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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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
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Abstract	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.
AbstractList	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. 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.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.
Author	De, Rajat K. Ghosh, Debraj
Author_xml	– sequence: 1 givenname: Debraj orcidid: 0000-0002-5529-8436 surname: Ghosh fullname: Ghosh, Debraj email: debrajrock@gmail.com organization: Machine Intelligence Unit, Indian Statistical Institution, Kolkata, West Bengal, India – sequence: 2 givenname: Rajat K. orcidid: 0000-0001-6080-1131 surname: De fullname: De, Rajat K. email: rajat@isical.ac.in organization: Machine Intelligence Unit, Indian Statistical Institution, Kolkata, West Bengal, India
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SubjectTerms	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
Title	Block Search Stochastic Simulation Algorithm (BlSSSA): A Fast Stochastic Simulation Algorithm for Modeling Large Biochemical Networks
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