Collado-Vides, J., Hofestadt, R., Mavrovouniotis, M.L., and Michal, G.. "Modeling and simulation of gene regulation and metabolic pathways." Biosystems. 49
(1).
1999.
pp. 79-82.
[ .pdf ] [ PubMed ]
Keywords: *Gene Expression Regulation ; *Metabolism ; *Models Genetic
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
Hofestadt, R. and Meineke, F.. "Interactive modelling and simulation of biochemical networks." Comput Biol Med. 25
(3).
1995.
pp. 321-34.
[ PubMed ]
The analysis of biochemical processes can be supported using methods of modelling and simulation. New methods of computer science are discussed in this field of research. This paper presents a new method which allows the modelling and analysis of complex metabolic networks. Moreover, our simulation shell is based on this formalization and represents the first tool for the interactive simulation of metabolic processes.
Keywords: *Biochemistry ; Cell Communication_physiology ; Databases Factual ; Enzymes_physiology ; Gene Expression_physiology ; Genes Regulator_physiology ; Genetic Diseases Inborn_enzymology ; Genetic Diseases Inborn_genetics ; Genetic Diseases Inborn_metabolism ; *Metabolism ; *Models Chemical ; *Models Genetic ; Probability ; *Software
Matsuno, H., Doi, A., Nagasaki, M., and Miyano, S.. "Hybrid Petri net representation of gene regulatory network." Pac Symp Biocomput. 2000.
pp. 341-52.
[ .pdf ] [ PubMed ]
It is important to provide a representation method of gene regulatory networks which realizes the intuitions of biologists while keeping the universality in its computational ability. In this paper, we propose a method to exploit hybrid Petri net (HPN) for representing gene regulatory networks. The HPN is an extension of Petri nets which have been used to represent many kinds of systems including stochastic ones in the field of computer sciences and engineerings. Since the HPN has continuous and discrete elements, it can easily handle biological factors such as protein and mRNA concentrations. We demonstrate that, by using HPNs, it is possible to translate biological facts into HPNs in a natural manner. It should be also emphasized that a hierarchical approach is taken for our construction of the genetic switch mechanism of lambda phage which is realized by using HPNs. This hierarchical approach with HPNs makes easier the arrangement of the components in the gene regulatory network based on the biological facts and provides us a prospective view of the network. We also show some computational results of the protein dynamics of the lambda phage mechanism that is simulated and observed by implementing the HPN on a currently available tool.
Keywords: Bacteriophage lambda_genetics ; Bacteriophage lambda_growth and development ; Computer Simulation ; Gene Expression Regulation ; Gene Expression Regulation Viral ; Genes Viral ; *Models Genetic ; Operon ; Repressor Proteins_genetics ; Stochastic Processes ; Viral Proteins_genetics