Modeling gene-regulatory networks using evolutionary algorithms and distributed computing

Abstract

Living organisms regulate the expression of genes using complex interactions of transcription factors, messenger RNA and active protein products. Due to their complexity, gene-regulatory networks are not fully understood, however, various modeling approaches can be used to gain insight into their function and operation. This paper describes an ongoing study to use evolutionary algorithms to create computational models of gene-regulatory networks based on observed microarray data. Because of the computational requirements of this approach (which requires the discovery ofgene network topologies), it is critical that it is implemented on a computing platform capable of delivering significant compute power. We discuss how this can be achieved using distributed and grid computing technology. In particular we investigate how Condor and JavaSpaces technology is suited to the requirements of our modeling approach.

Reconstructing biological gene regulatory networks: where optimization meets big data

“…Gene regulatory networks are not fully understood [115] and the reconstruction of biological GRNs is one of the most complex tasks in bioinformatics [82]. Using expression data to reconstruct gene regulatory networks is one of the most important challenges for research in systems biology [73] and an active area of research [40].…”

“…Swain et al [115] illustrated the importance of topology by using a caterpillar and butterfly analogy, where the two insects contain the same genes, the connectivity of which is changed during the crystalline phase resulting in the physical difference between the two. In some cases the topology of the network is more important than the parameterisation as the structure can determine the dynamic behaviour of the network [40,115].…”

“…In some cases the topology of the network is more important than the parameterisation as the structure can determine the dynamic behaviour of the network [40,115]. For some cases different parameters settings in network can lead to the same behaviour for small network motifs, for example some network motifs that produce oscillations and toggle switches contain many local parameter optima that produce the same network dynamics [117][118][119].…”

“…These algorithms are able to deal with large search spaces [82] and 3, and are therefore well suited to network reconstruction problems [115]. In these algorithms, a possible solution to the optimization problem is represented as an individual, which is in a population of individuals.…”

Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role

“…Several examples of parallel implementations exist in evolutionary methods for GRN modelling, (Daisuke & Horton, 2006;Fomekong-Nanfack et al, 2007;Imade et al, 2004;2003;Spieth, Streichert, Speer & Zell, 2005a). These correspond to both grid and cluster systems, while parallel frameworks for analysis have been implemented and are publicly available (Spieth et al, 2006;Swain et al, 2005).…”