Abstract B.A. McKinney and D. Tian

Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294.

Abstract

An artificial immune system algorithm is introduced in which nonlinear dynamic models are evolved to fi t time series of interacting biomolecules. This grammar-based machine learning method learns the structure and parameters of the underlying dynamic model. In silico immunogenetic mechanisms for the generation of model-structure diversity are implemented with the aid of a grammar, which also enforces semantic constraints of the evolved models. The grammar acts as a DNA repair polymerase that can identify recombination and hypermutation signals in the antibody (model) genome. These signals contain information interpretable by the grammar to maintain model context. Grammatical Immune System Evolution (GISE) is applied to a nonlinear system identification problem in which a generalized (nonlinear) dynamic Bayesian model is evolved to fi t biologically motivated artificial time-series data. From experimental data, we use GISE to infer an improved kinetic model for the oxidative metabolism of 17β-estradiol (E₂), the parent hormone of the estrogen metabolism pathway.

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