Chemical genomic profiling of biological networks using graph theory and combinations of small molecule perturbations

Genome-wide measurements of multiple experimental samples yield rich fingerprints for comparison and interpretation. Here, a two-dimensional matrix of the cellular effects of all possible pairwise combinations of 24 small molecules, each with a different structure and bioactivity, was used to profile otherwise isogenic deletion strains of the yeast Saccharomyces cerevisiae. Using principles from graph theory, we derived a discrete model of the data for each strain by encoding the information in the form of a binary adjacency matrix. This matrix was used to construct a graph composed of nodes representing small molecules and edges connecting combinations that inhibited cell cycle progression. Computation of a set of graph theoretic descriptors for each chemical genetic network provided a topological fingerprint that showed genotype-dependent fluctuations. Because the structure of the genetic network determines the structure of the chemical genetic network, multidimensional chemical genomic profiling can be used for the characterization of perturbations in biological networks or the networks themselves. This application of small molecules could be useful for discerning the molecular basis of highly complex biological phenotypes, including those involved in the susceptibility to or etiology of human disease.