Multistable Decision Switches for Flexible Control of Epigenetic Differentiation

Abstract

It is now recognized that molecular circuits with positive feedback can induce two different gene expression states (bistability) under the very same cellular conditions. Whether, and how, cells make use of the coexistence of a larger number of stable states (multistability) is however largely unknown. Here, we first examine how autoregulation, a common attribute of genetic master regulators, facilitates multistability in two-component circuits. A systematic exploration of these modules' parameter space reveals two classes of molecular switches, involving transitions in bistable (progression switches) or multistable (decision switches) regimes. We demonstrate the potential of decision switches for multifaceted stimulus processing, including strength, duration, and flexible discrimination. These tasks enhance response specificity, help to store short-term memories of recent signaling events, stabilize transient gene expression, and enable stochastic fate commitment. The relevance of these circuits is further supported by biological data, because we find them in numerous developmental scenarios. Indeed, many of the presented information-processing features of decision switches could ultimately demonstrate a more flexible control of epigenetic differentiation.

Why are cellular switches Boolean? General conditions for multistable genetic circuits

“…In a recent study (Guantes and Poyatos (2008)) on multistable circuits, the authors found tristable (N = 3) dynamics in genetic circuits of two components involving dimeric regulators. The computational analysis involved a systematic (both deterministic and stochastic) exploration of mutual-activation and mutual-inhibition circuits, i.e.…”

Bifurcation-based approach reveals synergism and optimal combinatorial perturbation

“…It involves several regulatory mechanisms including auto-regulation, cross-regulation, binding and unbinding of transcription factors to the promoters, transcriptions, translations, and degradations [7] and takes the form…”

“…The model and its simplified form are often used to describe the coexistence of multiple stable states in cell fate decisions [31][32][33]. As shown in [7], strong auto-activation (ρ x 1, ρ y 1) in the model (12)- (13) is necessary for the existence of multiple stable states. The existence of bistable states also needs strong crossactivation (v x 1, v y 1) under which coexistence of more than two stable states is not possible.…”

“…The standard values of all parameters are shown in Table 1. [7] ρ x , ρ y Auto-activation strengths of proteins x and y 10, 15 [7] β x , β y Basal production rates of proteins x and y 1.4, 1.2 [7] η x , η y Ratios of binding affinities in the case of transcriptional interactions 0.3, 0.25 [7] δ x , δ y Basal degradation rates of proteins x and y 10, 14 [7] μ x , μ y Joint regulatory strengths 0.1, 0.1 [7] η xy , η yx Joint binding constants 0, 0 [7] The occurrence of state a transition depends on the change of regulatory mechanisms that can be embodied by introducing a perturbation to a specific parameter. For instance, we can rewrite the ratio of binding affinity η x into the form:…”

“…In mathematical models, critical thresholds for such transitions correspond to bifurcations [6]. Typical bifurcations are those that mark the transition from one stable equilibrium to another one through a saddle-node or a pitchfork bifurcation [7] or the transition from an oscillatory attractor to a stable equilibrium through a Hopf bifurcation [8].…”