This proposal is for continued development and enhancement of existing and successful theory and software infrastructure developed at Caltech for the systems biology community, and builds on experience with SBML and engineering softwared. (1) Next-generation, multiscale, deterministic/stochastic simulation software. Computational models in biology are continually growing in complexity and size. Their accurate and effective simulation requires new algorithms and new software. Collaboration between the PI Doyle and Drs. Petzold (UCSB, creator of DASSL) and Dan Gillespie (creator of the stochastic simulation algorithm) have led to the development of combined deterministic/stochastic simulation algorithms that are much more efficient than existing stochastic algorithms, and can automatically determine the appropriate scale for different subsystems of a model. This program will support Dr. Gillespie's continued research and implementation of new algorithms in production-quality open-source software modules that will be made widely available. (2) Extension of SOSTOOLS. Recent Caltech research has developed mathematics for analyzing models, such as """"""""this model cannot explain the data for any set of plausible parameters"""""""" and """"""""this model is robust as parameters are varied."""""""" The theory builds on advances in several areas, including robust control and dynamical systems theory, computational complexity, real semi-algebraic geometry, semidefinite programming, and duality. The result of this work has been a new class of scalable algorithms for model analysis and (in)validation and iterative experimentation for large-scale, stochastic, nonlinear, nonequilibrium, hybrid (containing both continuous and discrete mathematics) networks with multiple time and spatial scales. The recent progress is implemented in SOSTOOLS, an open-source (GPL) MATLAB toolbox. This program will enhance and extend SOSTOOLS to exploit biological specific structure, treat stochastic models to complement simulation, make connections with Savageau's S-system formalism, perform model (in)validation from data, and develop provably correct model reduction for nonlinear biochemical models. (3) Integration of stochastic simulation and SOS analysis. Analysis of complex stochastic biochemical networks will require a blend of simulation and SOS analysis and model reduction, and this program will create an integrated suite of software tools with rigorous theoretical foundations. ? ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM078992-03
Application #
7482413
Study Section
Special Emphasis Panel (ZGM1-CBCB-5 (BM))
Program Officer
Anderson, James J
Project Start
2006-05-01
Project End
2010-04-30
Budget Start
2008-05-01
Budget End
2009-04-30
Support Year
3
Fiscal Year
2008
Total Cost
$338,533
Indirect Cost
Name
California Institute of Technology
Department
Type
Schools of Engineering
DUNS #
009584210
City
Pasadena
State
CA
Country
United States
Zip Code
91125
Li, Na; Cruz, Jerry; Chien, Chenghao Simon et al. (2014) Robust efficiency and actuator saturation explain healthy heart rate control and variability. Proc Natl Acad Sci U S A 111:E3476-85
Wallace, E W J; Gillespie, D T; Sanft, K R et al. (2012) Linear noise approximation is valid over limited times for any chemical system that is sufficiently large. IET Syst Biol 6:102-15
Nahmad, Marcos (2011) Steady-state invariant genetics: probing the role of morphogen gradient dynamics in developmental patterning. J R Soc Interface 8:1429-39
Roh, Min K; Daigle Jr, Bernie J; Gillespie, Dan T et al. (2011) State-dependent doubly weighted stochastic simulation algorithm for automatic characterization of stochastic biochemical rare events. J Chem Phys 135:234108
Daigle Jr, Bernie J; Roh, Min K; Gillespie, Dan T et al. (2011) Automated estimation of rare event probabilities in biochemical systems. J Chem Phys 134:044110
Roh, Min K; Gillespie, Dan T; Petzold, Linda R (2010) State-dependent biasing method for importance sampling in the weighted stochastic simulation algorithm. J Chem Phys 133:174106
Gillespie, Dan T; Roh, Min; Petzold, Linda R (2009) Refining the weighted stochastic simulation algorithm. J Chem Phys 130:174103
Gillespie, Daniel T (2009) Deterministic limit of stochastic chemical kinetics. J Phys Chem B 113:1640-4
Gillespie, Daniel T (2009) A diffusional bimolecular propensity function. J Chem Phys 131:164109
Gillespie, Dan T; Cao, Yang; Sanft, Kevin R et al. (2009) The subtle business of model reduction for stochastic chemical kinetics. J Chem Phys 130:064103

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