Intrinsic noise profoundly alters the dynamics and steady state of morphogen-controlled bistable genetic switches

Abstract

During tissue development, patterns of gene expression determine the spatial arrangement of cell types. In many cases, gradients of secreted signaling molecules - morphogens - guide this process. The continuous positional information provided by the gradient is converted into discrete cell types by the downstream transcriptional network that responds to the morphogen. A mechanism commonly used to implement a sharp transition between two adjacent cell fates is the genetic toggle switch, composed of cross-repressing transcriptional determinants. Previous analyses emphasize the steady state output of these mechanisms. Here, we explore the dynamics of the toggle switch and use exact numerical simulations of the kinetic reactions, the Chemical Langevin Equation, and Minimum Action Path theory to establish a framework for studying the effect of gene expression noise on patterning time and boundary position. This provides insight into the time scale, gene expression trajectories and directionality of stochastic switching events between cell states. Taking gene expression noise into account predicts that the final boundary position of a morphogen-induced toggle switch, although robust to changes in the details of the noise, is distinct from that of the deterministic system. Moreover, stochastic switching introduces differences in patterning time along the morphogen gradient that result in a patterning wave propagating away from the morphogen source. The velocity of this wave is influenced by noise; the wave sharpens and slows as it advances and may never reach steady state in a biologically relevant time. This could explain experimentally observed dynamics of pattern formation. Together the analysis reveals the importance of dynamical transients for understanding morphogen-driven transcriptional networks and indicates that gene expression noise can qualitatively alter developmental patterning.

References in corpus (8)

• The large deviation approach to statistical mechanics
• Optimal Path to Epigenetic Switching
• Control of stochastic and induced switching in biophysical networks
• Mutual Repression enhances the Steepness and Precision of Gene Expression Boundaries
• Exponential sensitivity of noise-driven switching in genetic networks
• Reaction coordinates for the flipping of genetic switches
• Isolating intrinsic noise sources in a stochastic genetic switch
• Transition Path, Quasi-potential Energy Landscape and Stability of Genetic Switches

Cited by in corpus (8)

• Path-integral formalism for stochastic resetting: Exactly solved examples and shortcuts to confinement
• Rigorous bounds on the stationary distributions of the chemical master equation via mathematical programming
• Minimum Action Path theory reveals the details of stochastic biochemical transitions out of oscillatory cellular states
• Thermodynamics of switching in multistable non-equilibrium systems
• Stable developmental patterns of gene expression without morphogen gradients
• Quasi-steady-state approximations derived from the stochastic model of enzyme kinetics
• Precision of morphogen-driven tissue patterning during development is enhanced through contact-mediated cellular interactions
• Noise control and utility: from regulatory network to spatial patterning