Our lab is interested in understanding the system-level architecture of genetic networks and the interplay between their design and the evolutionary process.

 

Gene and drug networks

We are combining theoretical and experimental approaches to study epistasis networks – networks that describe how perturbations (mutations or drugs) in a given biological system are combined to aggravate or alleviate the phenotypic consequences of each other. Such epistatic interactions, fundamental in various evolutionary processes, also helps elucidating the functional organization of complex genetic architectures. We are developing quantitative automated experimental tools based on bioluminescence and fluorescence measurements to achieve en mass, yet very accurate, quantification of epistatic interaction networks in bacteria and yeast. In a systematic study of epistasis between mutations and environmental stresses in Escherichia coli we found that, in contrast to the perception that stress generally reduces the organism's ability to tolerate mutations, there exist stresses that do the opposite – that is they alleviate the average effect of deleterious mutations (Kishony & Leibler, 2003). More recently, we have used the computational method of flux balance analysis (FBA) to study the epistasis network of yeast metabolism (Segre’ et al, 2005). Our results show that the epistasis network posses a special property, which we term “monochromaticity”, whereby functional gene modules interact with each other with purely aggravating or purely alleviating epistatic links. This property extends the concept of epistasis from the gene-gene level to the system level. The new definition for identifying functional modules is implemented in our Prism algorithm. In drug networks, the same conceptual method allow classification of drugs by their mechanism of actions based only on the properties of their mutual interactions (Yeh et al, 2006).

 

Evolutionary adaptation and drug resistance

Our evolution research is concentrating on adaptation in asexual organisms. In collaboration with Daniel Hartl, we demonstrated a simple equivalence principle which, at the limit of high mutation rates and population size, provides a projection of the complex adaptation process onto a simple two-dimensional parameter space of an effective mutational size and an effective mutational rate (Hegreness et al, 2006). We are using similar tools to study the impact of multi-drug combinations on the evolution of drug resistance (Chait et al, 2007).