Gabor Balazsi

Spatiotemporal stochastic resonance and its consequences in neural model systems.

The realization of spatiotemporal stochastic resonance is studied in a two-dimensional FitzHugh-Nagumo system, and in a one-dimensional system of integrate-and-fire neurons. We show that spatiotemporal stochastic resonance occurs in these neural model systems, independent of the method of modeling. Moreover, the ways of realization are analogous in the two model systems. The biological implications and open questions are discussed. (c) 2001 American Institute of Physics.

Optimality and adaptation of phenotypically switching cells in fluctuating environments.

Stochastic switching between alternative phenotypic states is a common cellular survival strategy during unforeseen environmental fluctuations. Cells can switch between different subpopulations that proliferate at different rates in different environments. Optimal population growth is typically assumed to occur when phenotypic switching rates match environmental switching rates. However, it is not well understood how this optimum behaves as a function of the growth rates of phenotypically different cells. In this study, we use mathematical and computational models to test how the actual parameters associated with optimal population growth differ from those assumed to be optimal. We find that the predicted optimum is practically always valid if the environmental durations are long. However, the regime of validity narrows as environmental durations shorten, especially if subpopulation growth rate differences differ from each other (are asymmetric) in two environments. Furthermore, we study the fate of mutants with switching rates previously predicted to be optimal. We find that mutants which match their phenotypic switching rates with the environmental ones can only sweep the population if the assumed optimum is valid, but not otherwise.

A common repressor pool results in indeterminacy of extrinsic noise.

For just over a decade, stochastic gene expression has been the focus of many experimental and theoretical studies. It is now widely accepted that noise in gene expression can be decomposed into extrinsic and intrinsic components, which have orthogonal contributions to the total noise. Intrinsic noise stems from the random occurrence of biochemical reactions and is inherent to gene expression. Extrinsic noise originates from fluctuations in the concentrations of regulatory components or random transitions in the cells state and is imposed to the gene of interest by the intra- and extra-cellular environment. The basic assumption has been that extrinsic noise acts as a pure input on the gene of interest, which exerts no feedback on the extrinsic noise source. Thus, multiple copies of a gene would be uniformly influenced by an extrinsic noise source. Here, we report that this assumption falls short when multiple genes share a common pool of a regulatory molecule. Due to the competitive utilization of the molecules existing in this pool, genes are no longer uniformly influenced by the extrinsic noise source. Rather, they exert negative regulation on each other and thus extrinsic noise cannot be determined by the currently established method.

Correction: Phenotypic heterogeneity promotes adaptive evolution

[This corrects the article DOI: 10.1371/journal.pbio.2000644.].

Spatiotemporal Stochastic Resonance and its consequences in a neural system

Biological neurons are good examples of a threshold device—this is why neural systems are in the focus when looking for realization of Stochastic Resonance (SR) and Spatiotemporal Stochastic Resonance (STSR) phenomena. There are two different ways to simulate neural systems—one based on differential equations, the other based on a simple threshold model. In this talk the effect of noise on neural systems will be discussed using both ways of modelling. The results so far suggest that SR and STSR do occur in models of neural systems. However, how significant is the role played by these phenomena and what implications might they have on neurobiology is still a question.