2014 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 October 8-10, 2014, Phoenix, AZ #GHC

Computational Biology  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.

2014 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,…)

2014 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

2014 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

2014 Computational Model of Antibacterial Surfaces  Goal: Develop biomaterials that minimize bacterial colonization  Proposal: Building Computational Model to reduce number of experiments and predict behavior

2014 Motivating Example: Bifunctional Polymer Brushes Dr. Henk J. Busscher’s group at University Medical Center Groningen, The Netherlands

2014 BioScape: A High Level Modeling and Simulation Language BioScape Syntax stepBac, shapeBac() = !attach.PBac() + mov.Bac() stepPBac, shapePBac() = | PBac()) + ?kill().DBac() stepDBac, shapeDBac() = stepPEO, shapePEO() = ?attach() 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): , Proceedings of the Third International Workshop on Interactions Between Computer Science and Biology (CS2Bio'12).

2014 From Lab Data To Computational Model Three different surfaces ConjugatesSurface coverage by Lysozyme in Wet Lab [%] Number of PEO Binding Sites in silico Number of Lysozyme Binding Sites in silico Pluronic Unmodified % Pl-Lys % Pl-Lys

2014 From Lab Data To Computational Model Ten times faster !!!  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. Simulation Time:

2014 Simulation Results 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 , Adhesion Phase Growth Phase Training Data Validation Experiment 4: Pluronic Unmodified Experiment 6: 1% Pluronic-Lysozyme Experiment 5: 100% Pluronic-Lysozyme Experiment 1: Pluronic Unmodified Experiment 3: 1% Pluronic-Lysozyme Experiment 2: 100% Pluronic-Lysozyme

2014 Predictions of the Computational Model 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 , 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 !!!

2014 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

2014 Simulation Results COPASI (Deterministic) and SPiM (Stochastic) Population of mRNAc, STAT1c, SOCS1 and STAT1n*-STAT1n* 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 , Population of mRNA in the cytoplasm mRNAc, using COPASI (red) and SPiM (green and blue)

2014 Beyond Biology… (a) Five Component Subsystem of Magellan GPS 315, and (b) Agent-Based Model of Magellan GPS 315 (a) (b) 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.

2014 Simulations Results (a) Failure Counts of Verified and Counterfeit Components for 3 Runs; Run 1 (Black), Run 2 (Blue) and Run 3 (Red) (b) 1 st time stamp – Configuration of assembled systems (ASystem) (c) Last time stamp -Failed assembled systems (FSystem) (a) (b) Stochastic Pi Machine (SPiM) Visual Implementation 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 , (c)(c)

2014 Conclusions and Future Work  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.  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. Future Work: Conclusions:

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