Adaptive Response of a Gene Network to Environmental Changes by Fitness-Induced Attractor Selection

Abstract

Cells switch between various stable genetic programs (attractors) to accommodate environmental conditions. Signal transduction machineries efficiently convey environmental changes to the gene regulation apparatus in order to express the appropriate genetic program. However, since the number of environmental conditions is much larger than that of available genetic programs so that the cell may utilize the same genetic program for a large set of conditions, it may not have evolved a signaling pathway for every environmental condition, notably those that are rarely encountered. Here we show that in the absence of signal transduction, switching to the appropriate attractor state expressing the genes that afford adaptation to the external condition can occur. In a synthetic bistable gene switch in Escherichia coli in which mutually inhibitory operons govern the expression of two genes required in two alternative nutritional environments, cells reliably selected the “adaptive attractor” driven by gene expression noise. A mathematical model suggests that the “non-adaptive attractor” is avoided because in unfavorable conditions, cellular activity is lower, which suppresses mRNA metabolism, leading to larger fluctuations in gene expression. This, in turn, renders the non-adaptive state less stable. Although attractor selection is not as efficient as signal transduction via a dedicated cascade, it is simple and robust, and may represent a primordial mechanism for adaptive responses that preceded the evolution of signaling cascades for the frequently encountered environmental changes.

Towards Establishing Ambient Network Environment

“…It is based on the study of the cell biology [11]: why the biological system is so adaptive to the environmental changes, and robust eventually? In [11], the authors experimentally studied the effects of two mutually inhibitory MURATA: TOWARDS ESTABLISHING AMBIENT NETWORK ENVIRONMENT 1073 operons in E. Coli cells reacting to the lack of a nutrient in their exposed medium.…”

“…In the context of information networks, it corresponds to performance metrics like the system throughput. An essential part of the above equation is the Gaussian noise term η which produces a randomness to escape from the local optimum.It is based on the study of the cell biology [11]: why the biological system is so adaptive to the environmental changes, and robust eventually? In [11], the authors experimentally studied the effects of two mutually inhibitory MURATA: TOWARDS ESTABLISHING AMBIENT NETWORK ENVIRONMENT 1073 operons in E. Coli cells reacting to the lack of a nutrient in their exposed medium.…”

“…In [11], the authors experimentally studied the effects of two mutually inhibitory MURATA: TOWARDS ESTABLISHING AMBIENT NETWORK ENVIRONMENT 1073 operons in E. Coli cells reacting to the lack of a nutrient in their exposed medium. A mathematical model was also proposed in [11], which is essentially stochastic N-differential equations.The basic dynamic behavior can be described as follows. The system state contains all x i and is derived from the concentrations of the messenger RNA (mRNA) molecules in the original model.…”

Biologically Inspired Networking

“…These networks do not necessarily require a preprogrammed connectivity for signal transduction from environmental inputs to DNA. For example, Kashiwagi et al [19] introduce the concept of adaptive response by attractor selection (ARAS), which shows that a gene network composed of mutually inhibitory operons allows its host Escherichia coli cells to adapt to changes in the availability of a nutrient for which no molecular machinery is available for signal transduction. Since ARAS is driven by noise, it is noise-tolerant and can even be stimulated by noise.…”

Distributed multipath routing with packet allocation based on the attractor renewal model

“…One of these protocols is the Adaptive Response Attractor Selection model (ARAS) [6]. ARAS was originally proposed to describe nonlinear dynamics of gene expression of a cell.…”