Abstract

In this paper, a unified approach to infer gene regulatory networks using the S-system model is proposed. In order to discover the structure of large-scale gene regulatory networks, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using genetic programming and recursive least square estimation. Then the exact values of the parameters will be calculated using a multi-dimensional optimization algorithm. Both downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimization. Simulation results using both synthetic data and real microarray measurements demonstrate the effectiveness of the proposed methods

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