Biology subjects

Uri Obolski

Publications and source records attributed to Uri Obolski.

3 recordsLinked to original sources

bioRxiv2016

With a little help from my friends: Cooperation can accelerate crossing of adaptive valleys

Natural selection favors changes that lead to genotypes possessing high fitness. A conflict arises when several mutations are required for adaptation, but each mutation is separately deleterious. The process of a population evolving from a genotype encoding for a local fitness maximum to a higher fitness genotype is termed an adaptive peak shift.\n\nHere we suggest cooperative behavior as a factor that can facilitate adaptive peak shifts. We model cooperation in a public goods scenario, wherein each individual contributes resources that are later equally redistributed among all cooperating individuals. We use mathematical modeling and stochastic simulations to study the effect of cooperation on peak shifts in well-mixed populations and structured ones. Our results show that cooperation can accelerate the rate of complex adaptation. Furthermore, we show that cooperation increases the population diversity throughout the peak shift process, thus increasing the robustness of the population to drastic environmental changes.\n\nOur work could help explain adaptive valley crossing in natural populations and suggest that the long term evolution of a species depends on its social behavior.

Evolutionary Biology↗

bioRxiv2016

Decoupling global biases and local interactions between cell biological variables

Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization. Here, we describe DeBias, a computational method to quantify and decouple global bias from local interactions between variables by modeling the observed co-localization as the cumulative contribution of a global and a local component. We showcase four applications of DeBias in different areas of cell biology, and demonstrate that the global bias encapsulates fundamental mechanistic information of cellular behavior. The DeBias software package is freely accessible online via a web-server at https://debias.biohpc.swmed.edu.

Cell Biology↗

bioRxiv2015

Predicting microbial relative growth in a mixed culture from growth curve data

Estimates of microbial fitness from growth curves are inaccurate. Rather, competition experiments are necessary for accurate estimation. But competition experiments require unique markers and are difficult to perform with isolates derived from a common ancestor or non-model organisms. Here we describe a new approach for predicting relative growth of microbes in a mixed culture utilizing mono- and mixed culture growth curve data. We validated this approach using growth curve and competition experiments with E. coli. Our approach provides an effective way to predict growth in a mixed culture and infer relative fitness. Furthermore, by integrating several growth phases, it provides an ecological interpretation for microbial fitness.

Evolutionary Biology↗