Slow update stochastic simulation algorithms for modeling complex biochemical networks

The stochastic simulation algorithm (SSA) based modeling is a well recognized approach to predict the stochastic behavior of biological networks. The stochastic simulation of large complex biochemical networks is a challenge as it takes a large amount of time for simulation due to high update cost....

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Vydáno v:BioSystems Ročník 162; s. 135 - 146
Hlavní autoři: Ghosh, Debraj, De, Rajat K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Ireland Elsevier B.V 01.12.2017
Témata:
B cell receptor signaling network
FcϵRI signaling network
Gillespie algorithm
Stochastic modeling
B cell receptor signaling network
FcϵRI signaling network
Stochastic modeling
Gillespie algorithm
ISSN:0303-2647, 1872-8324, 1872-8324
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Shrnutí:The stochastic simulation algorithm (SSA) based modeling is a well recognized approach to predict the stochastic behavior of biological networks. The stochastic simulation of large complex biochemical networks is a challenge as it takes a large amount of time for simulation due to high update cost. In order to reduce the propensity update cost, we proposed two algorithms: slow update exact stochastic simulation algorithm (SUESSA) and slow update exact sorting stochastic simulation algorithm (SUESSSA). We applied cache-based linear search (CBLS) in these two algorithms for improving the search operation for finding reactions to be executed. Data structure used for incorporating CBLS is very simple and the cost of maintaining this during propensity update operation is very low. Hence, time taken during propensity updates, for simulating strongly coupled networks, is very fast; which leads to reduction of total simulation time. SUESSA and SUESSSA are not only restricted to elementary reactions, they support higher order reactions too. We used linear chain model and colloidal aggregation model to perform a comparative analysis of the performances of our methods with the existing algorithms. We also compared the performances of our methods with the existing ones, for large biochemical networks including B cell receptor and FcϵRI signaling networks.
Bibliografie:ObjectType-Article-1
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ISSN:0303-2647
1872-8324
1872-8324
DOI:10.1016/j.biosystems.2017.10.011