta transient expression as a
system for genetic and biochemical analyses of chlorophyll biosynthesis. Plant Methods 2:15.
Garg, A.K., R.J.H. Sawers, H. Wang, J.-K. Kim, J.M. Walker, T.P. Brutnell, M.V. Parthasarathy, R.D.
Vierstra, and R.J. Wu (2006) Light-regulated overexpression of an Arabidopsis phytochrome A gene
in rice alters plant architecture and increases grain yield. Planta 223: 627-636.
Significant Publications:
Harjes, C.E., T.R. Rocheford, L. Bai, T.P. Brutnell, C.B. Kandianis, S.G. Sowinski, A.E. Stapleton, R.
Vallabhaneni, M. Williams, E.T. Wurtzel, J. Yan, E.S. Buckler (2008) Natural genetic variation in
lycopene epsilon cyclase tapped for maize biofortification. Science 319:330-333.
Conrad, L.J., L. Bai, K. Ahern, K. Dusinberre*, D.P. Kane* and T.P. Brutnell (2007) State II Dissociation
(Ds) element formation following Activator (Ac) excision in maize, Genetics 177:737-747.
Sheehan, M.J., L.M. Kennedy*, D. Costich and T.P. Brutnell (2007) Subfunctionalization of PhyB1 and
PhyB2 in the control of seedling and mature plant traits in maize. Plant J. 49:338-353.
Bai L, M. Singh, L. Pitt*, M. Sweeney* and T.P. Brutnell (2007) Generating novel allelic variation through
Activator insertional mutagenesis in maize. Genetics 175:981-992.
Sawers R.J., J. Viney, P.R. Farmer, R.R. Bussey*, G. Olsefski*, K. Anufrikova, C.N. Hunter and T.P.
Brutnell (2006) The maize Oil yellow1 (Oy1) gene encodes the I subunit of magnesium chelatase.
Plant Mol Biol 60: 95-106.
* undergraduate students supported through NSF funding
Synergistic Activities:
Outreach Activities I led development of a Cornell Summer Internship Program that served as a
foundation for our current REU program at Cornell (http://www.bti.cornell.edu/educationInternships.php). I
have also developed a high school teacher training program
(http://www.bti.cornell.edu/educationTeacherPrograms.php) with the goal of introducing concepts of plant
genomics research into the high school science curriculum. These programs have been developed to
encourage interaction between research scientists, students and educators and increase the participation
of students from traditionally underrepresented groups in NSF-funded research.
Scientific Service
2007 – present Member, Maize Genetics Executive Committee
2008
Chair, Maize Genetics Conference Steering Committee
2006 – present Associate Editor, Genetics
2005 – present Consultant, Monsanto
2005 – 2007
Panel Member, USDA
2004 – present Panel Member, site visit Team Member and PGRP-project Advisor, NSF
2000 – present ad hoc reviewer for Genetics, Genome Res, Mol Genet Genomics, Nat Genet, Plant Cell,
Plant J, Plant Physiol, PLoS Biol, PNAS, NSF, USDA, DOE
2000 – present Member, American Association for the Advancement of Science, American Society of
Plant Biology, Genetics Society of America
Teaching Experience
Molecular Techniques in Plant Science, Cold Spring Harbor Laboratory. I co-organized this three week
course in plant molecular biology and genomics techniques (summer).
Light Signal Transduction in Plants, BIOPL4829, Dept. of Plant Biology, Cornell University. I teach a four
week module on light signaling for graduate students and senior undergraduates (spring term).
Advanced Plant Genetics, PLBR 6060, Dept. of Plant Breeding, Cornell University. I teach two classes on
plant transposable elements in this graduate level course in plant genetics (spring term).
Methods of Plant Breeding, PLBR 4060, Dept. of Plant Breeding, Cornell University, I organize a half-day
lab on maize genetics (fall term).
Collaborators and Other Affiliations:
(i) Collaborators:
Sharon Regan (Queen’s Univ.)
Volker Brendel (Iowa State Univ.)
Torbert Rocheford (Purdue Univ.)
Richard Bruskiewich (IRRI)
Tom Ruff (Monsanto)
Edward Buckler (Cornell Univ./USDA)
Jocelyn Rose (Cornell University)
Thomas Clemente (Univ. of Nebraska)
Neil Schultes (Conn. Ag. Exp. Station)
Dean DellaPenna (Michigan State Univ.)
John Sheehy (IRRI)
Jon Duvick (Iowa State Univ.)
Ann Stapelton (NCSU)
Mike Edgerton (Monsanto)
David Stern (Boyce Thompson Inst.)
Ajay Garg (Cornell Univ.)
Qi Sun (Cornell University)
Martin Gent (Conn. Ag. Exp. Station)
Matthew Terry (Univ. of Southampton, UK)
Julian Hibberd (Univ. of Cambridge)
Robert Turgeon (Cornell Univ.)
Steve Huber (Purdue Univ.)
Klaas van Wijk (Cornell Univ.)
Matthew Hudson (Univ. of Illinois)
Rick Vierstra (Univ. of Wisconsin)
C. Neil Hunter (Sheffield Univ.)
Erik Vollbrecht (Iowa State Univ.)
Pankaj Jaiswal (Oregon State Univ.)
Hiayang Wang (Boyce Thompson Inst.)
Steve Kresovich (Cornell Univ.)
Susan Wessler (Univ. of Georgia)
Peng Liu (Iowa State Univ.)
Mark Williams (Dupont)
Mike Muszynski (Iowa State Univ.)
Elli Wurtzel (Lehman College)
Chris Myers (Cornell Univ.)
Israel Zelitch (Conn. Ag. Exp. Station)
Tim Nelson (Yale University)
Xinguang Xu (MPI, Shanghai)
Richard Peterson (Conn. Ag. Exp. Station)
(ii) Graduate and Postdoctoral Advisors: Stephen Dellaporta (Yale University), Jane Langdale (Univ. of
Oxford)(iii) Thesis Advisor (7): Ling Bai, Liza Conrad, Sarah Covshoff, Patrice Dubois, Jake Mace, Nicole
Hanley Markelz, Moira Sheehan (all Cornell University) Postgraduate-Scholar Sponsor (8): Denise
Costich, Tesfamichael Kebrom, Judith Kolkman, Kazuhiro Kikuchi, Pinghua Li, Anasuya Mohapatra,
Ruairidh Sawers, Manjit Singh, (all Boyce Thompson Inst.)
Richard Bruskiewich
[email protected]
Professional Preparation:
University of British Columbia
University of British Columbia
Simon Fraser University, BC
Medical Genetics
Biochemistry, Molecular
Biology & Genetics Option
General Studies
(Minor Computing)
Ph.D. 1999
B.Sc. with honours 1992
B.A. 1987
Appointments:
Senior Scientist
International Rice Research Institute (IRRI) Sept. 2000 – present
Adjunct Professor
University of the Philippines
2001- present
Postdoctoral Research Fellow
Sanger Centre, UK
1999 - 2000
Related Publications:
Koji Doi, Aeni Hosaka, Toshifumi Nagata, Kouji Satoh, Kohji Suzuki, Ramil Mauleon, Michael J
Mendoza, Richard Bruskiewich and Shoshi Kikuchi. (2008) Development of a novel data
mining tool to find cis-elements in rice gene promoter regions. BMC Plant Biology 2008,
8:20. doi:10.1186/1471-2229-8-20 (http://www.biomedcentral.com/1471-2229/8/20)
Richard Bruskiewich, et al. 2008. “The Generation Challenge Programme Platform: Semantic
Standards and Workbench for Crop Science,” International Journal of Plant Genomics, vol.
2008, Article ID 369601, 6 pages. doi:10.1155/2008/369601
Rice Annotation Project (2007) The Rice Annotation Project Database (RAP-DB): 2008 update.
Nucleic Acids Research, doi:10.1093/nar/gkm978
Samart Wanchana, Supat Thongjuea, Victor Jun Ulat, Mylah Anacleto, Ramil Mauleon, Mathieu
Conte, Matthieu Rouard, Manuel Ruiz, Nandini Krishnamurthy, Kimmen, Theo van Hintum
and Richard M. Bruskiewich (2007) The Generation Challenge Programme Comparative
Plant Stress-Responsive Gene Catalogue. Nucleic Acids Research,
doi:10.1093/nar/gkm798
Takeshi Itoh, Tsuyoshi Tanaka, Roberto A. Barrero, Chisato Yamasaki, Yasuyuki Fujii, Phillip B.
Hilton, Baltazar A. Antonio, Hideo Aono, Rolf Apweiler, Richard Bruskiewich, et al. 2007.
Curated genome annotation of Oryza sativa ssp. japonica and comparative genome
analysis with Arabidopsis thaliana. 2007. Genome Res. 17:175-183
Significant Publications:
Richard Bruskiewich, Guy Davenport, Tom Hazekamp, Thomas Metz, Manuel Ruiz, Reinhard
Simon, Masaru Takeya, Jennifer Lee, Martin Senger, Graham McLaren, and Theo Van
Hintum. 2006. The Generation Challenge Programme (GCP)-Standards for Crop Data.
OMICS 10(2):215-219
Kenneth L. McNally, Richard Bruskiewich, David Mackill, C. Robin Buell, Jan E. Leach, and
Hei Leung. 2006. Sequencing Multiple and Diverse Rice Varieties. Connecting WholeGenome Variation with Phenotypes. Plant Phys. 141:1–6
McLaren, CG; Bruskiewich, RM; Portugal, AM; Cosico, AB. 2005. The International Rice
Information System. a platform for meta-analysis of rice crop data. Plant Physio. 139 (2):
637-642 International Rice Genome Sequencing Project, 2005. The map-based sequence of
the rice genome. Nature 436, 793-800.
Richard Bruskiewich and Samart Wanchana S. 2007. C4 rice: brainstorming from
bioinformaticians. in Charting new pathways to C4 rice, ed. by J. E. Sheehy, P. L. Mitchell
and B. Hardy, p. 381-398, ill. Ref. Los Banos, Laguna: IRRI.
Synergistic Activities:
Scientific Service:
Sept. 2000 – present Senior Scientist, Computational and Systems Biology; IRRI Frontier
Project & Consortium for C4 Rice Photosynthesis.
Duties: Leading the IRRI-based bioinformatics and systems biology team
developing and applying computational tools and methodology to store,
analyze, integrate and interpret project data within the Bill and Melinda
Gates Foundation funded IRRI Frontier Project and Consortium for C4
Rice Photosynthesis.
1999 - 2000
Postdoctoral Research Fellow, Human Analysis Group, Sanger Centre,
Welcome Trust Genome Campus, Hinxton, Cambridgeshire, United
Kingdom.
Duties: assessment of the sensitivity and specificity of ab initio gene
prediction algorithms; whole genome annotation and visualization of the
first completed human chromosome, 22 (published in Nature, December
1999); whole genome analysis and visualization of Single Nucleotide
Polymorphisms in human chromosome 22
(i) Collaborators:
Apweiler, Rolf (European Bioformatics Inst.)
Brutnell, Thomas (Boyce Thompson Inst)
Buell, C. Robin (Michigan State Univ.)
Doi, Koji (National Inst. Of Agrobio.
Sciences)
Gent, Martin (Conn. Ag. Exp. Station)
Hazekamp, Tom (IPGRI)
Hosaka, Aeni (National Inst. Of Agrobio.
Sciences)
Jaiswal, Pankaj (Oregon State Univ.)
Jun Ulat, Victor (IRIS)
Khush, Gurdev (IRRI)
Kikuchi, Shoshi (National Inst. Of Agrobio.
Sciences)
Krishnamurthy, Nandini (Univ. of California)
Leung, Hei (IRRI)
Mackill, David (IRRI)
Madamba, Ma (IRRI)
Yamazaki, Yukiko (National Institute of Genetics)
Yap, Immanuel (Cornell Univ.)
Zhu, Xinguang (MPI, Shanghai)
Mauleon, Ramil (IRRI)McLaren, Graham
(IRRI)
Mendoza, Michael (IRRI)
Mertz, Thomas (IRRI)
Myers, Chris (Cornell University)
Nagata, Toshifumi (National Inst. Of
Agrobio. Sciences)
Portugal, Arlett (IRRI)
Ramos-Pamplona, Marilou
Rouard, Mathieu (CGAR)
Ruiz, Manuel (CIRAD)
Satoh, Kouji (National Inst. Of Agrobio.
Sciences)
Sjolander, Kimmen (Univ. of California)
Suzuki, Kohji (Hitachi Software Engineering)
Van Hintum, Theo (Centre for Genetic
Resources)
Wanchana, Samart (IRRI)
Wang, Guoliang (Ohio State Univ.)
Martin Paul Neville Gent
[email protected]
Professional preparation:
Oberlin College
Yale University
University of Pittsburgh
Appointments:
Agricultural Scientist
Associate Scientist
Assistant Scientist
Chemistry
Physical Chemistry
Life Sciences
Connecticut Agricultural Experiment Station
Connecticut Agricultural Experiment Station
Connecticut Agricultural Experiment Station
B.A., with Honors 1971
Ph.D. 1975
1975 - 1978
Oct 2003 - present
Oct 1989 - Oct 2003
April 1978 - Oct 1989
Related publications:
Gent, M.P.N. 2008. Effect of Shade on Water and Nutrient Use in Greenhouse Tomato. J. Amer. Soc.
Hort. Sci. 133:619-627.
Gent, M.P.N., J.C. White, B.D. Eitzer, M.J.I. Mattina. 2007. Modeling the Difference among Cucurbita in
Uptake and Translocation of p,p'-dichlorophenyl-1,1-dichloroethylene. Environ. Toxicol. Chem.
26:2476-2485.
Gent, M.P.N. 2006. Modeling the Effect of Nutrient Solution Composition and Irradiance on Accumulation
of Nitrate in Hydroponic Lettuce. Acta Horticulturae 718:469-476.
Gent, M.P.N. 2005. Effect of genotype, fertilization, and season on free amino acids in leaves of salad
greens grown in high tunnels. J Plant Nutrition 28:1-14.
Gent, M.P.N. 2003. Effect of conductivity and nitrate supply ratio on nitrate accumulation in hydroponic
lettuce. HortScience 38:222-227.
Significant publications:
Gent, M.P.N., J.C. White, Z.D. Parrish, M. Iseleyen, B.D. Eitzer, M.J.I. Mattina. 2007. Uptake and
Translocation of p,p’-dichlorodiphenyldichloroethylene supplied in Hydroponics Solution to Cucurbita.
Environ. Toxicol. Chem. 26:2467-2475.
Gent, M.P.N. 2007. Effect of Degree and Duration of Shade on Quality of Greenhouse Tomato.
HortScience 42:514-520.
Gent, M.P.N. 2004. Efficacy and persistence of paclobutrazol applied to rooted cuttings of rhododendron
before transplant. HortScience 39:105-109.
Gent, M.P.N. 2002. Modelling Intra-cellular control of nitrate and long distance transport in plants. Acta
Horticulturae 593:93-99.
Gent, M.P.N., Y-Z Ma. 2000. Growth and mineral nutrition of tomato seedlings under diurnal temperature
variation of the root and shoot. Crop Science 40:1629-1636.
Synergistic activities:
Outreach Activities
My research is primarily directed to improving commercial production of ornamental and vegetable crops
in greenhouses, in particular relating environment and fertilization to production, quality, and value for
human nutrition. Outreach consists of visits and collaboration with growers, talks to growers groups, and
interaction with Extension Agents
1995 – present
Steering ctte New England Vegetable and Fruit Growers Conference
1997 – present
Regional Research Ctte Commercial Greenhouse Production
2005 – present
USDA SBIR Grant with Geremia Greenhouse, Wallingford CT
Scientific Service:
1993-97, 2002-04, 2008-10
Chair, Working group on Plant Biology Amer. Soc. Hort. Sci.
2001 - present
Associate editor, J. Plant Nutrition
1985 – present
ad hoc reviewer for J. Amer. Soc. Hort. Sci., Hortscience, Acta Hort.,
Crop Science, Agron. J., Int. J. Phytoremediation
1980 – present
member Amer. Soc. Adv. Sci., Amer. Soc. Agronomy, Amer. Soc. Hort. Sci., Int.
Soc. Hort. Sci., Amer. Soc. Plant Biology
Collaborators and Other Affiliations:
(i) collaborators
Brutnell, Thomas P. (Boyce Thompson Institute)
Burger, William (CT Agric Experiment Station)
Eitzer, Brian (CT Agric Experiment Station)
Elmer, Wade (CT Agric Experiment Station)
Ferandino, Francis (CT Agric Experiment Station)
Gage, Daniel (University of Connecticut)
Geremia, Joseph (Geremia Greenhouse)
Iseleyen, Mohamed (CT Agric Experiment Station)
Kiyomoto, Richard (University of Connecticut)
Ma, Yong Zhan (Yale University)
Mattina, MaryJane (CT Agric Experiment Station)
McAvoy, Richard (University of Connecticut)
Parrish, Zachia (CT Agric Experiment Station)
Smets, Barth (University of Connecticut)
Wang, X (CT Agric Experiment Station)
Ward, Jeffrey (CT Agric Experiment Station)
White, Jason (CT Agric Experiment Station)
(ii) Graduate and Postdoctoral advisors:
Prestegard, James (University of Georgia); Ho, Chien (Carnegie Mellon University)
Pankaj Jaiswal
[email protected]
Professional Preparation:
University of Lucknow, India
University of Lucknow, India
University of Lucknow, India
Botany & Chemistry
Biochemistry
Botany & Plant Molecular Biology
B.Sc 1990
M.Sc 1992
Ph.D. 1998
Appointments
2008-Present Assistant Professor; Dept. of Botany & Plant Pathology, Oregon State University
2007-2008 Sr. Research Associate; Dept. of Plant Breeding & Genetics, Cornell University
2003-2006 Research Associate; Dept. of Plant Breeding & Genetics, Cornell University
2001-2002 Postdoctoral Associate; Dept. of Plant Breeding and Genetics, Cornell University
1999-2000 Postdoctoral Associate; Boyce Thompson Institute at Cornell University
1998–1999 Postdoctoral Associate; Vienna Biocenter, Univ. of Vienna, Austria
1995-1998 Senior Research Fellow, National Botanical Research Institute, Lucknow, India.
1992-1994 Junior Research Fellow, National Botanical Research Institute, Lucknow, India.
Five Publications Most Closely Related to the Topic:
Liang, C., P. Jaiswal, C. Hebbard, S. Avraham, E. S. Buckler, T. Casstevens, B. Hurwitz, S. McCouch, J.
Ni, A. Pujar, D. Ravenscroft, L. Ren, W. Spooner, I. Tecle, J. Thomason, C. W. Tung, X. Wei, I. Yap,
K. Youens-Clark, D. Ware and L. Stein (2008). Gramene: a growing plant comparative genomics
resource. Nucleic Acids Res. 36(Database issue): D947-53. Epub 2007 Nov 4.
Jaiswal P, Ni J, Yap I, Ware D, Spooner W, Youens-Clark K, Ren L, Liang C, Zhao W, Ratnapu K, Faga B,
Canaran P, Fogleman M, Hebbard C, Avraham S, Schmidt S, Casstevens TM, Buckler ES, Stein L,
McCouch S (2006) Gramene: a bird's eye view of cereal genomes. Nucleic Acids Res. 34: D717-723.
Ware DH*, Jaiswal P*, Ni J, Yap IV, Pan X, Clark KY, Teytelman L, Schmidt SC, Zhao W, Chang K,
Cartinhour S, Stein LD, McCouch SR. Gramene, a tool for grass genomics. Plant Physiol. 2002;
130(4): 1606-13. (* equal contribution)
Ware D, Jaiswal P, Ni J, Pan X, Chang K, Clark K, Teytelman L, Schmidt S, Zhao W, Cartinhour S,
McCouch S, Stein L (2002) Gramene: a resource for comparative grass genomics. Nucleic Acids Res
30: 103-105.
Gene Ontology Consortium. The Gene Ontology project in 2008." Nucleic Acids Res. 36(Database issue):
D440-4. Epub 2007 Nov 4.
Five Other Publications:
Azevedo, J., F. Courtois, M. A. Hakimi, E. Demarsy, T. Lagrange, J. P. Alcaraz, P. Jaiswal, L. MarechalDrouard and S. Lerbs-Mache (2008). "Intraplastidial trafficking of a phage-type RNA polymerase is
mediated by a thylakoid RING-H2 protein." Proc Natl Acad Sci U S A 105(26): 9123-8.
Jaiswal P, Avraham S, Ilic K, Kellogg EA, Pujar A, Reiser L, Seung RY, Sachs MM, Schaeffer M, Stein L,
Stevens P, Vincent L, Ware D, Zapata F (2005) Plant Ontology (PO): A controlled vocabulary of plant
structures and growth stages. Comparative and Functional Genomics 6: 388-397
Pujar A, Jaiswal P, Kellogg EA, Ilic K, Vincent L, Avraham S, Stevens P, Zapata F, Reiser L, Rhee SY,
Sachs MM, Schaeffer M, Stein L, Ware D, McCouch S (2006) Whole-plant growth stage ontology for
angiosperms and its application in plant biology. Plant Physiol. 142: 414-428.
Gene Ontology Consortium, The Gene Ontology (GO) Project in 2006. Nucleic Acids Research. 2006 Jan
1;34:D322-6
Beardslee TA, Roy-Chowdhury S, Jaiswal P, Buhot L, Lerbs-Mache S, Stern DB, Allison LA. A nucleus
encoded maize protein with sigma factor activity accumulates in mitochondria and chloroplasts. Plant
J. 2002; 31(2): 199-209
Synergistic Activities:
1. Co-PI on the NSF funded Gramene database project on Plant Comparative genomics (2007-2011;
DBI# 0703908). Co-PI on the NSF funded Plant Ontology Consortium grant (2003-2006; DBI#
0321666). Co-organizer for Ontologies for Biological Databases workshop at Plant and Animal
genome meetings (2004-2008), Plant Ontology workshops at ASPB-Plant Biology meeting (2004,
2007) and Ontology workshops at Cornell University (2006) and Cold Spring Harbor Laboratory
Biographical sketch, Pankaj Jaiswal
Page 1
(2007). I am the Gramene project’s developer and coordinator for cereal specific pathway databases.
I also lead the development of Plant Ontology and contribute the cereal plant specific intellectual
inputs to Gene Ontology Project (2001-present).
2. Teaching Plant Physiology, BOT331, Dept. of Botany & Plant Path, Oregon State Univ. (2009)
3. Served as adhoc reviewer for various NSF and USDA grant proposals, book chapters. several
scientific journals that include, Science, Bioinformatics, Genetics, Plant Physiology, Plant Molecular
Biology, Molecular Genetics and Genomics, Genome Biology, BioMed Central, Plant Cell Physiology,
Journal of Plant Biochemistry and Biotechnology and Physiology and Molecular Biology of Plants.
4. Advisor: Generation Challenge Programme, Subprog. 4, Domain Modeling & Ontology task (2007present)
Conflict of Interest:
(i) Collaborators & Other Affiliations:
Allison, Lori A. (University of Nebraska-Lincoln)
Avraham, Shulamit (CSHL)
Beardslee, Thomas A. (Univ of Nebraska-Lincoln)
Buhot, Laurence (Université Joseph Fourier)
Brutnell, Thomas, P. (Boyce Thompson Inst.)
Canaran, Payan (CSHL)
Cartinhour, Samuel (USDA-ARS)
Casstevens, Terry M. (Cornell Univ.)
Chang, Kuan (CSHL, WUSTL)
Clark, Kenneth (CSHL)
Faga, Benjamin (CSHL)
Ilic, Katica (TAIR, Stanford Univ.)
Lerbs-Mache, Silva (Université Joseph Fourier)
Ni, Junjian (Cornell Univ. )
Pan, Xiaokang (CSHL, Iowa State Univ.)
Pujar, Anuradha (Cornell Univ.)
Ratnapu, Kiran (CSHL)
Ravenscroft, Dean (Cornell Univ.)
Reiser, Leonore (TAIR, Stanford Univ.)
Ren, Liya (CSHL)
Roy-Chowdhury, Sanchita (University of
Nebraska-Lincoln)
Sachs, Martin M. (USDA-ARS)
Schaeffer, Mary (USDA-ARS)
Schmidt, Steve (CSHL)
Stevens, Peter (Missouri Botanical Garden)
Teytelman, Leonid (CSHL, UC Berkley)
Tung, Chih-wei (Cornell Univ.)
Vincent, Leszek (Univ. of Missouri, Colombia)
Yap, Immanuel (Cornell Univ.)
Zapata, Felipe (Missouri Botanical Gardens)
Zhao, Wei (CSHL)
Itoh, Takeshi (AFFRC, Japan)
Mishler, Brent (UC Berkley)
Nelson, Tim (Yale Univ.)
Sasaki, Takuji (AFFRC, Japan)
Ashburner, Michael (Cambridge Univ. UK)
Blake, Judith (Jackson Lab)
Bruskiewich, Richard (IRRI, Philippines)
Cherry, Mike (Stanford Univ.)
Harris, Midori (EBI, UK)
Lewis, Suzanna (LBL-UC Berkley)
Yamazaki, Yukiko (NIG-Japan)
McCouch, Susan R. (Cornell Univ. )
Buckler, Ed (Cornell Univ. )
Kellogg, Elizabeth A (Univ. of Missouri, St.
Louis)
Rhee, Seung Y. (TAIR, Stanford Univ.)
Stein, Lincoln (CSHL)
Ware, Doreen (CSHL, USDA-ARS)
Sane, Prafullachandra V. (NBRI, India)
(ii) Graduate and Postdoctoral Advisors: Silva Lerbs-Mache, Univ; Joseph Fourier, Susan McCouch,
Cornell; Naresh K Mehrotra, Lucknow Univ Lucknow, India; P Nath, NBRI, Sirish A Ranade, NBRI, India;
Aniruddha P Sane, NBRI, India; Prafullachandra V Sane, NBRI, India; Rudolf Schweyen, Vienna Biocenter,
Vienna; David Stern, Boyce Thompson Inst.
(iii) Thesis Advisor and Postgraduate-Scholar Sponsor: Justin Elser (Oregon State Univ.)
Biographical sketch, Pankaj Jaiswal
Page 2
Shaoying Lu
[email protected]
Professional Preparation:
Tsinghua University, China
Applied Mathematics
Tsinghua University, China
Applied Mathematics
University of California, San Diego Computational Mathematics
Appointments:
Research Assistant Professor
Postdoctoral Researcher
B.S., 1995
M.S., 1997
Ph.D., 2004
University of Illinois, Urbana-Champaign
University of California, San Diego
2005–present
2004-2005
Related Publications:
Shaoying Lu, Michailova A, Saucerman J, Cheng Y, Yu Z, Kaiser T, Li W, Bank RE, Holst M,
McCammon JA, Hayashi T, Hoshijima M, Arzberger P, McCulloch AD, Multi-scale modeling in rodent
ventricular myocytes: Contributions of structural and functional heterogeneities to excitationcontraction coupling. IEEE EMB (2009, in press).
Eichorst JP, Shaoying Lu*, Xu J, Wang Y, Differential RhoA dynamics in migratory and stationary cells
measured by FRET and automated image analysis. PLoS ONE (2009), 3(12):e4082 (*Equal
contribution author).
Shaoying Lu, Seong J, Ouyang M, Zhang J, Chien S, Wang Y, The Spatiotemporal Pattern of Src
Activation at Lipid Rafts Revealed by Diffusion-Corrected FRET Imaging, PLOS Computational
Biology (2008), 4(7):e1000127.
Bank RE and Shaoying Lu, A domain decomposition solver for a parallel adaptive meshing paradigm.
SIAM J. Sci. Comput. 26 (2004), no. 1, 105--127.
Significant Publications:
Seong J, Lu S, Ouyang M, Huang H, Zhang J, Frame MC, Wang Y, Visualization of Src activity at
different compartments of the plasma membrane by FRET imaging. Chemistry and Biology (2009),
16(1):48-57.
Kim TJ, Seong J, Ouyang M, Sun J, Lu S, Hong JP, Wang N, Wang Y, Substrate rigidity regulated Ca2+
oscillation via RhoA pathway in stem cells. J Cell Physiol, (2009), 218(2):285--93.
Ouyang M, Shaoying Lu, Li XY, Xu J, Seong J, Giepmans BN, Shyy JY, Weiss SJ, Wang Y. Visualization
of Polarized MT1-MMP activity in live cells by FRET Imaging. Journal of Biological Chemistry (2008),
283(25):177740-8.
Wang Y, Shaoying Lu, The Application of FRET Biosensors to Visualize Src Activation, Proceedings of
SPIE (2008), vol. 6868 (68680A):1-9.
Synergistic Activities:
Teaching Experience
• 03/99 - 06/01 Teaching Assistant, University of California, San Diego. Courses: Pre-Calculus,
Calculus, Linear Algebra, Introduction to Partial Differential Equations, Introduction to Applied
Statistics, Scientific Computing.
• 09/97 - 01/99 Graduate Student Instructor, University of Michigan, Ann Arbor. Courses:
PreCalculus, Calculus. Duties include independent teaching of the course for a small class of
thirty students, writing syllabus and exams.
• 09/95 - 07/97 Teaching Assistant, Tsinghua University. Courses: Numerical Analysis.
Invited Speeches
• Computational Analysis for Live-cell FRET Imaging, UIUC Digital Signal Processing Seminar,
2008.
• Scalable Parallel Algebraic Multgrid Methods, SIAM Conference on Parallel Processing for
Scientific Computing, San Francisco, CA, 2004.
Collaborators and other Affiliations:
(i) Collaborators:
Peter W. Arzberger (NPACI)
Thomas Brutnell (Boyce Thompson Inst.)
Richard Bruskiewich (IRRI)
Shu Chien (Univ. of California)
Margaret C. Frame (Univ. of Glasgow, UK)
Ben N. Giepmans (Groningen Univ., NL)
Martin Gent (Conn. Ag. Exp. Station)
Anushka Michailova (Univ. of California)
Andrew J. McCammon (Univ. of California)
Chris Myers (Cornell Univeristy)
Jeffrey J. Saucerman (Univ. of Virginia)
John Y. Shyy (Univ. of California)
Jing Zhang (Johns Hopkins University)
Yingxiao Wang (Univ. of Illinois)
Stephen J. Weiss (Univ. of Michigan)
Xinguang Zhu (MPI, Shanghai)
(ii)Graduate and Postdoctoral Advisors: Randolph E. Bank (Univ. of California), Michael J. Holst (Univ.
of California), Andrew D. McCulloch (Univ. of California).
Christopher R. Myers
[email protected]
Professional preparation:
Yale University
Cornell University
History
Physics
B.A., cum laude 1984
M.S. 1988, Ph.D. 1991
Appointments:
Senior Research Associate
Associate Director
Senior Scientist
Group Leader
Research Associate
NSF/CISE Research Associate
Postdoctoral Researcher
Cornell University
Cornell University
Beam Technologies, Inc.
Cornell University
Cornell University
Cornell University
UC-Santa Barbara
1998-present
2006-2007
1997
1994-1997
1993-1997
1993-1995
1991-1993
Related Publications:
R.N. Gutenkunst, J.J. Waterfall, F.P. Casey, K.S. Brown, C.R. Myers, and J.P. Sethna, “Universally
sloppy parameters sensitivities in systems biology biology is sloppy”, PLoS Computational Biology
3(10), e189 (2007).
B.C. Daniels, Y.-J. Chen, J.P. Sethna, R.N. Gutenkunst, and C.R. Myers, “Sloppiness, robustness, and
evolvability in systems biology”, Current Opinion in Biotechnology 19(4), 389-395 (2008).
A.O. Ferreira, C.R. Myers, J.S. Gordon, G.B. Martin, M. Vencato, A. Collmer, M.D. Wehling, J.R. Alfano,
G. Moreno-Hagelsieb, W.F. Lamboy, G. DeClerck, D.J. Schneider, and S.W. Cartinhour, "Wholegenome expression profiling defines the HrpL regulon of Pseudomonas syringae pv. tomato
DC3000, allows de novo reconstruction of the Hrp cis element, and identifies novel coregulated
genes", Mol. Plant-Microbe Int. 19(11), 1167-1179 (2006).
P.A. Bronstein, M.J. Filiatrault, C.R. Myers, M. Rutske, D.J. Schneider, and S.W. Cartinhour, "Global
transcriptional responses of Pseudomonas syringae DC3000 to changes in iron bioavailability in
vitro", BMC Microbiology 8:209 (2008).
B. Swingle, D. Thete, M. Moll, C.R. Myers, D.J. Schneider, and S.W. Cartinhour, "Characterization of the
PvdS-regulated promoter motif of Pseudomonas syringae pv. tomato DC3000 reveals regulon
members and insights regarding PvdS function in other pseudomonads", Molecular Microbiology 68
(4), 871-889, (2008).
Significant Publications:
C.R. Myers, “Satisfiability, sequence niches, and molecular codes in cellular signaling”, IET Systems
Biology 2(5), 304-312 (2008).
C.R. Myers, “Software systems as complex networks: structure, function, and evolvability of software
collaboration graphs”, Phys. Rev. E 68, 046116 (2003).
K.S. Brown, C.C. Hill, G.A. Calero, C.R. Myers, K.H. Lee, J.P. Sethna, and R.A. Cerione, “The statistical
mechanics of complex signaling networks: nerve growth factor signaling”, Physical Biology 1, 184-195
(2004).
J.J. Waterfall, F.P. Casey, R.N. Gutenkunst, K.S. Brown, C.R. Myers, P.W. Brouwer, V. Elser and J.P.
Sethna, "The sloppy model universality class and the Vandermonde matrix", Phys. Rev. Lett. 97,
150601 (2006).
C.R. Myers, R.N. Gutenkunst, and J.P. Sethna, “Python unleashed on systems biology”, Computing in
Science and Engineering 9(3), 34-37 (2007).
Synergistic Activities:
Scientific Service
1993 present Referee for several journals, reviewing papers in physics, biology, computer science, and
computational science
1990 present Developer and/or contributor to multiple open source scientific software packages
2008 present Member, External Advisory Board, Complexity Sciences Center, UC-Davis
2008 present Moderator, Molecular Networks section of q-bio e-print arxiv, www.arxiv.org/archive/q-bio
Teaching
2004 present Co-developer and co-teacher of interdisciplinary graduate computational laboratory course
on Computational Methods for Nonlinear Systems
Collaborations and other affiliations
(i)Collaborators:
James R. Alfano (Nebraska)
Philip A. Bronstein (USDA-ARS)
Piet Brouwer (Cornell)
Kevin S. Brown (Harvard)
Thomas Brutnell (BTI)
C.Robin Buell (TIGR)
Guillermo A. Calero (Cornell)
Samuel Cartinhour (USDA-ARS)
Fergal P. Casey (Univ. College, Dublin)
Richard A. Cerione (Cornell)
Alan Collmer (Cornell)
Jim P. Crutchfield (UC-Davis)
Raissa, M. D’Souza (UC-Davis)
Veit Elser (Cornell)
Ryan N. Gutenkunst (Cornell)
Melaine J. Filiatrault (USDA-ARS)
David Guttman (Toronto)
Christopher L. Henley (Cornell)
Colin C. Hill (GNS, Inc.)
Vinina Joardar (TIGR)
Kelvin H. Lee (Cornell)
Warren Lamboy (USDA-ARS)
Magdalen Lindeberg (Cornell)
John Mansfield (Imperial College, UK)
Gregory Martin (Cornell)
Gabriel Moreno-Hagelsieb (Wilfrid Laurier)
Tim Nelson (Yale)
Michael Rutske (Cornell)
Eric Sakk (Morgan State)
Lisa Schecter (Missouri-St. Louis)
Jakob Schiotz (DTU, Denmark)
David J. Schneider (USDA-ARS)
James P. Sethna (Cornell)
John Sheehy (IRRI)
John Stavrinides (Toronto)
Paul Stodghill (USDA-ARS)
Qi Sun(Cornell)
Bryan Swingle (USDA-ARS)
Yuhai Tu (IBM)
Robert Turgeon (Cornell)
Klaas van Wijk (Cornell)
Joshua J. Waterfall (Cornell)
Misty Wehling (Nebraska)
Mingming Wu (Cornell)
(ii)Graduate and Postdoctoral Advisors: Ph.D: James P. Sethna (Cornell); Postdoctoral: James S. Langer
(UC-Santa Barbara) (iii) Thesis Advisor: Postdocs: Siew Ann Cheong (Nanyang Tech. Univ.) [Four
postdoctoral scholars total.] Graduate students: co-supervised with James P. Sethna: Nicholas Bailey
(Ph.D in Physics, Cornell), Lance Eastgate (Ph.D. in Physics, Cornell), Joshua J. Waterfall (Ph.D. in
Physics, Cornell), Fergal P. Casey (Ph.D. in Applied Mathematics, Cornell), Ryan N. Gutenkunst (Ph.D. in
Physics, Cornell).
Xinguang Zhu
[email protected]
Professional Preparation:
Shandong Normal University, China
Institute of Botany, Chinese
Academy of Sciences, China.
University of Illinois - Urbana Champaign
Biology
B.Sc., 1996
Plant Physiology
Physiology and Molecular
Plant Biology
M.Sc., 1999
Appointments:
Junior Independent Group Leader,
Partner Institute of Computational Biology (MPG/CAS),
Research scientist, Department of Plant Biology, Institute of Genomic
Biology, University of Illinois at Urbana Champaign,
Postdoc research scientist, Plant Biology/UIUC
Ph.D., 2004
Aug. 2008-Current
Mar. 2006 – Current
Aug. 2004-Mar. 2006
Related Publications:
Zhu, X-G, Whitmarsh, J., Ort, D.R. and Long, S.P. (2004) Study of the effects of photoinhibition on
dynamics of carbon uptake using reverse ray-tracing algorithm. Journal of Experimental Botany 55:
1167-1175.
Zhu X-G, Govindjee, Baker, N.R., deSturler, E., and Long, S.P. (2005) Fluorescence in silico:
Simulations of chlorophyll a fluorescence induction kinetics of green plants via representation of the
complete sequence of excitation energy and electron transport events around photosystem II. Planta
223, 114-133
Long SP, Zhu X-G, Naidu SL, Ort DR (2006) Can improvement in photosynthesis increase crop yields?
Plant Cell and Environment 29: 315-330.
Zhu, X-G , deSturler, E. and Long, S.P. (2007) Optimized distribution of carbon metabolism enzymes can
dramatically increase photosynthetic CO2 uptake rate. - Theoretical explorations using a complete
model of photosynthetic carbon metabolism, Plant Physiology, 145: 513-526.
Zhu X-G, Long SP, Ort DR (2008) What is the maximum efficiency with which photosynthesis can convert
solar energy into biomass? Current Opinion in Biotechnology 19: 153-159.
Significant Publications:
Zhu, X-G, Portis, A.R. and Long, S.P. (2004) Would transformation of C3 crop plants with foreign
Rubisco increase productivity? A computational analysis extrapolating from kinetic properties to
canopy photosynthesis. Plant Cell and Environment 27, 155-165.
Wittig, W., Bernacchi, C.J., Zhu, X.-G., Gielen, B., Miglietta, F., Angelis, P.D., Ceulemans, R. and
Long, S.P. (2005) Modeling gross primary production for Populus spp grown under free-air CO2
enrichment. Global Change Biology, 11, 644-656.
Wang Z, Zhu X-G, Chen YZ, Yang J, Li YY, Li YX, Liu L (2006) Exploring photosynthesis evolution
by comparative analysis of metabolic network between chloroplast and cyanobacteria. BMC
Genomics 7: 100.
Wang SW and Zhu X-G. Coupling cyberinfrastructure and GIS to empower ecological and
environmental research. Bioscience (2008).
Ainsworth EA, et al, Zhu X-G, Curtis PS, Long SP (2002) A meta-analysis of elevated [CO2] effects
on soybean (Glycine max) physiology, growth and yield. Global Change Biology 8: 695-709.
Synergistic Activities:
Scientific Service
From 2003:
Reviewer for Plant, Cell & Environment, Tree Physiology, Global Change Biology,
Photosynthesis Research, Functional Plant Biology, Plant Physiology, New Phytologist,
Journal of Photochemistry and Photobiology; Climate Change
From 2009:
Editorial advisory board: GCB Bioenergy
Teaching Experience
September 2008 Invited speaker and workshop session leader, Dynamic models of photosynthetic
metabolisms – theory and lab. At Plant Systems Modeling Summer School, the
Flanders Interuniversity Institute for Biotechnology (VIB), Department of Plant Systems
Biology, Ghent, Belgium.
April 2008
Organizer, speaker, and session leader, Photosynthesis systems biology research –
theory and lab. At Workshop for Bioenergy related Plant Systems Biology Research,
Instiute of Computational Biology, Chinese Acadamy of Sciences and Max-Planck
Institue, Shanghai, China.
Collaborators & Other Affiliations:
(i) Collaborators:
Baker, Neil R. (Essex Univ., UK)
Bernacchi, Carl J. (Water Survey, IL)
Cornic, Gabriel. (Uni. Paris)
DeLucia, Evan H. (Univ. Illinois)
de Sturler, Eric (Virginia Tech)
Govindjee (Univ. Illinois)
Kramer, David (Washington State Univ.)
Li, YiXue (Shanghai Center for Bioinformatics Tech)
Liu, Lei (Univ. Illinois)
Long, Stephen P. (Univ. Illinois)
Mott, Keith (Utah State Univ.)
Ort, Donald R. (Univ. Illinois)
Portis, Archie R. (Univ. Illinois)
Rogers, Alistair (Brookhaven National Laboratory, NY)
Sheng, Zhong (Univ. Illinois)
Wang, Shaowen (Univ. Illinois)
(ii) Graduate and Postdoctoral Advisors: Long, S. P. (Univ. Illinois), Zhang, Q.D. (Institute of Botany,
Chinese Academy of Sciences, Retired).(iii) Thesis Advisor, Postdoc (4) Dr. Daniel Tholen (Utrecht
University); Dr. FuQiao Xu (Shanghai Institute of Biological Sciences); Dr. GuiLian Zhang (East China
Normal University); Dr. Vincent Devloo (University livre de Bruxelles) Graduate (5) Zhuo Wang, Yu Wang,
Lisha Zhu, Xiao Chang, HongBo Li