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Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses.
Institute for Molecular Evolution, Evolutionary Biology Centre of the University of Uppsala, Uppsala, Department of Biometry and Informatics, SLU, Uppsala.ORCID iD: 0000-0002-7173-5579
Department of Biometry and Informatics, SLU, Uppsala.
Institute for Molecular Evolution, Evolutionary Biology Centre of the University of Uppsala, Uppsala; Linnaeus Centre for Bioinformatics, Uppsala University and SLU, Uppsala, Sweden.
2002 (English)In: Biosystems (Amsterdam. Print), ISSN 0303-2647, E-ISSN 1872-8324, Vol. 66, no 1-2, p. 31-41Article in journal (Refereed) Published
Abstract [en]

Reverse engineering algorithms (REAs) aim at using gene expression data to reconstruct interactions in regulatory genetic networks. This may help to understand the basis of gene regulation, the core task of functional genomics. Collecting data for a number of environmental conditions is necessary to reengineer even the smallest regulatory networks with reasonable confidence. We systematically tested the requirements for the experimental design necessary for ranking alternative hypotheses about the structure of a given regulatory network. A genetic algorithm (GA) was used to explore the parameter space of a multistage discrete genetic network model with fixed connectivity and number of states per node. Our results show that it is not necessary to determine all parameters of the genetic network in order to rank hypotheses. The ranking process is easier the more experimental environmental conditions are used for the data set. During the ranking, the number of fixed parameters increases with the number of environmental conditions, while some errors in the hypothetical network structure may pass undetected, due to a maintained dynamical behaviour.

Place, publisher, year, edition, pages
Ireland: Elsevier, 2002. Vol. 66, no 1-2, p. 31-41
Keywords [en]
Reverse engineering; genetic regulatory networks; genetic algorithm; gene expression data; experimental design
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-40648DOI: 10.1016/S0303-2647(02)00019-9PubMedID: 12204440Scopus ID: 2-s2.0-0036627676OAI: oai:DiVA.org:oru-40648DiVA, id: diva2:778054
Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2018-01-30Bibliographically approved

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Repsilber, Dirk

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