Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. 1998

P J Goss, and J Peccoud
Department of Organismic and Evolutionary Biology, Harvard University, Museum of Comparative Zoology Laboratories, 26 Oxford Street, Cambridge, MA 02138, USA. goss@mcz.harvard.edu

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.

UI MeSH Term Description Entries
D008958 Models, Molecular Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures. Molecular Models,Model, Molecular,Molecular Model
D008967 Molecular Biology A discipline concerned with studying biological phenomena in terms of the chemical and physical interactions of molecules. Biochemical Genetics,Biology, Molecular,Genetics, Biochemical,Genetics, Molecular,Molecular Genetics,Biochemical Genetic,Genetic, Biochemical,Genetic, Molecular,Molecular Genetic
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D013269 Stochastic Processes Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables. Process, Stochastic,Stochastic Process,Processes, Stochastic

Related Publications

P J Goss, and J Peccoud
December 2004, Journal of bioinformatics and computational biology,
P J Goss, and J Peccoud
January 2010, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference,
P J Goss, and J Peccoud
February 1988, Methods of information in medicine,
P J Goss, and J Peccoud
January 2014, BMC bioinformatics,
P J Goss, and J Peccoud
February 2009, Journal of bioinformatics and computational biology,
P J Goss, and J Peccoud
August 2013, IET systems biology,
P J Goss, and J Peccoud
May 2022, International journal of neural systems,
Copied contents to your clipboard!