Abstract

In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.

Highlights

  • A system is not just an assembly of more than two components which have unique function and show timevariant behavior

  • New real-coded genetic algorithm (RCGA), the combination of AREX with just generation gap (JGG), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data

  • We have developed an efficient computational technique based on the RCGA called UNDX + minimal generation gap (MGG) [14,15,16] and have applied to the inference of genetic interaction

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Summary

Introduction

A system is not just an assembly of more than two components which have unique function and show timevariant behavior. The aim of systems biology is to understand how functional properties and behavior of living organisms are brought about by the interactions of their constituents such as genes, proteins, metabolites and so on. The research strategies of systems biology can be divided into the following four fields: 1) System identification: inference of interaction between system components, 2) System analysis: dynamics of time-variant components, 3) System control: control toward the desirable condition of the system, 4) System design: design the system which realizes a certain dynamic or timevariant behavior, which expands to the research field, “Synthetic Biology”. These research developments are strongly supported by mathematical and computational methodologies, such as inference algorithm, statistical analysis, numerical calculation, nonlinear optimization, computer simulation and so on. At the research field of systems identification, in order to infer the interaction among systems components, we have to develop the powerful inferring engine, in which large numbers of real-coded parameter values related to the interactions can be estimated efficiently using experimentally observed time-course data; algorithm of powerful numerical optimization for inverse problem involving more than 100 numbers of numerical parameters

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