Conversion of graded to binary response in an activator-repressor system.

Appropriate regulation of gene expression is essential to ensure that protein synthesis occurs in a selective manner. The control of transcription is the most dominant type of regulation mediated by a complex of molecules such as transcription factors. In general, regulatory molecules are of two types: activator and repressor.

Positive feedback, stochasticity and genetic competence.

A single gene, regulating its own expression via a positive feedback loop, constitutes a common motif in gene regulatory networks and signalling cascades. Recent experiments on the development of competence in the bacterial population B. subtilis show that the autoregulatory genetic module by itself can give rise to two types of cellular states.

Stochastic model of transcription factor-regulated gene expression.

We consider a stochastic model of transcription factor (TF)-regulated gene expression. The model describes two genes, gene A and gene B, which synthesize the TFs and the target gene proteins, respectively. We show through analytic calculations that the TF fluctuations have a significant effect on the distribution of the target gene protein levels when the mean TF level falls in the highest sensitive region of the dose-response curve.

Noise characteristics of feed forward loops.

A prominent feature of gene transcription regulatory networks is the presence in large numbers of motifs, i.e., patterns of interconnection, in the networks.

Graded and binary responses in stochastic gene expression.

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.