Stochastic modeling of auto-regulatory genetic feedback loops: a review and comparative study
arXiv:1910.08937 · doi:10.1016/j.bpj.2020.02.016
Abstract
Auto-regulatory feedback loops are one of the most common network motifs. A wide variety of stochastic models have been constructed to understand how the fluctuations in protein numbers in these loops are influenced by the kinetic parameters of the main biochemical steps. These models differ according to (i) which sub-cellular processes are explicitly modelled; (ii) the modelling methodology employed (discrete, continuous or hybrid); (iii) whether they can be analytically solved for the steady-state distribution of protein numbers. We discuss the assumptions and properties of the main models in the literature, summarize our current understanding of the relationship between them and highlight some of the insights gained through modelling.
arXiv comment12 pages, 3 figures. Submitted to Biophysical Journal
References in corpus (27)
- Analytical distributions for stochastic gene expression
- Noise-based switches and amplifiers for gene expression
- Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
- Exact distributions for stochastic gene expression models with bursting and feedback
- Steady-state fluctuations of a genetic feedback loop: an exact solution
- Determining the stability of genetic switches: explicitly accounting for mRNA noise
- How accurate are the non-linear chemical Fokker-Planck and chemical Langevin equations?
- Stochastic phenotype transition of a single cell in an intermediate region of gene-state switching
- Exact solution of stochastic gene expression models with bursting, cell cycle and replication dynamics
- Small protein number effects in stochastic models of autoregulated bursty gene expression
- Gene expression dynamics with stochastic bursts: exact results for a coarse-grained model
- Noise-induced multistability in chemical systems: Discrete vs Continuum modeling
- Emergent Lévy behavior in single-cell stochastic gene expression
- Dynamic behavior of stochastic gene expression models in the presence of bursting
- Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback
- Positive Feedback, Stochasticity and Genetic Competence
- Bistable switching asymptotics for the self regulating gene
- MCMC inference for Markov Jump Processes via the Linear Noise Approximation
- Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks
- Mixed Poisson distributions in exact solutions of stochastic auto-regulation models
- Approximate probability distributions of the master equation
- Transcriptional leakage versus noise: A simple mechanism of conversion between binary and graded response in autoregulated genes
- Stochastic Hybrid Models of Gene Regulatory Networks - A PDE Approach
- Parameter estimation for biochemical reaction networks using Wasserstein distances
- Influence of gene copy number on self-regulated gene expression
- Stochastic single-gene auto-regulation
- Self-regulating genes. Exact steady state solution by using Poisson Representation