A number of experimental exigencies have so far limited a mechanistic and predictive view on evolution. Evolutionary intermediates and their fitnesses are critical but hard to observe, the relation between possible genotypic changes and the corresponding phenotypic and fitness effects are unclear, the dependence of the evolution of complex traits on environmental variability further increases the complexity of these relations and the population dynamics, to the point that even elementary fitness tradeoffs between environment and genetic constraint are unknown for essentially any phenotypic trait. Our aim is to disentangle these factors using simple model systems that are genetically engineered.
We used published measurements of Escherichia coli mutants to predict intermediate phenotypes. Using this approach, we could simulate the evolution of a small genetic network by computer. Following the duplication of one of the components in the network, a new protein-DNA interaction develops via the accumulation of point mutations and selection. The resulting evolutionary pathways revealed a high potential to obtain a new regulatory interaction, in which neutral drift plays an almost negligible role. This study provided a mechanistic rationale for why such rapid divergence can occur and under which minimal selective conditions. In addition it yielded a quantitative prediction for the minimum number of essential mutations.
Recent research topics include: tradeoffs and optimality in the evolution of gene regulation, multi-peaked fitness landscapes and its relation to reciprocal sign epistasis, and the evolution of small regulatory networks.
Publications:
- "Empirical fitness landscapes reveal accessible evolutionary paths" - Nature 445:383-386 (2007)
- "Evolutionary potential of a duplicated repressor-operator pair: simulating pathways using mutation data" - PLoS Comput Biol. 2:e58 (2006)
Persons Involved:
- Daan Kiviet
- Marjon de Vos
- Manjunatha Kogenaru
- Alexandre Dawid
- Philippe Nghe