In this paper we present a new methodology, based on genetic algorithms and multiple linearregression, for discovering regulation mechanisms responsible for observed time series inbiological networks. The modeling framework employed is called Metabolic P systems; they aredeterministic and time-discrete dynamical systems proposed as an effective alternative to ordinarydifferential equations for modeling biochemical systems. Our methodology is here successfullyapplied to the mitotic oscillator in early amphibian embryos. Starting from the time series ofsubstances involved in this system, we are able to reconstruct an MP system reproducing theobserved dynamics, where the regulatory components were discovered by our evolutionarymethodology. In particular, genetic algorithms are used as a variable selection technique toidentify the best representation of any regulation function in terms of some given primitivefunctions.

From time series to biological network regulations: an evolutionary approach

CASTELLINI, ALBERTO;Zucchelli, Mauro;BUSATO, MIRKO;MANCA, Vincenzo
2013

Abstract

In this paper we present a new methodology, based on genetic algorithms and multiple linearregression, for discovering regulation mechanisms responsible for observed time series inbiological networks. The modeling framework employed is called Metabolic P systems; they aredeterministic and time-discrete dynamical systems proposed as an effective alternative to ordinarydifferential equations for modeling biochemical systems. Our methodology is here successfullyapplied to the mitotic oscillator in early amphibian embryos. Starting from the time series ofsubstances involved in this system, we are able to reconstruct an MP system reproducing theobserved dynamics, where the regulatory components were discovered by our evolutionarymethodology. In particular, genetic algorithms are used as a variable selection technique toidentify the best representation of any regulation function in terms of some given primitivefunctions.
Biological time series; Biological networks; MP systrms; Evolutionary algorithms; Dynamical inverse problems
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/570549
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