Block Search Stochastic Simulation Algorithm (BlSSSA): A Fast Stochastic Simulation Algorithm for Modeling Large Biochemical Networks

Stochastic simulation algorithms are extensively used for exploring stochastic behavior of biochemical pathways/networks. Computational cost of these algorithms is high in simulating real biochemical systems due to their large size, complex structure and stiffness. In order to reduce the computation...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 19; no. 4; pp. 2111 - 2123
Main Authors: Ghosh, Debraj, De, Rajat K.
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
1,3-Butadiene
Algorithms
B cell receptor signaling network
Biochemistry
Biological system modeling
Colloidal aggregation network and stochastic modeling
Computational efficiency
Computational modeling
Computer applications
Computing costs
Data structures
Fc receptors
FceRI signaling network
Gillespie algorithm
Indexes
Mathematical model
Networks
Oxidation
Signaling
Simulation
Statistics
Stiff network
Stiffness
Stochastic processes
Stochasticity
ISSN:1545-5963, 1557-9964, 1557-9964
Online Access:Get full text
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Summary:Stochastic simulation algorithms are extensively used for exploring stochastic behavior of biochemical pathways/networks. Computational cost of these algorithms is high in simulating real biochemical systems due to their large size, complex structure and stiffness. In order to reduce the computational cost, several algorithms have been developed. It is observed that these algorithms are basically fast in simulating weakly coupled networks. In case of strongly coupled networks, they become slow as their computational cost become high in maintaining complex data structures. Here, we develop Block Search Stochastic Simulation Algorithm ( BlSSSA ). BlSSSA is not only fast in simulating weakly coupled networks but also fast in simulating strongly coupled and stiff networks. We compare its performance with other existing algorithms using two hypothetical networks, viz., linear chain and colloidal aggregation network, and three real biochemical networks, viz., B cell receptor signaling network, FceRI signaling network and a stiff 1,3-Butadiene Oxidation network. It has been shown that BlSSSA is faster than other algorithms considered in this study.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2021.3070123