Informatic approaches to evolutionary systems biology

Abstract

The sheer complexity of evolutionary systems biology requires us to develop more sophisticated tools for analysis, as well as more probing and biologically relevant representations of the data. My research has focused on three aspects of evolutionary systems biology. I ask whether a gene's position in the human metabolic network affects the degree to which natural selection prunes variation in that gene. I estimated the selective constraint (the ratio of non-synonymous to synonymous nucleotide substitutions) on 80.2% of the genes in the metabolic network using a maximum likelihood model of codon evolution and compared this value to the betweenness centrality of each enzyme. Second, I have focused on the evolution of metabolic systems in the presence of gene and genome duplication. I show that increases in a particular gene's copy number are correlated with limiting metabolic flux in the reaction associated with that gene. Finally, I have investigated the proliferative cell programs present in 6 different cancers (breast, colorectal, gastrointestinal, lung, oral squamous and prostate cancers). I found an overabundance of genes that share expression between cancer and embryonic tissue and that these genes form modular units within regulatory, proteininteraction, and metabolic networks. This despite the fact that these genes, as well as the proteins they encode and reactions they catalyze show little overlap among cancers, suggesting parallel independent reversion to an embryonic pattern of gene expression.