OBJECTIVE: 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, by building computational models it is possible to gain insight into their function and operation. METHODS: Evolutionary algorithms are used to create computational models of gene-regulatory networks based on observed microarray data. These algorithms can be computationally intensive. They will be implemented within an existing grid computing infrastructure, that has been developed for data mining purposes, and which is able to deliver the required compute power. RESULTS: We discuss how models can built 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. CONCLUSIONS: Determining network models of gene-regulatory networks using evolutionary algorithms not only requires considerable computational power, but also a modeling formalism that can explain the underlying dynamics.
OBJECTIVE: 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, by building computational models it is possible to gain insight into their function and operation. METHODS: Evolutionary algorithms are used to create computational models of gene-regulatory networks based on observed microarray data. These algorithms can be computationally intensive. They will be implemented within an existing grid computing infrastructure, that has been developed for data mining purposes, and which is able to deliver the required compute power. RESULTS: We discuss how models can built 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. CONCLUSIONS: Determining network models of gene-regulatory networks using evolutionary algorithms not only requires considerable computational power, but also a modeling formalism that can explain the underlying dynamics.