Am J Physiol Cell Physiol 292: C164 –C177, 2007.
First published August 2, 2006; doi:10.1152/ajpcell.00193.2006.
Building the power house: recent advances in mitochondrial
studies through proteomics and systems biology
Thuy D. Vo and Bernhard O. Palsson
Department of Bioengineering, University of California, San Diego, La Jolla, California
Submitted 19 April 2006; accepted in final form 28 July 2006
ADVANCES IN BIOINFORMATICS and high-throughput technologies
have rapidly expanded the availability of information at all
levels of biological investigation. This information has greatly
enhanced our knowledge of the molecular makeup, localization, and interactions among cellular components. The need for
organizing and understanding these parts catalogs has led to the
emergence of numerous biological databases and has also
given birth to the field of systems biology. The launching of
systems biology as a discipline represents a collaborative effort
among scientists of different research areas to unite biological
data, physical principles, and mathematical tools to unravel the
complexity of living systems. Successes in this field depend on
the interplay of four processes: 1) identifying key biological
components, 2) reconstructing networks of interactions among
these components, 3) quantitatively analyzing these networks,
and 4) generating testable hypotheses for model validation and
further experimental investigations. Together, these four steps
form a cycle (Fig. 1) in which every iteration provides better
understanding of the biological system than the sum of results
individually collected from each process.
Component identification is an ongoing process in biological
study and has accumulated data at many different levels of
detail and scale. Early studies painstakingly identified and
characterized enzymes and enzymatic activity individually (57,
115), whereas more recent studies have aimed at the organelle
and cellular levels. Although high-throughput technologies
have not been able to achieve the complete biochemical characterization of individual enzymes, they are excellent tools for
identification of unknown components for further investigation. Large data sets of mitochondrial genomics and proteomics are currently available, and metabolomics are likely to
appear on the horizon. Furthermore, current advances of proteomic techniques do not limit their use to component cataloging; they are also utilized in interaction discovery and functional-state characterization. For example, techniques discussed in this review, such as immunocapturing, can detect the
assembly and interaction among proteins of each respiratory
chain complex (RCC). Mass spectrometry-based technologies, including liquid chromatography (LC) followed by
tandem mass spectrometry (LC-MS/MS), matrix-assisted
laser desorption ionization-mass spectrometry (MALDIMS), and surface-enhanced laser desorption ionizationtime-of-flight mass spectrometry (SELDI-TOF MS), can
reveal functional states of the cell based on identities of
uncovered proteins. Knowledge about such interactions and
functional states paves the way to better understanding of
cellular physiology. The innovative high-throughput techniques, in some sense, can be considered disruptive technologies, as they force scientists to transform molecular
biology to a systemic and quantitative discipline (65). As
shown in Fig. 1, component identification is just a first step;
for these “omics” data sets to provide tangible progress,
methods must be established to analyze and interpret them,
thereby completing the systems biology cycle. The use of
computational tools is no doubt essential for the integration
and analysis of omics and legacy data. Here we review
successes and challenges resulting from recent experimental
and computational investigations of the mitochondrion.
First, we discuss why it is possible and useful to consider
the mitochondrion a system. Second, we review results from
proteomic studies, the most popular mitochondrial omics
data type, in studying complex diseases associated with this
Address for reprint requests and other correspondence: B. Palsson, Dept. of
Bioengineering, Univ. of California, San Diego, MC 0412, La Jolla, CA 92093
(e-mail: [email protected]).
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
constraint-based modeling; kinetics-based modeling; data integration;
standards; bioinformatics
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Vo TD, Palsson BO. Building the power house: recent advances in
mitochondrial studies through proteomics and systems biology. Am J
Physiol Cell Physiol 292: C164 –C177, 2007. First published August
2, 2006; doi:10.1152/ajpcell.00193.2006.—The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems.
Systemic studies of mitochondria have generated a large number of
proteomic data sets in numerous species, including yeast, plant,
mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and
characterizing complex diseases associated with these organelles.
Various proteomic techniques for isolation and purification of proteins
have been developed; each tailored to preserve protein properties
relevant to study of a particular disease type. Examples of such
techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser
desorption ionization-time-of-flight mass spectrometry, which allows
sequence-specific binding. With the rapidly increasing number of
discovered molecular components, computational models are also
being developed to facilitate the organization and analysis of such
data. Computational models of mitochondria have been accomplished
with top-down and bottom-up approaches and have been steadily
improved in size and scope. Results from top-down methods tend to
be more qualitative but are unbiased by prior knowledge about the
system. Bottom-up methods often require the incorporation of a large
amount of existing data but provide more rigorous and quantitative
information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here
provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.
RECENT ADVANCES IN MITOCHONDRIAL STUDIES
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organelle. Finally, we summarize results from computational studies that integrate high-throughput and legacy data
to improve the understanding of mitochondrial biology and
mitochondrial diseases.
MITOCHONDRION AS A SYSTEM
Mitochondria are semiautonomous organelles. Due to their
endosymbiotic origin (80, 81), the organelles retain their genome,
bilayer membrane, transcriptional and translational capabilities,
high metabolic and signaling activities, and division ability independent from the cell (34). The protein make up of these organelles contains components at all levels of the central dogma of
molecular biology. For these reasons, mitochondria are natural
candidates for systemic studies. With the mitochondrial genome
encoding only a small fraction of mitochondrial proteins, proteomic technology becomes key in the identification of components and functions of this organelle. In addition, because of its
manageable number of proteins, the mitochondrial proteome is an
attractive target for initial application of new proteomic techniques. Furthermore, as components of the mitochondrial proteome encompass a wide range of hydrophobicity, molecular
weight, isoelectric values, and copy numbers, this proteome
serves as an ideal benchmark (85). To date, mitochondrial proteomes of many species have been reported, including human (36,
105, 121), rat (75), mouse (26), fruit fly (3), yeast (98), Neurospora crassa (111), rice (43), Arabidopsis thaliana (16), pea (120),
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and soybean (45). Successes in comprehensiveness and technology advancement in these studies have been reviewed extensively
(25, 27, 37, 106); here we focus on the application and analysis of
proteomic data in elucidating mitochondrial functions and mitochondrion-associated diseases.
MITOCHONDRIAL PROTEOME AS A DIAGNOSTIC TOOL FOR
MITOCHONDRIAL DISEASES
Mitochondria are present in almost all human cell types,
with a few exceptions such as red blood cells. Mitochondrial
proteomes from various human cells, including cardiomyocytes (36, 121), hepatocytes (82), and T leukemia cells (105),
have been identified. The organelle once known only as the
“power house” of the cell now emerges as a target for treating
a variety of human disorders (131, 132). Although mitochondrial defects are implicated in a wide range of diseases, the
more widely studied defects can be loosely classified into three
categories: respiratory chain defects, neurodegenerative diseases, and cancer. This classification is used to provide a rough
structure to our discussion and is not meant to be mutually
exclusive in the underlying disease etiology.
Respiratory Chain Defects: Disease Diagnosis and
Characterization by Immunologic Assays
Defects in proteins of the respiratory chain lead to a multitude of mitochondrial disorders, of which the most prominent
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Fig. 1. Systems biology cycle. Systems biology can be defined to be the quantitative study of biological processes as whole systems, instead of isolated parts.
The field is characterized by synergistic integration of data and theory that can be combined to produce a model. Analysis of the model provides predictions about
physiological functions with measurements that are difficult or expensive to obtain. Validation of these predictions helps identify novel components, which, in
turn, refine the model.
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logic assay also revealed phenotypes for mutations that had
previously only been genetically defined. In particular, the
mutation T423M of complex I was found to lead to a 25%
reduction of catalytic function rather than a misassembly of the
complex.
Complexes determined to be deficient in a particular patient
by immunologic tests can be subjected to further enzymatic
activity quantification. Using spectrophotometry, Kramer et al.
(62) developed an automated assay for measuring concentration and enzyme activity of a full panel of respiratory chain
enzymes. Enzyme activity was measured via absorbance
change produced by a specific electron acceptor or donor in
each complex. This assay was recommended for use in conjunction with Western blot analysis, as the spectrophotometric
method can detect RCC deficiency in a protein not probed, or
alterations in post-translational modification, whereas Western
blot shows the physical amount of protein subunits. In particular, Western blot analysis explained the RCC deficiency in a
patient who tested normal by the enzymatic activity assay. This
combined spectrophotometry-Western blot assay provides
rapid, precise results from a crude tissue sample without the
need for isolation of mitochondria from tissue homogenate.
The authors reported a 91% agreement with the established
diagnostic method through a blinded comparison of 10 patients
with known diagnoses and 1 healthy control. The applicability
of spectrophotometry for an enzymatic assay, however, ultimately depends on the suitability of absorbance spectra of the
enzymes.
Neurodegenerative Diseases: Oxidative Damage Identified as
the Common Cause
Dysfunction in the electron transport chain and the consequent oxidative damage are also associated with many neurodegenerative diseases, including Alzheimer’s disease (AD),
Parkinson’s disease (PD), and amyotrophic lateral sclerosis
(ALS) (18). However, as the etiologies of these diseases are
still poorly characterized, studies of these disorders tend to be
exploratory, rather than focusing on the mechanism and extent
of involvement of the respiratory chains on disease pathogenesis and progression. Besides sequencing efforts to discover
associated genetic mutations, proteomic techniques have been
applied to identify new and differentially expressed proteins in
cell line models of these diseases. In particular, isotope-code
affinity tags (ICAT) and LC-MS/MS have been employed to
analyze mitochondrial proteins isolated from primary neuron
cultures (76), dissected substantia nigra from Parkinsonian
mice (52), and a mouse embryonic spinal cord-neuroblastoma
hybrid cell line (33). Here we briefly describe methods and
results obtained from these three studies.
ICAT enables quantitative proteomic analysis of two different pools of proteins based on differential isotopic tagging of
each pool (39, 41, 116). The ICAT reagent (Applied Biosystems, Foster City, CA) labels proteins by specifically reacting
with thiol groups on cysteine residues. Each sample pool is
treated with an isotopically different ICAT reagent, which is
either light, through the use of 1H or 12C linker, or heavy,
through the use of 2H or 13C linker. The combined sample is
proteolytically digested with trypsin. Avidin affinity chromatography is used to capture and isolate peptides containing
labeled cysteines, and the resulting fractions are analyzed with
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are Leigh’s syndrome (complex I–IV), MELAS (complex I),
and Leber’s hereditary optic neuropathy (LHON; complexes I,
III, and V). Given the high number of subunits in each
complex, it is not surprising that disorders involving the
respiratory chain are reported to be more commonly a result of
altered assembly than catalytic site dysfunction (42, 70).
Therefore, methods for diagnosis and evaluation of such diseases should be capable of detecting misassembly, as well as
reduced activity due to catalytic site mutations. In contrast to
large-scale proteomic studies seeking to identify the entire
proteome of the mitochondrion, monoclonal antibody-based
focused proteomics (85) aims to identify specific proteins of
interest with the maximal protein integrity. Instead of twodimensional electrophoresis, which denatures proteins, monoclonal antibodies capture the protein complexes while preserving post-translational modifications and minimizing protein
degradation (110). Immunocapture of complexes is also simpler, less expensive, and requires less sample than enzymatic
assays. An obvious drawback, however, is that immunocapture
can only be done for proteins for which antibodies are obtainable. A list of 35 commercially available monoclonal antibodies (MitoSciences, Eugene, OR) to mitochondrial proteins and
their applications has been published (17). This immunologic
approach can be applied at various stages of disease characterization, including detection of affected complexes (17, 70,
71), profiling of assembly patterns (84, 124), and measurement
of complex activity (62). The following three studies are
examples at each of these three stages.
The five RCC have been obtained by immunocapture and
resolved by SDS-PAGE (85). Hanson et al. (42) proposed
using this combined method to quickly diagnose affected
complexes of the different respiratory chain defects. In particular, the cells were simultaneously probed with monoclonal
antibodies against the RCC (distinguished by green fluorescent
labels) and against porin as a control (distinguished by red
fluorescent labels). In normal samples, equal expression of the
RCC and porin, i.e., equal signals from the green and red
fluorescent signals, makes the cells appear yellow. However,
when a complex is low or missing, the dominant signal from
porin antibodies gives the cell a red-orange color. Affected
proteins, therefore, can be identified simply by viewing the
sample with fluorescence microscopy. If antibodies recognizing proteins in the denatured state are also available, Western
blots, where specific detection of the antibody is obtained with
chemiluminescent substrates, can serve as an alternative (17).
In addition, Western blotting is more amenable to protein
quantification and allows one to confirm the molecular weights
of target proteins.
Triepels et al. (124) examined 11 different patients whose
complex I deficiency had been found with enzyme activity
measurements. Mitochondria were isolated from fibroblasts of
each patient and treated with a mixture of six different antibodies. Western blot signals, normalized to that of control
fibroblasts, confirmed that the levels of complex I were diminished in most of the patient cell lines compared with the
control. The steady-state levels of fully assembled complex I
were also found to depend on expression levels of all the
subunits being examined. When any subunit was mutated, the
levels of the assembled complexes were reduced. The study
also suggested which of the patients most likely had a mutated
assembly factor. Besides diagnostic purposes, this immuno-
RECENT ADVANCES IN MITOCHONDRIAL STUDIES
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(64). The authors identified 318 proteins in mitochondrionenriched fractions isolated from mouse substantia nigra. Of
these proteins, ⬎100 exhibited substantial differences in relative abundance between MPTP-treated and control mice. This
is a much higher number of proteins compared with the 45
proteins identified by Lovell et al. (76) (by the same method),
because these authors combined results from triplicate experiments using pooled samples of 15 MPTP-treated mice. The
authors argued that combining results from multiple experiments was necessary, as current LC-MS/MS technology
achieves only 30% reproducibility with moderately complex,
identical samples. Besides complex I, which has been shown to
be inhibited by MPTP (114), the levels of several subunits of
complexes III and V were also shown to decrease after MPTP
treatment. This study concludes that MPTP-induced mitochondrial dysfunction leads to an increase in ROS production and a
decrease in ATP production.
ALS is a fatal neurodegenerative disease characterized by
progressive loss of voluntary motor neurons. Similar to PD, the
majority of ALS patients (90%) have the sporadic form of the
disease, while only 10% have the familial form (fALS). Approximately 20% of fALS are caused by mutations in superoxide dismutase SOD1, an enzyme that normally removes
superoxide radicals by converting them to molecular oxygen to
mitigate oxidative damage to the cell. Studies have shown that
clinical progression and pathological alteration of motor neurons in sporadic ALS are very similar to those from both fALS
patients carrying SOD1 mutations and transgenic mice expressing mutant G93A-SOD1 (33, 38). It is further thought that
fALS caused by SOD1 mutations is a result of a toxic gain of
function, rather than SOD1 deficiency, because some of these
mutations do not seem to affect normal SOD1 activity and
SOD1-null mice do not develop the disease (79). Fukada et al.
(33) used two independent approaches, two-dimensional gel
electrophoresis followed by MALDI-MS/MS and SDS-PAGE
followed by LC-MS/MS, to characterize changes in the mitochondrial proteome in the presence of the mutant G93A-SOD1.
The chosen model system was a mouse embryonic spinal
cord-neuroblastoma hybrid cell line (NSC34) exhibiting motor
neuron phenotype. The authors searched for mitochondrial
proteins that were altered by G93A-SOD1 under the hypothesis
that mitochondrial dysfunction and apoptosis activation play a
role in motor neuron death in fALS. Combining results from
both approaches, 470 unique proteins were identified in the
mitochondrial fraction, of which 75 were newly discovered
proteins that had been reported previously only at the cDNA
level. Two-dimensional gel electrophoresis identified 40 proteins displaying differential expression, 24 of which were
results of different post-translational modifications caused by
the G93A-SOD1 mutation. In particular, the authors found five
distinct isoforms of VDAC2 and decreased abundance of the
channel in G93A-SOD1 cells compared with wild-type cells.
Modifications of VDAC2 and apoptotic activation were suggested to be a result of SOD1-mediated toxicity, although
possible mechanisms were not discussed. Five chaperones and
heat shock proteins were also found to have decreased abundance in G93A-SOD1 cells. The authors suggested that the
reduced activity of these proteins somehow facilitated the entry
of mutant SOD1 into the mitochondria, thereby inducing mitochondrial abnormality. Wild-type SOD1 was not found in
mitochondrial matrix (74, 92). Intriguingly, although fALS has
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LC-MS/MS. LC-MS separates and determines the differential
expression of each tagged peptide pair from the signal intensity
ratios of isotopically light- and heavy-labeled forms. Peptides
exhibiting significant differential expression can be further
sequenced with the tandem MS. ICAT is particularly useful for
samples ⬍100 ␮g (76) and allows for simultaneous quantification and identification of proteins in one analysis. The
method, however, is applicable only to cysteine-containing
proteins, which make up about 30% of the total proteome.
AD is the most common neurodegenerative disease, affecting ⬃10% of Americans ⬎65 yr old and 50% of those ⬎85 yr
old (30). Causes for the late-onset, sporadic AD are largely
unknown. Numerous causal connections, however, have linked
mitochondrial dysfunction with sporadic AD: somatic mitochondrial DNA (mtDNA) mutations, generation of reactive
oxygen species (ROS), cytochrome c oxidase deficiency, misregulation of redox-active heavy metals (Fe and Cu), and
impaired energy metabolism resulting from these causes (19,
102). Studies of AD have often centered on amyloid-␤ peptide,
a protein frequently found in brains of patients with AD. To
investigate the peptide’s functional effects on neurotoxicity,
Lovell et al. (76) applied the ICAT labeling technique and
LC-MS/MS to quantitatively evaluate changes in mitochondrial proteins of primary neuron cultures exposed to amyloid-␤. ICAT analysis of mitochondria from control and amyloid-␤-treated cultures identified 45 nonredundant mitochondrion-specific proteins. Ten proteins showed significantly
altered expression (P ⬍ 0.05), and nine demonstrated a trend
toward significance (P ⬍ 0.09) in amyloid-␤-treated cultures.
Among the enzymes with increased expression were those
playing essential roles in energy metabolism, such as pyruvate
kinase, glyceraldehyde 3-phosphate dehydrogenase, and creatine kinase. Such increased expression was a possible compensatory response of amyloid-␤-treated cells to the declining
mitochondrial membrane potentials induced by oxidative
stress. Other proteins exhibiting elevated levels were guanine
nucleotide-binding protein-␤ (modulator in signaling pathways), cofilin (control of polymerization/depolymerization),
Na⫹/K⫹-transporting ATPase (ion balance), voltage-dependent ion channels (VDAC1 and VDAC3), cyclophilin A (interacting with apoptosis-inducing factors), dihydropyrimidinase (axonal outgrowth), and 60S ribosomal protein L4 (protein synthesis). Although the authors could not explain the
statistically significant decrease of ATP synthase ␥-chain, they
concluded that the increased production of the other proteins in
primary cortical neurons undergoing apoptosis was perhaps the
cell’s effort to maintain ATP production.
PD is the second most common neurodegenerative disorder
after AD (52). Pathological characteristics of PD include the
progressive and selective loss of nigrostriatal dopaminergic
neurons and deposits of the protein ␣-synuclein called Lewy
bodies. The mechanisms underlying PD development and
Lewy body formation are not fully characterized, although
increasing evidence suggests that mitochondrial dysfunction,
oxidative damage, excitotoxicity, and inflammation are contributing factors. Jin et al. (52) employed ICAT in combination
with LC-MS/MS to study mitochondrion-enriched fractions
isolated from the substantia nigra in mice treated chronically
with N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP).
The compound MPTP produces pathological and biochemical
changes similar to the well-established changes in PD patients
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never been regarded as a metabolic disease, half of the differentially expressed proteins were those involved in various
metabolic pathways, including subunits of complexes I, II, IV,
and V. Although the authors did not speculate about the
involvement of these metabolic enzymes, it is possible that as
the motor neurons deteriorate, the cell reduces its metabolic
demand, thereby reducing the requirement for these proteins.
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Mitochondrial Proteomics
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BUILDING THE POWER HOUSE: FROM PROTEOMIC TO
SYSTEMS BIOLOGY
The unifying theme across the proteomic studies described
here is the search for a physiological context, mostly in terms
of protein expression levels, of the cellular response to a
particular disorder or disturbance. This theme reflects the
transition of biology from component identification to functional state characterization. In fact, though it is useful to know
which mutation causes a particular disease, it is much more
beneficial, in terms of therapeutic development, to understand
downstream effects of the mutated enzyme’s actions. For
example, there are approximately 100 SOD1 mutations attributable to fALS (79), but a cure for the disease awaits discoveries about consequences of the mutations on the cellular and
organism systems as a whole. In understanding cellular physiology, it is necessary to conduct experiments and analysis on
the cellular scale. Proteomic studies, as well as genomic and
microarray experiments, have begun to meet the need for
cellular-scale investigation. Cellular-scale analysis can be broken into two components: data integration and quantitative
analysis. These two components, together with experimental
investigations, complete the cycle that defines systems biology.
Data Integration: Databases and Network Reconstruction
Numerous databases have been created to centralize, organize, and facilitate the accessibility of various mitochondrial
omic data sets (Table 1). Of these databases, MitoP2 (5, 97) is
perhaps the most comprehensive in providing data relevant to
mitochondrial proteomic studies. Released in 2004, MitoP2
replaced an earlier version named MITOP. The database combines information regarding the genetic, functional, and pathogenetic aspects of nuclear-encoded mitochondrial proteins. The
current version contains data for human, mouse, yeast, N.
crassa, and A. thaliana. Besides data for proteins known to be
mitochondrial, referred to as the reference set by the authors,
MitoP2 also provides information about putative mitochondrial
proteins identified by homology search tools. Each protein
entry is annotated with function, chromosomal localization,
subcellular localization, homologs and associated confidence
values, gene ontology (GO) number, applicable Online Mendelian Inheritance in Man (OMIM; 40), literature references,
and cross-references to external databases. Users can search for
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Genetic alterations in the mitochondrial genome (mtDNA)
have been suggested to be the cause of numerous types of
cancer (51, 54). In fact, the frequency of mtDNA mutations in
cancer cells was reported to be 10 times higher than that of
nuclear DNA mutations (127). The involvement of mitochondria in energy metabolism, apoptosis, and cellular oxidative
stress responses further implicates the organelle’s role in the
development of malignant cells. Mitochondrial proteomes naturally become an attractive target for biomarker identification
and monitoring disease progression.
Using the lipophilic cation 4-iodobutyltriphenylphosphonium (IBTP), Lin et al. (72) developed a method to chemically
label and measure redox changes in mitochondrial thiols.
Driven by the large membrane potential across the inner
mitochondrial membrane, IBTP compounds accumulate in the
mitochondrial matrix, where they oxidize exposed thiol groups
to form a stable thioester. Conversely, thiols already oxidized
as a result of cellular oxidative stress are unable to react with
IBTP, thereby reducing the amount of label observed. Using
this technique, the group showed that free thiol groups on
complexes I, II, and IV were vulnerable to oxidative stresses
caused by S-nitrosothiol, peroxynitrite, or direct glutathione
oxidation. The technique, therefore, is well suited for quantifying and characterizing oxidative damage associated with
many cancer types.
MS is frequently applied in many proteomic studies, including those aiming at mitochondria. A typical mass spectrometer
contains three main components: ionization source, analyzer,
and detector. Two soft ionization techniques primarily used for
large biomolecules such as peptides are electrospray ionization
(ESI) and MALDI. In ESI, the sample is dispersed through a
capillary device at high voltage, and ionized peptides are
separated according to their mass-to-charge ratios in an electric
field. In MALDI, the sample is embedded in a UV-absorbing
chemical matrix, which facilitates the ionization and vaporization of the peptides. The charged peptides are also accelerated
in an electric field and then enter a drift tube (time-of-flight
analyzer), where molecules are separated and reach the detector at a time determined by their mass-to-charge ratio. Lee et al.
(66) and Yim et al. (138) applied this method to study the
anticancer mechanisms of Taxol and 5-fluorouracil, respectively, in cervical carcinoma cells. In both studies, they found
that the drugs elevated proteins involved in apoptosis and
antiproliferation. In particular, Taxol increased expression of
bcl-2, bax, bcl-X, p21-waf, and TNF-␣, while 5-fluorouracil
upregulated expression of Apo-1, caspase-3, and caspase-8.
Similarly increased expression of mitochondrial apoptotic proteins (Apaf-1, caspase-9, cytochrome c oxidase subunit II)
were also found in colorectal adenocarcinomas obtained from
patients (82).
More recently, SELDI-TOF, an extension of MALDI-TOF
developed by Ciphergen Biosystems (93), has become a powerful tool for rapid identification of cancer-specific biomarkers
and proteomic signatures. In the SELDI method, patient samples (tissues or body fluids) are allowed to sequence-specifically bind to the surface of a protein-binding chip, which is
subsequently treated with a matrix compound and irradiated
with a laser. Relative ion intensities can be computationally
analyzed and classified. In the few years since its development,
there have been ⬎100 studies applying the technology to a
wide variety of cancers, including leukemia (2) and ovarian
(73, 139), colorectal (137), breast (107), gastric (99), and
prostate (90) cancer. Clinical applications of this technology
have been reviewed extensively (93, 135). Because this technology can be applied to raw samples, we are not aware of its
use for isolated mitochondria.
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Table 1. Databases dedicated to mitochondria
Database
Website
Content
mtDB (49)
GOBASE (113)
www.genpat.uu.se/mtDB
megasun.bch.umontreal.ca/gobase/
Mitomap (15)
MitoProteome (22)
MitoDat (67)
www.mitomap.org
www.mitoproteome.org
www.ecb.ncifcrf.gov/mitoDat
Human Mitochondrial Protein Database
bioinfo.nist.gov:8080/examples/servlets
MitoP2 (5, 97)
ihg.gsf.de/mitop2
URLs of each database are provided, however, due to the fluidity nature of domain names, the address can be quickly outdated, and it is advisable that users
search for the databases using search engines such as Google.
proteins by species, functional category, disease, homology,
subcellular localization, or scores from homology calculations.
Network abstractions can be developed to summarize available data and interaction among known components. Networks
of proteins and metabolites are the most common and can now
be reconstructed at the genome scale (28, 104). Reconstructed
networks serve as a highly organized repository for results of
a large number of experiments, from which testable hypotheses
can be drawn (Fig. 2). Interrogating the properties of these
networks allows one to evaluate their accuracy and functions.
In particular, the biochemical pathways describing the metab-
olism of a mitochondrion have been constructed into a network
that precisely preserves the interconnectivity of genes, proteins, and metabolites in this organelle (128). This reconstruction integrated data from the mitochondrial genome (4), the
heart mitochondrial proteome (87, 121), and the annotated
human genome (63, 126), together with a large volume of
literature on mitochondrial metabolism. The resulting network,
with 189 biochemical reactions and 230 metabolites, described
energy metabolism, ROS detoxification, and heme synthesis,
as well as nitrogen and lipid metabolism. Every metabolite and
reaction in the network was localized to one of three cellular
Fig. 2. Network reconstruction as applied to the glycolytic pathway. A reconstruction of a biochemical pathway starts with identification of enzymes, metabolites,
and directionality of each reaction. Left: list of reactions participating in the glycolytic pathway. Each reaction is labeled with an abbreviation of the corresponding
enzyme name. Each reaction is written to balance elements and charges of reactants on both sides. Net reaction shows a gain of 2 protons, which can possibly
contribute to the proton gradient across the mitochondrial membrane. Right: mathematical representation (S matrix) of the list of reactions. Each element Sij
denotes participation of metabolite i in reaction j. Reconstruction also directly integrates associations of locus, gene, protein, and reaction for each enzyme when
possible. Examples are shown for the enzyme phosphofructose kinase (PFK). PFK consists of 4 subunits and has tissue variations (muscle, liver, platelets) as
well as transcript variations (locus 5211 has 2 alternative splices, 5211.1 and 5211.2). Muscle and liver isoforms are homotetramers of the M (PFKM) and L
(PFKL) subunits, respectively; platelets contain various heterotetramer combinations such as 3 PFKL and 1 PFKM or 2 PFKL and 2 PFKM (29).
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mtDNA sequences and polymorphic sites
Genetic maps of completely sequenced mitochondrial genomes
and RNA secondary structures of mitochondria-encoded
molecules
Polymorphisms and mutations of human mtDNA
Fully annotated human mitochondrial sequences
Enzymes and genes involved in mitochondrial biogenesis and
function
Comprehensive data on mitochondrial and human nuclear
encoded proteins involved in mitochondrial biogenesis and
function
Comprehensive list of mitochondrial proteins of several
species including human. Links and search tools for datasets
relevant to study of mitochondrial proteomes
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RECENT ADVANCES IN MITOCHONDRIAL STUDIES
compartments: mitochondrial, cytosolic, or extracellular. Metabolites were also characterized by molecular formulas and
predominant charge forms determined at pH 7.2. Such procedures for manually reconstructing and curating metabolic networks, i.e., using a bottom-up approach (44, 89), have been
detailed in the literature (103). This integration serves as a
curated database that can assist in the evaluation of genomic
annotation and protein expression studies (88).
The top-down network reconstruction approach, i.e., based
on high thoroughput data, has also been applied to mitochondrial proteomic data sets. In one of the most comprehensive
mitochondrial proteomic studies, Mootha et al. (83) identified
591 mitochondrial proteins using mitochondria samples from
mouse brain, heart, kidney, and liver. Combining this data set
with mRNA expression, the authors hierarchically clustered
386 genes into subnetwork modules. Each of these gene
modules was characterized by tightly correlated gene expression across the four tissues, suggesting shared transcriptional
regulatory mechanisms as well as cellular functions among
module members. A number of these modules had clear functional associations, such as oxidative phosphorylation,
branched-chain amino acid metabolism, steroidogenesis, and
heme biosynthesis. The modules were further extended to
mitochondrial neighborhoods, which grouped genes that were
functionally coordinated with mitochondrial processes. These
modules represented a first step toward a systematic, functional
AJP-Cell Physiol • VOL
characterization of mitochondrial and mitochondrion-associated genes. Advantages of this top-down network reconstruction are that it is relatively quick, automated, and not subject to
bias from prior information about the system. One disadvantage, however, is that the method is solely dependent on the
often noisy high-throughput data sets (112) and the parameters
used for clustering.
Quantitative Analysis: Model Validation and Functional
State Determination
A large number of mitochondrial models describing a wide
range of mitochondrial functions, including energy transduction (10 –12, 50, 61, 69, 100), Ca2⫹ signaling (31, 86, 134), and
respiratory regulation (59, 60, 133), can be found in the
literature. The increasing accessibility of computational power
and availability of data have made the building of larger and
more comprehensive models possible. Analysis of model predictions, either validation or falsification, drives the iterative
model-building process toward a fuller and more accurate
representation of the corresponding biological system. The
following are results from some of the recent models of
mitochondria.
Kinetics-based modeling. A biological process can be quantitatively described by relating the reaction rate to the change
in concentrations of involved metabolites (Fig. 3). The reaction
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Fig. 3. Constraint- and kinetics-based representations for 4 reactions in the tricarboxylic acid cycle. Constraint-based approach requires a theoretical boundary
(dashed line) to distinguish the inside and outside of the system. The S matrix is written for the 4 internal reactions and 3 exchange reactions (EX1, EX2, EX3),
which allow metabolites (accoa, co2, and nadh) to cross the boundary. Flux balance analysis is applied to find a flux distribution v that maximizes output of nadh.
Mass-balance constraint is represented by d[X]/dt, where [X] is a vector of metabolite concentrations. Time derivative d[X]/dt is equivalent to the 4 differential
equations shown for the kinetics-based model but accounts for all 12, instead of just 4, metabolites. Kinetics-based descriptions for CS and ACO are shown as
examples; similar expressions are required for the rest of the enzymes in the model and can be found in Cortassa et al. (20). There are 2 notable differences in
the 2 approaches: 1) The relation between substrate concentrations and reactions is linear in the constraint-based approach but is highly nonlinear in the kinetic
counterpart. 2) The requirement of the large number of kinetic parameters is eliminated in the constraint-based model. Consequently, a kinetic model is useful
when enzyme mechanisms are known and immediate responses of the system to perturbations are of interest. On the other hand, because of the much simpler
mathematics involved, constraint-based models can be applied to large-scale networks and are useful when steady-state responses are relevant.
RECENT ADVANCES IN MITOCHONDRIAL STUDIES
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cally, the authors evaluated effects of substrate availability
(through the acetyl-CoA level), phosphorylation potential, proton leak, and respiratory inhibition on mitochondrial energetics. A twofold increase in respiration was found when acetylCoA varied from 0 to 10 ␮M. Saturation of oxygen consumption was reached at 5 ␮M acetyl-CoA. A steep increase in
respiration occurred in response to small changes in ADP
concentration, indicating a shift from state 4 to state 3 respiration. Inhibition of the respiratory chain, simulated by decreasing the available electron carriers, led to a decrease in
membrane potential accompanied by a concomitant decrease in
oxygen consumption. The study also addressed the hypothesis
that Ca2⫹ influx acts as a control factor for increasing ATP
production. Two opposing effects of Ca2⫹ were discussed: an
increase in mitochondrial Ca2⫹ stimulated TCA dehydrogenase activities, but the influx of cations into the matrix decreased the inner mitochondrial membrane potential. As a
result, positive responses in terms of oxygen consumption,
proton gradient, and ATP production to increased Ca2⫹ were
observed only when the increase of proton pumping by activated dehydrogenases exceeded the depolarization due to Ca2⫹
entry. In this respect, the model disagrees with observations
from experiments with isolated porcine heart mitochondria
(122) that high Ca2⫹ concentration continued to produce a
positive stimulation of Ca2⫹ on oxygen consumption and ATP
synthase activity. The authors suggested that a further incorporation of Ca2⫹ in the ATPase kinetic expression would
eliminate this inconsistency. This suggestion forms an interesting hypothesis of Ca2⫹ influence on F1F0-ATPase activity,
which can be verified with theoretical and experimental investigation. Particularly, it will be significant to see whether this
positive effect is reproducible at the tissue and organ levels or
is an artifact of in vitro experiments. Conclusions from this
study were that mitochondrial energetics may be controlled
through processes upstream (“push” condition) and downstream (“pull” condition) of NADH production. Stimulatory
effects of Ca2⫹ on oxygen uptake, membrane potential, and
ATPase activity were higher, percentage-wise, in the push
condition, but higher in magnitude in the pull condition. The
authors acknowledged that the model could not provide an
unequivocal estimate of the relative contributions of control
points upstream and downstream of the TCA cycle and suggested that further experiments can help resolve this matter.
However, as biological control is a mostly theoretical concept
at the present, a precise definition in terms of measurable
quantities is necessary before the concept is readily confirmed
or refuted by experiments.
Besides scientific findings, the studies by Beard (8) and
Cortassa et al. (20) also contributed significantly to the mitochondrial modeling community by publishing all governing
equations and values and sources of parameters associated with
their models. This contribution is arguably more important
than the scientific findings themselves, as they facilitate the
reproducibility of the work as well as follow-up studies. It is
thus recognized that kinetics-driven models such as these,
although useful, require a large number of system- and condition-specific parameters for which values are difficult and
laborious to obtain. The lack of data for such parameters is a
major hindrance to kinetics-based modeling, rendering it inaccessible for many systems of interest, particularly those not
amenable to experimental studies.
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rate is primarily dependent on the kinetics of the corresponding
enzyme, which, in turn, depends on the enzymatic mechanism,
regulatory state, and quantity of available enzymes. A kineticsbased description is perhaps the most straightforward, although
not always simple or feasible, approach to quantitatively model
biological systems. Kinetic models of mitochondria have been
consistently improved in scale and sophistication. Recently,
Beard (8) expanded the kinetic modeling approach with thermodynamic principles to study the mitochondrial respiratory
system. He stressed that consistency and accuracy of the
mathematical description are vital in developing models of
biophysical processes that span multiple cellular scales. This
thermodynamically consistent model applied differential equations to describe the proton gradient, substrate transport, and
ATP synthesis and used distinct state variables for mitochondrial membrane potentials. The basic components included in
the model were reactions at complexes I, III, IV, and V,
adenine nucleotide translocase, phosphate transport, and cation
fluxes. The model accounted for three separate compartments:
mitochondrial matrix, intermembrane space, and external
space. The overall flux balance was governed by a system of 17
differential equations and 35 parameters. The parameters were
categorized into three classes: 16 adjustable parameters, the
values of which were determined by fitting model simulations
to published data; 17 fixed parameters, the values of which
were established in the literature; and 2 free parameters, the
values of which were set arbitrarily large. The author cleverly
used 9 independent data curves (14) to estimate values for the
16 adjustable parameters. A unique advance made by this
model is that the proton gradient and the membrane potential (2
separate components of the electrochemical gradient) are
treated as independent variables. Changes in proton concentration were expressed as a function of fluxes by dehydrogenase
enzymes, respiratory complexes (I, III, IV, and V), ion transport, and membrane leak. The membrane potential was also
considered to be a function of these components but was
adjusted by the inner membrane capacitance. Separate treatments of these two components allowed the author to discover
effects of phosphate control on the mitochondrial membrane
potential. In fact, results from this study strongly suggested that
phosphate modulates the activity of complex III in a concentration-dependent manner. This valuable hypothesis can be
readily tested in vitro with isolated mitochondria. This study
highlights the power of kinetic models in uncovering control
points in a system and in driving testable hypotheses that can
be used for further experimental study.
Instead of focusing on the pH gradient and reactions of the
respiratory chains, the model by Cortassa et al. (20) integrates
these components with enzymes in the tricarboxylic acid
(TCA) cycle and Ca2⫹ dynamics (20). The major advance of
this model over its predecessors (58, 77, 130) is the direct
incorporation of Ca2⫹ concentration into the mechanisms of
␣-ketoglutarate and isocitrate dehydrogenases and explicit examination of their influences on mitochondrial energy metabolism. This unified model was used to explore the coupling of
energy demand in response to changes in substrate supply,
inhibition of the respiratory chain, and Ca2⫹ level in heart
mitochondria. The 12 ordinary differential equations included
in the model described the time derivatives of the mitochondrial membrane potential and concentrations of NAD, NADH,
ADP, ATP, Ca2⫹, and the TCA cycle intermediates. Specifi-
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RECENT ADVANCES IN MITOCHONDRIAL STUDIES
⳵关X兴
⫽S䡠v⫽0
⳵t
(1)
where X represents the vector of metabolite concentrations, S is
the stoichiometric matrix, and the variable v represents a flux
vector containing the steady-state reaction rate for every reaction in the network. Solutions for v are systematically determined by the successive application of additional constraints,
such as those representing directional (reversible vs. irreversible) and enzymatic capacity considerations. These constraints
have the following form
␣i ⱕ vi ⱕ ␤i
(2)
where ␣i and ␤i represent the lower and upper bounds on the
steady-state rate of each reaction. Energy-balance constraints have also been developed to disallow fluxes in
thermodynamically infeasible internal reaction cycles (9,
94). These bilinear constraints introduce a second set of
variables, stored in the vector ⌬␮, which represents the
change in chemical potential associated with reactions in the
network:
K 䡠 ⌬␮ ⫽ 0
v 䡠 ⌬␮ ⬍ 0
(3)
The matrix K (Eq. 2) stores the null space basis for a matrix S⬘,
where S⬘ contains rows and columns in S corresponding only
to internal reactions. The resulting solution space often contains a range of possible values, rather than a unique number,
for each reaction rate vi that satisfies the stated constraints.
Equations 1 and 2 are most commonly applied to study metabolic states of cellular systems because of their simple mathematics and guaranteed solutions. Such simplification is based on two
fundamental assumptions that must be discussed. First, it is
assumed that all biochemical reactions and physical interactions
can be written as equations with known participants. Second, the
flux distribution v calculated based on Eqs. 1 and 2 assumes a
steady state where the change in concentration of every chemical
species in the network is approximately zero. Rationales and
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ramifications associated with these two assumptions are as follows. The first assumption is straightforward for most biochemical
reactions where reactants and products are well defined. However,
participants and stoichiometry of reactions or interactions involving signaling molecules, activation and inactivation of enzymes,
and voltage-gated responses are frequently ill defined, making it
difficult to incorporate them into the stoichiometric matrix. There
are two implications of the steady-state assumption: 1) there must
be no internal buildup of metabolites in the cell, so that the mass
conservation equation (Eq. 1) holds perfectly, and 2) observable
phenotypes or biological phenomena of interest occur on a time
scale longer than the rate at which metabolites are produced and
consumed by reactions in the network. Consequently, Eq. 1 and
the mentioned constraints apply only to a subset of biological
systems and only at a time scale satisfying assumption 2. Specifically, these equations are most appropriately used to investigate
time-invariant biological qualities, such as network topology (95)
and metabolite pool identification (32), or to characterize gene
essentiality (28) and end points of adaptive evolution (47). Successes in these studies have clearly demonstrated the strength of
constraint-based modeling in studying such biological behaviors
(7, 23, 47). In contrast, the steady-state assumption precludes the
use of constraint-based methods to study concentration-dependent
behaviors associated with transient or periodic dynamics observed
in regulatory (activation, inhibition, feedback) or signaling responses by neuronal and muscular cells. For example, even when
the components and interactions of the JAK-STAT signaling
network are painstakingly identified, only topological characteristics can be satisfactorily analyzed (91). With respect to the
mitochondrial systems, the constraint-based approach cannot be
used to model oscillatory behaviors resulting from muscle excitation (6, 21) but is ideal for studying metabolic disturbance due
to enzymatic defects (100). These limitations are associated only
with the use of Eq. 1 and are not inherent in the constraint-based
methodology. In other words, the constraint-based approach allows the use of Eq. 1, i.e., the omission of kinetic data, but it does
not preclude the use of such data if they are available. In fact,
if such data are available, they can be formulated into constraints to further resolve flux calculation. On the other hand,
since the constraint-based approach was developed as a way to
avoid the need for kinetic data and the nonlinearity present in
kinetic rate laws, most researchers apply the constraint-based
method only when they do not have kinetic data and/or want to
simplify the calculation. These assumptions are thus not direct
results from the constraint-based philosophy but have always
been associated with it.
Methods of analysis under the constraint-based framework.
Under the constraint-based modeling framework, flux balance
analysis is commonly applied to find a physiologically meaningful v within the solution space (13, 96). Specifically, linear
programming is employed to search for a flux distribution v
that maximizes a particular end product such as NADH production (Fig. 3). The previously described mitochondrial metabolic network (see Data Integration) was used to assess a
broad range of the mitochondria’s physiological functions,
such as ATP production, heme biosynthesis, and phospholipid
biosynthesis (128). The linear programming formulation assumed that the flux distribution that most efficiently carried out
a given mitochondrial function under the constrained cellular
resources would most resemble the mitochondrial physiological state in the cell. Notably, the maximal ATP yield per
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Constraint-based modeling. An alternative, data-driven,
constraints-based approach has been developed to partially
overcome this difficulty (Fig. 3). This modeling approach seeks
to narrow the range of possible phenotypes that can be displayed by a metabolic system by imposing physicochemical
constraints, rather than precisely determining the exact behavior of the system (24, 96). The constraint-based method is
typically used in combination with a reconstructed network
representing the system of interest. Quantitatively, the reconstructed network can be represented by a stoichiometric matrix
S (m ⫻ n), where m is the number of metabolites and n is the
number of reactions (103). Each element Sij represents the
coefficient of metabolite i in reaction j, following the convention that Sij is positive if the metabolite is the product of the
reaction and negative otherwise. A zero entry is used when the
metabolite does not participate in the reaction. Reversible
reactions can be written in either direction. Under the steadystate assumption, the rate of consumption of every metabolite
equals its rate of production. This mass conservation relation
translates to a system of ordinary differential equations
RECENT ADVANCES IN MITOCHONDRIAL STUDIES
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Fig. 4. Effect of diabetic disturbance on phospholipid synthesis (A) and pyruvate
dehydrogenase flux (B). x-Axis, allowable flux values for the corresponding
reaction; y-axis, likelihood that the reaction assumes a particular flux value. Each
curve represents the distribution of allowable flux values for the corresponding
reaction under a particular condition: normal (black), diabetic (magenta), diabetic
with a forced normal glucose uptake (green), and diabetic with forced normal
glucose and ketone body uptake (blue). Forced normal uptake of glucose and
ketone bodies represents effects of a possible treatment. As long as fatty acid
uptake is high, treatments only minimally affect steady-state reaction rates.
small (Fig. 4A). Increasing the ketone body and/or glucose uptake
to normal physiological values led to a minimal increase in
allowable range of reaction fluxes. Interestingly, the flux through
the mitochondrial pyruvate dehydrogenase enzyme was significantly restricted by network stoichiometry when the fatty acid
uptake was increased (Fig. 4B). Many studies have tried to
identify factors that affect the inhibitory mechanism of pyruvate
dehydrogenase under conditions such as diabetes (117, 119); this
study showed that an increase in cellular fatty acid uptake flux
forced a significantly lower flux through this enzyme as a direct
consequence of overall network stoichiometry.
FUTURE DIRECTIONS: BRIDGING EXPERIMENTAL AND
COMPUTATIONAL STUDIES
With the plentiful experimental data and computational
models analyzing such data, what is needed to complete the
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glucose of the reconstructed network was calculated to be 31.5.
Although simple, the significance of this calculation is at least
twofold. First, it resolves an inconsistency in a quantity that
exists in the literature as both 36 –38 (129) and 31 (109, 118).
Second, it represents the power of this method to systematically account for both costs and gains of a cellular process. The
value of 31.5 ATP per glucose molecule was found through
discovery of a net difference of two protons per glucose
molecule between the present calculation and those previously
reported (109, 118) after all protons consumed in glycolysis,
the malate-aspartate shuttle, and phosphate transport are accounted for. These two protons were gained through glycolysis
when every reaction was elementally and charge balanced.
Such a theoretical calculation does not account for the variable
proton leak or electron loss in the respiratory chain that is
inherent in all mitochondria and thus should be considered only
as an upper-bound approximation.
Due to the common underdetermined nature of constraintbased models, the flux distribution calculated to maximize a
particular objective function, ATP demand, for example, is not
unique. Alternate pathways, termed alternate optima (78), produce
the same optimal objective values. The flux distributions corresponding to these paths are found by setting the maximal objective value as an additional constraint and repeating the search for
v. Enumeration of all possible alternate optima is computationally
intractable for large networks, but is feasible for sufficiently small
systems, including the present mitochondrial metabolic network.
All possible pathways that can be used by the mitochondrial
network to optimally satisfy each of the three metabolic functions
were identified (128). When glucose, fatty acids, and glutamate
were simultaneously available to produce ATP, only four optimal
flux distributions were found. A much larger number of alternate
optimal solutions were found for the maximal synthesis of heme
(8,288 solutions) and phospholipids (21,863 solutions), respectively. The number of alternate optima with respect to an objective
represents the network redundancy in carrying out the corresponding metabolic function.
For most biological systems, there are an infinite number of
theoretical steady-state v satisfy Eqs. 1 and 2. A distribution of
possible values for each reaction rate (vi) can be determined by
sampling the entire null space of S. Such results allow one to
evaluate how changes in steady-state rates of a subset of reactions
systemically affect the rates of the remaining reactions in the
network. A random walk algorithm (artificial centering hit-andrun) (55) has been used to evaluate steady-state flux distributions
in the cardiac mitochondrion subjected to metabolic disturbances
associated with diabetes (123). Effects of diabetes on mitochondrial metabolism were assessed by simultaneous application of
constraints reflecting 1) an increased fatty acid uptake via the
carnitine-palmintoyltransferase-I shuttle, 2) a decreased glucose
consumption due to the reduced number of glucose transporters in
cell membrane, and 3) an increased ketone body uptake due to its
higher blood concentration (Fig. 4). In the normal condition,
allowable flux values (flux values satisfying all imposed constraints) of each reaction formed a broad distribution. The peak of
each distribution represented the most probable flux value of that
reaction. However, the increased mitochondrial fatty acid uptake
in diabetes led to much narrower distributions and hence smaller
allowable flux ranges, for most reaction fluxes. In particular, the
phospholipid synthesis activity increases sharply, but the range of
feasible flux values for maintaining cellular steady state is very
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Responsibilities of computational biologists, on the other
hand, may include several criteria.
Outlining data specificity. Currently, most available biological data are qualitatively described, and quantitative data are
often reported in arbitrary units. In moving toward standardized data representation, modelers should provide guidelines
on what is desirable in terms of units, data types (snap shot vs.
time series, in vivo vs. in vitro), and number of replications
necessary for statistical confidence.
Providing testable hypotheses. For modeling studies to drive
the systems biology cycle forward, analysis of such models
should not only confirm or falsify experimental interpretations
but ought to lead to testable hypotheses. Such hypotheses
should provide expected results when possible and should be
realistic and practical within the constraints of experimental
studies.
Suggesting experimental design. Computational analyses of
experimental data or model prediction can identify which
measurements are the most informative. Such results can
greatly benefit the design of subsequent experiments.
Making model and analysis programs available for others.
Many computational studies only briefly describe the underlying algorithms and do not publish associated computational
programs, as it is not required by most peer-reviewed journals.
However, for results to be reproducible, a documented source
code of such programs should accompany every study. Standards for distributing models, such as systems biology markup
language (SBML) (46) and minimum information requested in
the annotation of biochemical models (MIRIAM) (68), could
be adopted, and a centralized database could be created as a
repository for these models.
Perhaps one way to accelerate the realization of such standards and to drive systems biology forward is to have experimental and computational researchers meet each other half
way. It is probably no longer realistic for each group to stay
within the boundary of their respective discipline. To transform
biology into a more quantitative discipline, efforts should be
contributed by both groups of researchers. Experimentalists
should understand the basics of computer-aided analytical
tools, and computational biologists should understand the systems beyond the mathematical representation by experiencing
experimental biology firsthand at the bench. Through interdisciplinary training, perhaps the next generation of researchers
will no longer have to identify themselves as either experimentalist or computational biologist but, rather, biologists of a new
era, the systems biology era.
ACKNOWLEDGMENTS
The authors thank Andrew Joyce and Dr. John Mongan for critical reading
of the manuscript.
GRANTS
This work was supported by University of California Systemwide Biotechnology Research and Education Program GREAT Training Grant 2005-246 to
T. D. Vo.
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