Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression

Abstract

In any given cell, thousands of genes are expressed and work in concert to ensure the cell's function, fitness, and survival. Each gene, in turn, must be expressed at the proper time and in the proper amounts to ensure the appropriate functional outcome. The regulation and expression of some genes are highly robust; their expression is controlled by invariable expression programs. For instance, developmental gene expression is extremely similar in a given cell type from one individual to another. The expression of other genes is more variable: Their levels are noisy and are different from cell to cell and from individual to individual. This can be highly beneficial in physiological responses to outside cues and stresses. Recent advances have enabled the analysis of differential gene expression at a systems level. Gene regulatory networks (GRNs) involving interactions between large numbers of genes and their regulators have been mapped onto graphic diagrams that are used to visualize the regulatory relationships. The further characterization of GRNs has already uncovered global principles of gene regulation. Together with synthetic network biology, such studies are starting to provide insights into the transcriptional mechanisms that cause robust versus stochastic gene expression and their relationships to phenotypic robustness and variability. Here, we discuss GRNs and their topological properties in relation to transcriptional and phenotypic outputs in development and organismal physiology.

Figure 1.

Robust or stochastic gene expression can generate diverse phenotypes. (Left) Hypothetical gene expression profiles for a population of cells or an individual cell over time (time). Network diagrams are shown to highlight the relationship of gene expression and network dynamics. Cells colored differently reflect different phenotypic fates.

Figure 2.

Common network architectures in GRNs. Examples of modules and motifs are shown. (A) GRN modules. (Left) A TF module; (right) a gene module. (B) GRN motifs. A, B, and C represent three genes that interact in a GRN. FFLs can be divided into two types—coherent and incoherent. In coherent FFLs, the effects of A on C from direct and indirect paths are the same. The type I coherent FFL is the most common (shown here), where A activates B and C, and B activates C. In incoherent FFLs, the effects from A on C are opposite. In this type II incoherent FFL, A represses C, and it activates C by repressing the repressor B. (Right) A feedback loop (FBL) is illustrated in which A activates B, B activates C, and the product of C negatively regulates A.

Figure 3.

Redundancy can be conferred by different mechanisms. (A) Genes can be controlled by redundant enhancers: a primary, proximal enhancer and a distal, secondary shadow enhancer. (B) Different TFs from the same family can bind the same cis-regulatory site and control the gene redundantly. (C) Multiple different types of TFs can redundantly control gene expression by together binding to a cis-regulatory module (e.g., enhancer).

Figure 4.

Stochasticity in gene expression underlies partial phenotypic penetrance. Summary of data from Raj and colleagues (Raj et al. 2010) explaining partial penetrance of defects in skn-1 mutants. A network controlling intestinal development in C. elegans in wild-type (left) and skn-1 mutants (right). END-1 expression levels must reach a threshold to activate expression of elt-2 and downstream intestinal expression programs.

Figure 5.

Entry and exit from competence relies on stochastic gene expression induced by a circuit composed of ComK, ComS, and the protease complex MecA ClpP/C. (A) ComK is necessary and sufficient to induce competence in bacteria. The ComK protein is degraded by the MecA ClpP/C protease complex. ComK negatively regulates the expression of ComS, which, in turn, negatively regulates the protease complex by competing for binding with ComK. (B) The entire circuit cycles over time, resulting in changing levels of ComK, and cycling between competent and vegetative states. (C) The changes that occur over time to network components that result in entry into the competence state.