Literature DB >> 15235611

In silico simulation of biological network dynamics.

Lukasz Salwinski1, David Eisenberg.   

Abstract

Realistic simulation of biological networks requires stochastic simulation approaches because of the small numbers of molecules per cell. The high computational cost of stochastic simulation on conventional microprocessor-based computers arises from the intrinsic disparity between the sequential steps executed by a microprocessor program and the highly parallel nature of information flow within biochemical networks. This disparity is reduced with the Field Programmable Gate Array (FPGA)-based approach presented here. The parallel architecture of FPGAs, which can simulate the basic reaction steps of biological networks, attains simulation rates at least an order of magnitude greater than currently available microprocessors.

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Year:  2004        PMID: 15235611     DOI: 10.1038/nbt991

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  12 in total

1.  Rule-based modelling and simulation of biochemical systems with molecular finite automata.

Authors:  J Yang; X Meng; W S Hlavacek
Journal:  IET Syst Biol       Date:  2010-11       Impact factor: 1.615

Review 2.  Statistical signals in bioinformatics.

Authors:  Samuel Karlin
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-12       Impact factor: 11.205

3.  Biochemical simulations: stochastic, approximate stochastic and hybrid approaches.

Authors:  Jürgen Pahle
Journal:  Brief Bioinform       Date:  2009-01-16       Impact factor: 11.622

4.  A Digitally Programmable Cytomorphic Chip for Simulation of Arbitrary Biochemical Reaction Networks.

Authors:  Sung Sik Woo; Jaewook Kim; Rahul Sarpeshkar
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2018-04       Impact factor: 3.833

5.  Bootstrapping least-squares estimates in biochemical reaction networks.

Authors:  Daniel F Linder; Grzegorz A Rempała
Journal:  J Biol Dyn       Date:  2015       Impact factor: 2.179

6.  In silico model-based inference: a contemporary approach for hypothesis testing in network biology.

Authors:  David J Klinke
Journal:  Biotechnol Prog       Date:  2014-08-26

Review 7.  Quantitative understanding of cell signaling: the importance of membrane organization.

Authors:  Krishnan Radhakrishnan; Adám Halász; Dion Vlachos; Jeremy S Edwards
Journal:  Curr Opin Biotechnol       Date:  2010-09-09       Impact factor: 9.740

8.  Reaction factoring and bipartite update graphs accelerate the Gillespie Algorithm for large-scale biochemical systems.

Authors:  Sagar Indurkhya; Jacob Beal
Journal:  PLoS One       Date:  2010-01-06       Impact factor: 3.240

9.  Deterministic mathematical models of the cAMP pathway in Saccharomyces cerevisiae.

Authors:  Thomas Williamson; Jean-Marc Schwartz; Douglas B Kell; Lubomira Stateva
Journal:  BMC Syst Biol       Date:  2009-07-16

10.  Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

Authors:  Alexander Mitsos; Ioannis N Melas; Melody K Morris; Julio Saez-Rodriguez; Douglas A Lauffenburger; Leonidas G Alexopoulos
Journal:  PLoS One       Date:  2012-11-30       Impact factor: 3.240

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