title: From time series to biological network regulations: an evolutionary approach.
abstract: In this paper we present a new methodology, based on genetic algorithms and multiple linear regression, for discovering regulation mechanisms responsible for observed time series in biological networks. The modeling framework employed is called Metabolic P systems; they are deterministic and time-discrete dynamical systems proposed as an effective alternative to ordinary differential equations for modeling biochemical systems. Our methodology is here successfully applied to the mitotic oscillator in early amphibian embryos. Starting from the time series of substances involved in this system, we are able to reconstruct an MP system reproducing the observed dynamics, where the regulatory components were discovered by our evolutionary methodology. In particular, genetic algorithms are used as a variable selection technique to identify the best representation of any regulation function in terms of some given primitive functions.
authors.affiliation: Center for Biomedical Computing, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy. alberto.castellini@univr.it
authors.forename: Alberto
authors.initials: A
authors.lastname: Castellini
authors.forename: Mauro
authors.initials: M
authors.lastname: Zucchelli
authors.forename: Mirko
authors.initials: M
authors.lastname: Busato
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Manca
mesh_terms: D000465:Algorithms; D000663:Amphibians; D000818:Animals; D004625:Embryo, Nonmammalian; D016014:Linear Models; D053858:Metabolic Networks and Pathways; D008938:Mitosis / Q000502:physiology; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods*