Biology subjects

Timofeyev, I.

Publications and source records attributed to Timofeyev, I..

3 recordsLinked to original sources

bioRxiv2024

Role of Non-Exponential Reversal times in Aggregation Models of Bacterial Populations

In this paper, we consider 1D agent-based and kinetic models of aggregation with reversals. In particular, we fit a Gamma distribution to represent the run times in myxobacteria and analyze numerically the importance of non-exponential reversal times. We demonstrate that non-exponential reversal times aid aggregation and result in tighter aggregates. We compare and contrast the behavior of agent-based and kinetic models, and also consider kinetic models with aggregation driven by chemotaxis. Thus, incorporating non-exponential reversal times into models of aggregation can be particularly important for reproducing experimental data, such as aggregate persistence and dispersal.

biophysics↗

bioRxiv2020

Fitness Estimation for Genetic Evolution of Bacterial Populations

In this paper we develop and test algorithmic techniques to estimate genotypes fitnesses by analysis of observed daily frequency data monitoring the long-term evolution of bacterial populations. In particular, we develop a non-linear least squares approach to estimate selective advantages of emerging new mutant strains in locked-box stochastic models describing bacterial genetic evolution similar to the celebrated Lenski experiment on Escherichia Coli. Our algorithm first analyses emergence of new mutant strains for each individual trajectory. For each trajectory our analysis is progressive in time, and successively focuses on the first mutation event before analyzing the second mutation event. The basic principle applied here is to minimize (for each trajectory) the mean squared errors of prediction w(t) - W(t) where the observed white cell frequencies w(t) are predicted by W(t), which is computed as the conditional expectation of w(t) given the available information at time (t - 1). The pooling of all selective advantages estimates across all trajectories provides histograms on which we perform a precise peak analysis to compute final estimates of selective advantages. We validate our approach using ensembles of simulated trajectories.

evolutionary biology↗

bioRxiv2018

Boundary-Driven Emergent Spatiotemporal Order in Growing Microbial Colonies

We introduce a tractable stochastic spatial Moran model to explain experimentally-observed patterns of rod-shaped bacteria growing in rectangular microfluidic traps. Our model shows that spatial patterns can arise as a result of a tug-of-war between boundary effects and modulations of growth rate due to cell-cell interactions. Cells align parallel to the long side of the trap when boundary effects dominate. However, when the magnitude of cell-cell interactions exceeds a critical value, cells align orthogonally to the traps long side. Our model is analytically tractable, and completely solvable under a mean-field approximation. This allows us to elucidate the mechanisms that govern the formation of population-level patterns. The model can be easily extended to examine various types of interactions that can shape the collective behavior in bacterial populations.

biophysics↗