Abstract
In order to understand and infer a principle underlying biological phenomena, it is necessary to handle massive data of expression patterns, kinetics, and metabolism, so that plausible regulative mechanisms are revealed. The in silico sampling and screening method described in this paper automatically infers possible regulatory network structures using several gene expression profiles. In an experimental evaluation of the feasibility of using this method, each of the possible topologies of three-unit networks were tested exhaustively. After a genetic algorithm was used to identify the parameter set for topology, the plausible topologies were selected by using mutant gene expression data. The experimental results demonstrate that the method can derive a set of possible network structures that includes the correct one.
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Morohashi, M., Kitano, H. (1999). Identifying Gene Regulatory Networks from Time Series Expression Data by in silico Sampling and Screening. In: Floreano, D., Nicoud, JD., Mondada, F. (eds) Advances in Artificial Life. ECAL 1999. Lecture Notes in Computer Science(), vol 1674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48304-7_66
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DOI: https://doi.org/10.1007/3-540-48304-7_66
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