#GHC14
Language Design and
Implementation for Computational
Modeling, Simulation and
Visualization
Vishakha Sharma (PhD Candidate)
Adriana Compagnoni (Advisor)
Department of Computer Science
Stevens Institute of Technology, NJ
2014
October 8-10, 2014, Phoenix, AZ
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Computational Biology
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The convergence of Biology and Computer Science
An emerging discipline
Studies complex biological systems
Large numbers of diverse and multifunctional elements
Selective Interactions
It combines experimental and computational research
Intrinsically interdisciplinary
Accelerates our understanding of life.
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Suitability of Concurrent Programming
Languages
 Biological processes are intrinsically concurrent
 Communication channels can be used to represent
biological reactions (send/receive handshake)
 Dynamic creation of communication channels enables
reactions in a changing system (multiplication, growth,
death, mutation,…)
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Benefits of Computational Models
 In vitro vs in silico experiments: In vitro (may be) is
faster, but in silico is cheaper
 Can venture what is not observable suggesting
unforeseen experiments.
 Offer a testbed for unknown behavior – what if…
 Can generate synthetic data
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The Cost of Drug Development
For companies that have launched more than three drugs,
the median cost per new drug is $4.2 billion; for those
that have launched more than four, it is $5.3 billion. Even if
a company only develops one drug, the median spending
is still a hefty $351 million.
98 companies, 220 drugs
8/11/2013 Matthew Herpes, Forbes
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Computational Model of Antibacterial
Surfaces
 Goal: Develop biomaterials that minimize bacterial
colonization
 Proposal: Building Computational Model to reduce number of
experiments and predict behavior
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Motivating Example: Bifunctional Polymer
Brushes
Dr. Henk J. Busscher’s group at University Medical Center Groningen, The Netherlands
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BioScape: A High Level Modeling and
Simulation Language
BioScape Syntax
[email protected], 2.0
[email protected], 1.0
Bac()@movBac, stepBac, shapeBac() =
!attach.PBac() +
mov.Bac()
PBac()@movPBac, stepPBac, shapePBac() =
[email protected].(PBac() | PBac()) +
?kill().DBac()
DBac()@movDBac, stepDBac, shapeDBac() = [email protected]
PEO()@movPEO, stepPEO, shapePEO() = ?attach()
Lyso()@movLyso, stepLyso, shapeLyso() = !kill()
Adriana Compagnoni, Vishakha Sharma, Yifei Bao, Matthew Libera, Svetlana Sukhishvili. Philippe Bidinger, Livio Bioglio and Eduardo
Bonelli. BioScape: A Modeling and Simulation Language for Bacteria-Materials Interactions. Electronic Notes in Theoretical Computer
Science, 293(0):35 - 49, 2013. Proceedings of the Third International Workshop on Interactions Between Computer Science and Biology
(CS2Bio'12).
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From Lab Data To Computational Model
Three different surfaces
Conjugates
Surface
coverage by
Lysozyme in
Wet Lab [%]
Pluronic
Unmodified
Number of
PEO Binding
Sites in
silico
Number of
Lysozyme
Binding
Sites in
silico
10000
1% Pl-Lys
32
6800
3200
100% Pl-Lys
47
5300
4700
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From Lab Data To Computational Model
Simulation Time:
 1 unit of simulation time corresponds to 10 minutes of wet lab.
 Adhesion phase: 12 units of simulation time corresponds to 120
minutes or 2 hours of wet lab.
 Growth phase: 108 units of simulation time corresponds to 1080
minutes or 18 hours of wet lab.
Ten times faster !!!
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Simulation Results
Adhesion Phase
Experiment 1: Pluronic Unmodified
Training Data
Experiment 2: 100% Pluronic-Lysozyme
Training Data
Experiment 3: 1% Pluronic-Lysozyme
Validation
Growth Phase
Experiment 4: Pluronic Unmodified
Validation
Experiment 5: 100% Pluronic-Lysozyme
Validation
Experiment 6: 1% Pluronic-Lysozyme
Validation
Vishakha Sharma, Adriana Compagnoni, Matthew Libera, Agnieszka K. Muszanska, Henk J. Busscher, Henny C. van der Mei. Simulating
Anti-adhesive and Antibacterial Bifunctional Polymers for Surface Coating using BioScape. In Proceedings of the ACM Conference on
Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB), Washington, DC, September 22 - 25, 2013.
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Predictions of the Computational Model
Between 1% and 10% of conjugation in the initial concentration
yields the minimal amount of bacteria with the maximal % of
dead bacteria.
LESS IS BETTER !!!
Vishakha Sharma, Adriana Compagnoni, Matthew Libera, Agnieszka K. Muszanska, Henk J. Busscher, Henny C. van der Mei. Simulating
Anti-adhesive and Antibacterial Bifunctional Polymers for Surface Coating using BioScape. In Proceedings of the ACM Conference on
Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB), Washington, DC, September 22 - 25, 2013.
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JAK-STAT Signal Transduction Pathway
What do we propose?
We propose the construction of a stochastic computational model:
• for better understanding of cell biology along the pathway, and
• for the simulation of the effect of existing drugs as well as for development for future
treatments.
JAK-STAT Signal Transduction Pathway - 46 Reactions
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Simulation Results
COPASI (Deterministic) and SPiM (Stochastic)
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Population of mRNAc, STAT1c, SOCS1 and STAT1n*-STAT1n*
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Population of mRNA in the cytoplasm mRNAc, using COPASI (red) and SPiM (green and blue)
Vishakha Sharma and Adriana Compagnoni. Computational and Mathematical Models of the JAK-STAT Signal Transduction
Pathway. In Proceedings of the Summer Computer Simulation Conference (SCSC), Toronto, Canada, July 7 - 10, 2013.
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Beyond Biology…
Motivation: Effects of Counterfeit Components in complex multi-component systems.
Proposal: Building stochastic computational model for a) identifying counterfeiting and studying
its effects in military supply chain; and b) simulation to compare expected failures of a
system as a whole versus failure due to the counterfeit components of lesser quality.
(b)
(a)
(a) Five Component Subsystem of Magellan GPS 315, and
(b) Agent-Based Model of Magellan GPS 315
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Simulations Results
Stochastic Pi Machine (SPiM)
Implementation
Visual
(b)
(c)
(a)
(a) Failure Counts of Verified and Counterfeit Components for 3 Runs; Run 1 (Black), Run 2 (Blue) and Run 3 (Red)
(b) 1st time stamp – Configuration of assembled systems (ASystem)
(c) Last time stamp -Failed assembled systems (FSystem)
Vishakha Sharma, Adriana Compagnoni and Jose Emmanuel Ramirez-Marquez. Computational Modeling of the Effects of Counterfeit
Components. In Proceedings of the Summer Computer Simulation Conference (SCSC), Monterey, CA, July 6 - 10, 2014.
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Conclusions and Future Work
Conclusions:
 We define BioScape, a high-level modeling and simulation language for the stochastic
simulation of biological and biomaterials processes.
 We visualize biofilm formation.
 We construct and validate the stochastic computational model for antibacterial surfaces.
 We predict optimal surface configuration with minimal number of attached bacteria and
maximal proportion of dead bacteria.
 We develop a model that can be used to predict the behavior of the JAK-STAT pathway in
the presence of inhibitory agents, creating a platform to assist in the development of new
drugs.
 We construct a model for identifying counterfeiting and studying its effects in the military
supply chain.
Future Work:
 Multifunctional coatings – Assembly from first principles
 Study adenoviral traffic in healthy/cancerous eukaryotic cells.
 Apply our stochastic computational modeling approach to other complex interdisciplinary
domains.
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