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. We use a simple mathematical model to describe the dynamics of gene expression in the lactose utilization network, and use a cost-benefit framework to link the model output to fitness. We simulate mutations by changing model parameters and computing altered fitness to obtain the DFE. We find beneficial mutations distributed exponentially, but distribution of deleterious mutations seems far more complex. In addition, we find neither the starting fitness, nor the exact location on the fitness landscape, affecting these distributions qualitatively. Lastly, we quantify epistasis in our model and find that the distribution of epistatic effects remains qualitatively conserved across different locations on the fitness landscape. Overall, we present a first attempt at exploring the specific statistical features of the fitness landscape associated with a system, by using the specific mathematical model associated with it.

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. RESULTS: Through our simulations, we track evolution of this transcription network and comment on its dynamics and statistics of this movement. Significantly, we report that there exists a critical parameter which controls the ability of a network to reach the global fitness peak on the landscape. This parameter is the fraction of all permissible values of a biochemical parameter that can be accessed from its current value via a single mutation. CONCLUSIONS: Overall, through this work, we aim to present a general framework for analysis of movement of populations (and particularly regulatory networks) on landscapes.

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. We show that for a given topology, multiple parameter sets exist which confer maximal fitness to the cell, and that pairwise correlations exist between parameters in optimal sets. In addition, our results indicate that in the parameter space defining the interactions in a topology, a highly rugged fitness landscape exists.

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. However, cheaters exploit cooperators in a population by not contributing to the production of the common goods but enjoy benefits from goods secreted by cooperating individuals. Thus, compared to cooperators, cheaters exhibit a fitness advantage. This suggests that in a population of cooperators invaded by cheaters, the cheaters should be naturally selected for. Instead, however, cooperation is ubiquitous and occurs in many species at various levels of biological organization. So, the question thus arises that what sort of strategies do these microorganisms employ to survive in the presence of cheaters? We try to answer this question here by mathematical analysis of a strategy used in microbial populations where public benefit received by cheaters is restrained to limit cheater invasion. Our results suggest that individuals exhibiting a little selfishness while still contributing to the population are best suited to resist cheater invasion.