Inferring transcriptional regulators for sets of co-expressed genes by multi-objective evolutionary optimization

Higher organisms are able to respond to continuously changing external conditions by transducing cellular signals into specific regulatory programs, which control gene expression states of thousands of different genes. One of the central problems in understanding gene regulation is to decipher how combinations of transcription factors control sets of co expressed genes under specific experimental conditions. Existing methods in this field mainly focus on sequence aspects and pattern recognition, e.g., by detecting cis-regulatory modules (CRMs) based on gene expression profiling data. We propose a novel approach by combining experimental data with a priori knowledge of respective experimental conditions. These various sources of evidence are likewise considered using multi-objective evolutionary optimization. In this work, we present three objective functions that are especially designed for stimulus-response experiments and can be used to integrate a priori knowledge into the detection of gene regulatory modules. This method was tested and evaluated on whole-genome microarray measurements of drug-response in human hepatocytes.