A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene.

This manuscript presents a comparison of noise properties exhibited by two stochastic binary models for: (i) a self-repressing gene; (ii) a repressed or activated externally regulating one. The stochastic models describe the dynamics of probability distributions governing two random variables, namely, protein numbers and the gene state as ON or OFF. In a previous work, we quantify noise in protein numbers by means of its Fano factor and write this quantity as a function of the covariance between the two random variables. Then we show that distributions governing the number of gene products can be super-Fano, Fano or sub-Fano if the covariance is, respectively, positive, null or negative. The latter condition is exclusive for the self-repressing gene and our analysis shows the conditions for which the Fano factor is a sufficient classifier of fluctuations in gene expression. In this work, we present the conditions for which the noise on the number of gene products generated from a self-repressing gene or an externally regulating one are quantitatively similar. That is important for inference of gene regulation from noise in gene expression quantitative data. Our results contribute to a classification of noise function in biological systems by theoretically demonstrating the mechanisms underpinning the higher precision in expression of a self-repressing gene in comparison with an externally regulated one.

Random monoallelic expression: regulating gene expression one allele at a time.

Monoallelic gene expression is a remarkable process in which transcription occurs from only one of two homologous alleles in a diploid cell. Interestingly, between 0.5% and 15% of autosomal genes exhibit random monoallelic gene expression, in which different cells express only one allele independently of the underlying genomic sequence, in a cell type-specific manner. Recently, genome-wide studies have increased our understanding of the cell type-specific incidence of random monoallelic gene expression, and how the imbalance in allelic expression is distinguished within the cell and potentially maintained across cell generations. Monoallelic gene expression is likely generated through stochastic independent regulation of the two alleles upon differentiation, and has varied implications for the cell and organism, in particular with respect to disease.