Convergent genetic adaptation of in minimal media leads to pleiotropic divergence.

Adaptation in an environment can either be beneficial, neutral or disadvantageous in another. To test the genetic basis of pleiotropic behaviour, we evolved six lines of independently in environments where glucose and galactose were the sole carbon sources, for 300 generations. All six lines in each environment exhibit convergent adaptation in the environment in which they were evolved.

Distribution of fitness effects of mutations obtained from a simple genetic regulatory network model.

Beneficial and deleterious mutations change an organism's fitness but the distribution of these mutational effects on fitness are unknown. Several experimental, theoretical, and computational studies have explored this question but are limited because of experimental restrictions, or disconnect with physiology. Here we attempt to characterize the distribution of fitness effects (DFE) due to mutations in a cellular regulatory motif.

Mathematical modeling of movement on fitness landscapes.

Background: Movement of populations on fitness landscapes has been a problem of interest for a long time. While the subject has been extensively developed theoretically, reconciliation of the theoretical work with recent experimental data has not yet happened. In this work, we develop a computational framework and study evolution of the simplest transcription network between a single regulator, R and a single target protein, T.

Optimal parameter values for the control of gene regulation.

How does a transcription network arrive at the particular values of biochemical interactions defining it? These interactions define DNA-transcription factor interaction, degradation rates of proteins, promoter strengths, and communication of the environmental signal with the network. What is the structure of the fitness landscape that is defined by the space that these parameters can take on? To answer these questions, we simulate the simplest regulatory network: a transcription factor, R, and a target protein, T. We use a cost-benefit analysis to evolve the network and eventually arrive at values of parameters which maximize fitness.

Analysis of a strategy for cooperating cells to survive the presence of cheaters.

Cooperation benefits individual cells in a microbial population by helping accomplish tasks which are difficult or non-beneficial for individuals in the population to carry out by themselves. Hence, numerous examples exist of bacteria cooperating and working towards a common objective. The sharing of a common public good via quorum sensing is one of the ways of cooperation among individuals of many microbial populations.