Our lab studies topics in evolution, experimentally and theoretically.
We measured the cost and benefit of protein expression in E. coli, and demonstrated in evolutionary experiments that protein levels evolve to maximize fitness within a few hundred generations [Dekel 2005].
We studied the origin of modularity in biological systems. Modularity can rapidly evolve when conditions change in a way that requires the same set of tasks in different combinations [Kashtan 2005, Kashtan 2007, Parter 2007, Parter 2008]. In this way, evolution learns from the past to generalize to new environments.
We study evolution when multiple objectives are at play. When a biological system needs to perform more than one task, it faces a fundamental tradeoff: no phenotype can be optimal at all tasks. We used Pareto optimality theory to show that this leads to simplicity in the range of phenotypes: natural selection to phenotypes that lie on low-dimensional polygons and polyhedra , such as lines, triangles and tetrahedrons [Shoval 2012]. The vertices, known as archetypes, are the phenotypes optimal at a single task. Evolutionary tasks can be inferred from data by the features enriched at the points closest to the archetypes. We applied this approach to systems ranging from animal and fossil morphology to biological circuits and cancer gene expression. Algorithms for Pareto analysis fo biological data are available.