Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ∼80% of the known relationships without any false positives

Bridging taxonomic and disciplinary divides in infectious disease

Citation: Borer, E.T., & Antonovics, J. (2011). Bridging Taxonomic and Disciplinary Divides in Infectious Disease. EcoHealth 8, 261–267. https://doi.org/10.1007/s10393-011-0718-6Pathogens traverse disciplinary and taxonomic boundaries, yet infectious disease research occurs in many separate disciplines including plant pathology, veterinary and human medicine, and ecological and evolutionary sciences. These disciplines have different traditions, goals, and terminology, creating gaps in communication. Bridging these disciplinary and taxonomic gaps promises novel insights and important synergistic advances in control of infectious disease. An approach integrated across the plant-animal divide would advance our understanding of disease by quantifying critical processes including transmission, community interactions, pathogen evolution, and complexity at multiple spatial and temporal scales. These advances require more substantial investment in basic disease research