Abstract
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.
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Busch, H., Sandmann, W., Wolf, V. (2006). A Numerical Aggregation Algorithm for the Enzyme-Catalyzed Substrate Conversion. In: Priami, C. (eds) Computational Methods in Systems Biology. CMSB 2006. Lecture Notes in Computer Science(), vol 4210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11885191_21
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DOI: https://doi.org/10.1007/11885191_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46166-1
Online ISBN: 978-3-540-46167-8
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