Bibliography of Author: Goss, P.J.

  1. Goss, P.J. and Peccoud, J.. "Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets." Proc Natl Acad Sci U S A. 95 (12). 1998. pp. 6750-5.
    [ .pdf ] [ PubMed ]

    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.

    Keywords: *Computer Simulation ; Human ; *Models Molecular ; *Molecular Biology ; *Stochastic Processes


  2. Goss, P.J. and Peccoud, J.. "Analysis of the stabilizing effect of Rom on the genetic network controlling ColE1 plasmid replication." Pac Symp Biocomput. 1999. pp. 65-76.
    [ .pdf ] [ PubMed ]

    A stochastic model of ColE1 plasmid replication is presented. It is implemented by using UltraSAN, a simulation tool based on an extension of stochastic Petri nets (SPNs). It allows an exploration of the variation in plasmid number per bacterium, which is not possible using a deterministic model. In particular, the rate at which plasmid-free bacteria arise during bacterial division is explored in some detail since spontaneous plasmid loss is a widely observed empirical phenomenon. The rate of spontaneous plasmid loss provides an evolutionary explanation for the maintainance of Rom protein. The presence of Rom acts to reduce variance in plasmid copy number, thereby reducing the rate of plasmid loss at bacterial division. The ability of stochastic models to link biochemical function with evolutionary considerations is discussed.

    Keywords: Cell Division ; Computational Biology_*methods ; *DNA Replication ; Escherichia coli_*genetics ; Escherichia coli_growth and development ; *Models Genetic ; *Plasmids ; Stochastic Processes