Reaction coordinates for the flipping of genetic switches

Abstract

We present a detailed analysis, based on the Forward Flux Sampling (FFS) simulation method, of the switching dynamics and stability of two models of genetic toggle switches, consisting of two mutually-repressing genes encoding transcription factors (TFs); in one model (the exclusive switch), they mutually exclude each other's binding, while in the other model (general switch) the two transcription factors can bind simultaneously to the shared operator region. We assess the role of two pairs of reactions that influence the stability of these switches: TF-TF homodimerisation and TF-DNA association/dissociation. We factorise the flipping rate k into the product of the probability rho(q*) of finding the system at the dividing surface (separatrix) between the two stable states, and a kinetic prefactor R. In the case of the exclusive switch, the rate of TF-operator binding affects both rho(q*) and R, while the rate of TF dimerisation affects only R. In the case of the general switch both TF-operator binding and TF dimerisation affect k, R and rho(q*). To elucidate this, we analyse the transition state ensemble (TSE). For the exclusive switch, varying the rate of TF-operator binding can drastically change the pathway of switching, while changing the rate of dimerisation changes the switching rate without altering the mechanism. The switching pathways of the general switch are highly robust to changes in the rate constants of both TF-operator and TF-TF binding, even though these rate constants do affect the flipping rate; this feature is unique for non-equilibrium systems.

arXiv comment24 pages, 7 figures

References in corpus (14)

• A Novel Path Sampling Method for the Calculation of Rate Constants
• Sampling rare switching events in biochemical networks
• Simulating rare events in equilibrium or non-equilibrium stochastic systems
• Forward Flux Sampling-type schemes for simulating rare events: Efficiency analysis
• Rate constants for diffusive processes by partial path sampling
• The Importance of Being Discrete - Life Always Wins on the Surface
• Genetic Toggle Switch Without Cooperative Binding
• Nonlinear Protein Degradation and the Function of Genetic Circuits
• Signal detection, modularity and the correlation between extrinsic and intrinsic noise in biochemical networks
• Absolute Rate Theories of Epigenetic Stability
• Computing stationary distributions in equilibrium and non-equilibrium systems with Forward Flux Sampling
• Transitions Induced by the Discreteness of Molecules in a Small Autocatalytic System
• Enhancement of the stability of genetic switches by overlapping upstream regulatory domains
• Testing the transition state theory in stochastic dynamics of a genetic switch

Cited by in corpus (23)

• Forward Flux Sampling for rare event simulations
• Spatio-temporal correlations can drastically change the response of a MAPK pathway
• Bacterial growth: a statistical physicist's guide
• Optimizing information flow in small genetic networks. I
• Intrinsic noise profoundly alters the dynamics and steady state of morphogen-controlled bistable genetic switches
• Studying Rare Events using Forward-Flux Sampling: Recent Breakthroughs and Future Outlook
• Stability and multi-attractor dynamics of a toggle switch based on a two-stage model of stochastic gene expression
• Mutual Repression enhances the Steepness and Precision of Gene Expression Boundaries
• Exponential sensitivity of noise-driven switching in genetic networks
• DNA looping provides stability and robustness to the bacteriophage lambda switch
• Positive Feedback Regulation Results in Spatial Clustering and Fast Spreading of Active Signaling Molecules on a Cell Membrane
• Oscillations in the expression of a self-repressed gene induced by a slow transcriptional dynamics
• What makes the lac-pathway switch: identifying the fluctuations that trigger phenotype switching in gene regulatory systems
• The Limiting Dynamics of a Bistable Molecular Switch With and Without Noise
• Statistical physics of a model binary genetic switch with linear feedback
• Mean-field vs. stochastic models for transcriptional regulation
• Trajectory reweighting for non-equilibrium steady states
• Stable developmental patterns of gene expression without morphogen gradients
• Markov State Models of Gene Regulatory Networks
• Spatial-Stochastic Simulation of Reaction-Diffusion Systems
• Variational Markov chain mixtures with automatic component selection
• Switching off: the phenotypic transition to the uninduced state of the lactose uptake pathway
• The importance of crowding in signaling, genetic, and metabolic networks