Stochasticity plays an important role in many biological processes. Examples include bistable genetic switches, noise enhanced robustness of oscillations, and uctuation enhanced sensitivity or """"""""stochastic focusing"""""""". Numerous cellular systems, including development, morphogenesis, polarization and chemotaxis rely on spatial stochastic noise for robust performance. At the same time, stochastic simulations are complex and consume large amounts of computer time. They may require the researcher to be procient in the use of one or more complex software packages. Learning to use existing simulation tools and to integrate them with other software takes considerable time. In many cases, the tools do not exist and require the expertise of mathematicians and computer scientists to develop them. Often, researchers must purchase and maintain clusters of computers to perform the large-scale computations. All of this adds costs and delays to the research process. Currently, there exists no software package that allows researchers to easily build a stochastic model of a biological system, and scale it up to increasing levels of detail and complexity. We propose to build an environment where the modeler can focus his/her attention on the biology;alleviating the burden of software installation and versions, mathematical algorithms, code optimizations, computer systems, etc. This environment will run on laptops and computer workstations (for small problems), extending on demand to high-performance compute clusters, grids, and public or private clouds;thus creating a cost-eective and energy-ecient solution for simulations of all sizes. We will equip this environment with state of the art software for key classes of problems, and make it easy for software developers to integrate new and improved algorithms without the need to develop their own software infrastructure. We will develop new algorithms and software to address key computational capabilities that have not previously been attainable: (1) fully- adaptive, hybrid solvers for sti (and nonsti) well-mixed systems (2) ecient computation of probabilities of rare events, and (3) simulation of spatial stochastic systems at speeds that are several orders of magnitude faster than previous methods. The availability of such a community resource will enable and accelerate progress in both biology and algorithm development.

Public Health Relevance

Computer modeling and simulation provide critical insights necessary for the understanding of fundamental cellular systems: researchers postulate a mathematical model incorporating the relationships between key components, simulate it on a computer, and then compare the results to experiment to determine whether the model is plausible. Such an understanding, or model, of a biochemical process is important for drug targeting and therapeutic intervention. Stochasticity (randomness) plays an important role in many biological processes. Such simulations are complex and consume large amounts of computer time. We propose to build a comprehensive, state of the art software system for simulating stochastic models. The availability of such a community resource will enable and accelerate progress in biology and medicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB014877-03
Application #
8657394
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Peng, Grace
Project Start
2012-05-15
Project End
2015-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California Santa Barbara
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Santa Barbara
State
CA
Country
United States
Zip Code
93106
Hellander, Stefan; Petzold, Linda (2016) Reaction rates for a generalized reaction-diffusion master equation. Phys Rev E 93:013307
Meinecke, Lina; Lötstedt, Per (2016) Stochastic diffusion processes on Cartesian meshes. J Comput Appl Math 294:1-11
Drawert, Brian; Hellander, Stefan; Trogdon, Michael et al. (2016) A framework for discrete stochastic simulation on 3D moving boundary domains. J Chem Phys 145:184113
Kaucka, Marketa; Ivashkin, Evgeny; Gyllborg, Daniel et al. (2016) Analysis of neural crest-derived clones reveals novel aspects of facial development. Sci Adv 2:e1600060
Lawson, Michael J; Petzold, Linda; Hellander, Andreas (2015) Accuracy of the Michaelis-Menten approximation when analysing effects of molecular noise. J R Soc Interface 12:
Flegga, Mark B; Hellander, Stefan; Erban, Radek (2015) Convergence of methods for coupling of microscopic and mesoscopic reaction-diffusion simulations. J Comput Phys 289:1-17
Lötstedt, Per; Meinecke, Lina (2015) Simulation of stochastic diffusion via first exit times. J Comput Phys 300:862-886
Daigle Jr, Bernie J; Soltani, Mohammad; Petzold, Linda R et al. (2015) Inferring single-cell gene expression mechanisms using stochastic simulation. Bioinformatics 31:1428-35
Hellander, Stefan; Hellander, Andreas; Petzold, Linda (2015) Reaction rates for mesoscopic reaction-diffusion kinetics. Phys Rev E Stat Nonlin Soft Matter Phys 91:023312
Wu, Sheng; Fu, Jin; Petzold, Linda R (2015) Adaptive deployment of model reductions for tau-leaping simulation. J Chem Phys 142:204108

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