Abstract
Author SummaryIn the simplest scenario, a gene is expressed when an activator protein binds to its regulatory sequence, and is silenced when the regulatory sequence is bound by a repressor. Many genes are regulated by both activators and repressors, with the response determined by the combined influence of both factors. When the response is monostable graded, expression is finely tuned to a level that reflects the proportion of the bound activator to the bound repressor. Monostable graded systems allow cells to respond precisely to stimuli. If the response is bistable, the response of each cell depends on whether the activator or the repressor wins. Bistable regulation results in the same gene being expressed in some cells and silenced in others, an outcome that promotes cellular differentiation. It remains unclear, however, how different genetic regulatory structures code for monostable graded and bistable responses. We modeled mathematically the behavior of repressors as they bind to and spread their inhibitory effect along genes and found that the spatial distribution of the binding sites determines which response is chosen. If repressors bind both upstream and downstream of the coding sequence, the response is bistable. If they bind only to one side of the coding sequence, the response is monostable. We confirmed our theoretical findings using synthetic genetic constructs in yeast. These findings help to explain how variations in the location of regulatory elements can lead to cellular differentiation and adaption to varying environments.
Highlights
Graded and switch-like responses reflect fundamental aspects of the functioning of regulatory networks
Positive feedback loops in transcriptional or protein kinase networks have been increasingly recognized as a driving force of cellular differentiation [10,11]
We examined whether the spatial distribution of activator and repressor binding sites influences gene expression to become monostable or bistable
Summary
Graded and switch-like responses reflect fundamental aspects of the functioning of regulatory networks. When the signal strength reaches a threshold value, the switch-like response is often manifested in ON and OFF states within a cell population. This binary response can be induced by positive feedback loops capable of generating bistability, but many other mechanisms can support it by rendering the underlying processes more nonlinear and stochastic [3,4,5,6,7,8,9]. Positive feedback loops in transcriptional or protein kinase networks have been increasingly recognized as a driving force of cellular differentiation [10,11]. The components of these networks are dissolved in the cytoplasm or nucleoplasm, and typically have a spatially homogeneous distribution
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