An Aggregation Technique for the Transient Analysis of Stiff Markov Chains
IEEE Transactions on Computers
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Journal of Computational Physics
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Probabilistic model checking of complex biological pathways
Theoretical Computer Science
Computational Probability for Systems Biology
FMSB '08 Proceedings of the 1st international workshop on Formal Methods in Systems Biology
Abstraction for Stochastic Systems by Erlang's Method of Stages
CONCUR '08 Proceedings of the 19th international conference on Concurrency Theory
Sliding Window Abstraction for Infinite Markov Chains
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Concurrency and composition in a stochastic world
CONCUR'10 Proceedings of the 21st international conference on Concurrency theory
Model checking Markov chains using Krylov subspace methods: an experience report
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Streamlined formulation of adaptive explicit-implicit tau-leaping with automatic tau selection
Winter Simulation Conference
Simplification of a complex signal transduction model using invariants and flow equivalent servers
Theoretical Computer Science
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Computational models of biochemical systems are usually very large, and moreover, if reaction frequencies of different reaction types differ in orders of magnitude, models possess the mathematical property of stiffness, which renders system analysis difficult and often even impossible with traditional methods. Recently, an accelerated stochastic simulation technique based on a system partitioning, the slow-scale stochastic simulation algorithm, has been applied to the enzyme-catalyzed substrate conversion to circumvent the inefficiency of standard stochastic simulation in the presence of stiffness. We propose a numerical algorithm based on a similar partitioning but without resorting to simulation. The algorithm exploits the connection to continuous-time Markov chains and decomposes the overall problem to significantly smaller subproblems that become tractable. Numerical results show enormous efficiency improvements relative to accelerated stochastic simulation.