Learning regulation functions of metabolic systems by artificial neural networks

ABSTRACT

Metabolic P systems, also called MP systems, are discrete dynamical systems which proved to be effective for modeling biological systems. Their dynamics is generated by means of a metabolic algorithm based on "flux regulation functions". A significant problem related to the generation of MP models from experimental data concerns the synthesis of these functions. In this paper we introduce a new approach to the synthesis of MP fluxes relying on neural networks as universal function approximators, and on evolutionary algorithms as learning techniques. This methodology is successfully tested in the case study of mitotic oscillator in early amphibian embryos.

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INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES
      Subjects: Biology and genetics

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.2 Approximation
          Subjects: Nonlinear approximation

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.5 Model Development
          Subjects: Modeling methodologies

General Terms:
Algorithms, Experimentation

Keywords:
biological modeling, evolutionary algorithms, memetic algorithms, metabolic systems, neural networks, optimization, p systems, regression, systems biology