Literature DB >> 9618484

Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets.

P J Goss1, J Peccoud.   

Abstract

An integrated understanding of molecular and developmental biology must consider the large number of molecular species involved and the low concentrations of many species in vivo. Quantitative stochastic models of molecular interaction networks can be expressed as stochastic Petri nets (SPNs), a mathematical formalism developed in computer science. Existing software can be used to define molecular interaction networks as SPNs and solve such models for the probability distributions of molecular species. This approach allows biologists to focus on the content of models and their interpretation, rather than their implementation. The standardized format of SPNs also facilitates the replication, extension, and transfer of models between researchers. A simple chemical system is presented to demonstrate the link between stochastic models of molecular interactions and SPNs. The approach is illustrated with examples of models of genetic and biochemical phenomena where the ULTRASAN package is used to present results from numerical analysis and the outcome of simulations.

Mesh:

Year:  1998        PMID: 9618484      PMCID: PMC22622          DOI: 10.1073/pnas.95.12.6750

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  36 in total

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Authors:  J E TILL; E A MCCULLOCH; L SIMINOVITCH
Journal:  Proc Natl Acad Sci U S A       Date:  1964-01       Impact factor: 11.205

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Authors:  H Weintraub
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4.  Stochastic model for mast cell proliferation in culture of murine peritoneal cells.

Authors:  T Kobayashi; T Nakahata
Journal:  J Cell Physiol       Date:  1989-01       Impact factor: 6.384

5.  Regulation of lambda dv plasmid DNA replication. A quantitative model for control of plasmid lambda dv replication in the bacterial cell division cycle.

Authors:  D D Womble; R H Rownd
Journal:  J Mol Biol       Date:  1986-10-05       Impact factor: 5.469

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Authors:  A P Korn; R M Henkelman; F P Ottensmeyer; J E Till
Journal:  Exp Hematol       Date:  1973       Impact factor: 3.084

7.  Stochastic models for an open biochemical system.

Authors:  S Hasstedt
Journal:  Biosystems       Date:  1978-12       Impact factor: 1.973

8.  A stochastic model of self-renewal and commitment to differentiation of the primitive hemopoietic stem cells in culture.

Authors:  T Nakahata; A J Gross; M Ogawa
Journal:  J Cell Physiol       Date:  1982-12       Impact factor: 6.384

9.  A full stochastic description of the Michaelis-Menten reaction for small systems.

Authors:  P Arányi; J Tóth
Journal:  Acta Biochim Biophys Acad Sci Hung       Date:  1977

10.  Regulation of IncFII plasmid DNA replication. A quantitative model for control of plasmid NR1 replication in the bacterial cell division cycle.

Authors:  D D Womble; R H Rownd
Journal:  J Mol Biol       Date:  1986-12-05       Impact factor: 5.469

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  57 in total

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2.  The selective values of alleles in a molecular network model are context dependent.

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Review 6.  The cognitive phenotype of Down syndrome: insights from intracellular network analysis.

Authors:  Avi Ma'ayan; Katheleen Gardiner; Ravi Iyengar
Journal:  NeuroRx       Date:  2006-07

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Journal:  EMBO Rep       Date:  2009-08       Impact factor: 8.807

8.  Stochastic modeling of gene positive autoregulation networks involving signal molecules.

Authors:  Xin Fang; William E Bentley; Evanghelos Zafiriou
Journal:  Biophys J       Date:  2008-07-03       Impact factor: 4.033

9.  Transcription factor network reconstruction using the living cell array.

Authors:  Eric Yang; Martin L Yarmush; Ioannis P Androulakis
Journal:  J Theor Biol       Date:  2008-10-22       Impact factor: 2.691

10.  Tracing the footsteps of autophagy in computational biology.

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Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

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