Graded and Binary Responses in Stochastic Gene Expression

Abstract

Recently, several theoretical and experimental studies have been undertaken to probe the effect of stochasticity on gene expression (GE). In experiments, the GE response to an inducing signal in a cell, measured by the amount of mRNAs/proteins synthesized, is found to be either graded or binary. The latter type of response gives rise to a bimodal distribution in protein levels in an ensemble of cells. One possible origin of binary response is cellular bistability achieved through positive feedback or autoregulation. In this paper, we study a simple, stochastic model of GE and show that the origin of binary response lies exclusively in stochasticity. The transitions between the active and inactive states of the gene are random in nature. Graded and binary responses occur in the model depending on the relative stability of the activated and deactivated gene states with respect to that of mRNAs/proteins.The theoretical results on binary response provide a good description of the ``all-or-none'' phenomenon observed in an eukaryotic system.

to be published in Physical Biology

References in corpus (3)

• Noise-based switches and amplifiers for gene expression
• Stochastic gene expression as a many body problem
• A Cooperative Stochastic Model of Gene Expression

Cited by in corpus (18)

• Analytical distributions for stochastic gene expression
• Stochastic phenotype transition of a single cell in an intermediate region of gene-state switching
• Connecting protein and mRNA burst distributions for stochastic models of gene expression
• Positive Feedback, Stochasticity and Genetic Competence
• Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks
• Post-transcriptional regulation of noise in protein distributions during gene expression
• Regularity of invariant densities for 1D-systems with random switching
• Dichotomous noise models of gene switches
• Stochastic model of transcription factor-regulated gene expression
• Conversion of graded to binary response in an activator-repressor system
• Construction of stochastic hybrid path integrals using "quantum-mechanical'' operators
• First-passage processes and the target-based accumulation of resources
• Control of noise in gene expression by transcriptional reinitiation
• Synchronization of stochastic hybrid oscillators driven by a common switching environment
• Spin coherent states and stochastic hybrid path integrals
• Emergent phenomena in living systems: a statistical mechanical perspective
• Noise in gene expression may be a choice of cellular system
• Analytical results for a stochastic model of gene expression with arbitrary partitioning of proteins