Dr. Aviv Bergman

Our research agenda involves multidisciplinary research into quantitative problems of evolutionary biology that can be approached using a combination of computational, mathematical and experimental tools. The focus of the research program is mainly on quantitative aspects of evolution and developmental biology, however, the relationship between these subjects and experimental molecular genetic studies of evolution and development are an integral part of the research efforts. The following provides some recent examples.   

The current research was posed initially by Waddington some 60 years ago, that is, why is phenotypic expression robust, or canalized, to mutations and environmental variations? Occasionally genetic mutations or environmental assaults disrupt the system sufficiently to cause phenotypic disorder. This is the context in which we study an important property of complex genetic systems, namely, their apparent robustness in the face of mutations and environmental variation. We model gene networks in order to generate hypotheses and predictions that are then tested with biological data. These tests are made possible by systematic genomics efforts undertaken in recent years. One example is provided by the project to delete each gene in the yeast Saccharomyces cerevisiae and to assay the effects of each knockout on the expression of other genes. Analyses of microarray and other data in the context of canalization will shed light on the evolution of robustness and other properties of complex genetic systems. Our approach also helps us to understand the contribution to proper development of specific control mechanisms, such as Hsp90 and other chaperones. In the long run such studies will enhance our understanding of the relationship between genetic network architecture and phenotypic fidelity, and the relationship between micro- and macroevolution.  

In a related study we examine the relationship between the topology of a biological network and its functional or evolutionary properties.  It has been suggested that most, if not all, biological networks are ‘scale free.’  That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa.  The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein-protein interactions.  These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology.  One often cited general property is that, when compromised, highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes.   For example, more highly connected proteins are more likely to be lethal when knocked out.  However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect.  Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations.  We use evolutionary simulations of gene networks (theoretically/numerically) as well as experimentally, to address these properties in the context of complex gene networks.

Selected Publications