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A toggle switch consists of two genes that mutually repress each other. This regulatory motif is active during cell differentiation and is thought to act as a memory device, being able to choose and maintain cell fate decisions. In this contribution, we study the stability and dynamics of a two-stage gene expression switch within a probabilistic framework inspired by the properties of the Pu/Gata toggle switch in myeloid progenitor cells. We focus on low mRNA numbers, high protein abundance and monomeric transcription factor binding. Contrary to the expectation from a deterministic description, this switch shows complex multi-attractor dy- namics without autoactivation and cooperativity. Most importantly, the four attractors of the system, which only emerge in a probabilistic two-stage description, can be identified with committed and primed states in cell differentiation. We first study the dynamics of the system and infer the mechanisms that move the system between attractors using both the quasi-potential and the probability flux of the system. Second, we show that the residence times of the system in one of the committed attractors are geometrically distributed and provide an analytical expression of the distribution. Most importantly we find that the mean residence time increases linearly with the mean protein level. Finally, we study the implications of this distribution for the stability of a switch and discuss the influence of the stability on a specific cell differentiation mechanism. Our model explains lineage priming and proposes the need of either high protein numbers or long term modifications such as chromatin remodeling to achieve stable cell fate decisions. Notably we present a system with high protein abundance that nevertheless requires a probabilistic description to exhibit multistability, complex switching dynamics and lineage priming.
In the last years, tens of thousands gene expression profiles for cells of several organisms have been monitored. Gene expression is a complex transcriptional process where mRNA molecules are translated into proteins, which control most of the cell f
Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from si
The arabinose utilization system of E. coli displays a stochastic all or nothing response at intermediate levels of arabinose, where the population divides into a fraction catabolizing the sugar at a high rate (ON state) and a fraction not utilizing
Expression of cellular genes is regulated by binding of transcription factors to their promoter, either activating or inhibiting transcription of a gene. Particularly interesting is the case when the expressed protein regulates its own transcription.
The bulk of stochastic gene expression models in the literature do not have an explicit description of the age of a cell within a generation and hence they cannot capture events such as cell division and DNA replication. Instead, many models incorpor