Making sense of the metabolome using evolutionary computation: seeing the wood with the trees.

One should perhaps start off by asking the question, 'But what wood is it we want to see?' There are so many trees that make up the wood; within a post-genomics context, genes, transcripts, proteins, and metabolites are the more tangible ones. Rather than studying these components in isolation, a more holistic approach is to unravel the interactions between the myriad of subcellular components and this is vital to systems biology. Moreover, this will help define the phenotype of the organism under investigation. Metabolomics is complementary to transcriptomics and proteomics, and despite the immense metabolite diversity observed in plants, metabolomics has been embraced by the plant community and in particular for studying metabolic networks. Whilst post-genomic science is producing vast data torrents, it is well known that data do not equal knowledge and so the extraction of the most meaningful parts of these data is key to the generation of useful new knowledge. A metabolomics experiment is guaranteed to generate thousands of data points (e.g. samples multiplied by the levels of particular metabolites) of which only a handful might be needed to describe the problem adequately. Evolutionary computational-based methods such as genetic algorithms and genetic programming are ideal strategies for mining such high-dimensional data to generate useful relationships, rules, and predictions. This article describes these techniques and highlights their usefulness within metabolomics.