Inferring gene regulatory networks from single-cell data: a mechanistic approach

Abstract

The recent development of single-cell transcriptomics has enabled gene expression to be measured in individual cells instead of being population-averaged. Despite this considerable precision improvement, inferring regulatory networks remains challenging because stochasticity now proves to play a fundamental role in gene expression. In particular, mRNA synthesis is now acknowledged to occur in a highly bursty manner. We propose to view the inference problem as a fitting procedure for a mechanistic gene network model that is inherently stochastic and takes not only protein, but also mRNA levels into account. We first explain how to build and simulate this network model based upon the coupling of genes that are described as piecewise-deterministic Markov processes. Our model is modular and can be used to implement various biochemical hypotheses including causal interactions between genes. However, a naive fitting procedure would be intractable. By performing a relevant approximation of the stationary distribution, we derive a tractable procedure that corresponds to a statistical hidden Markov model with interpretable parameters. This approximation turns out to be extremely close to the theoretical distribution in the case of a simple toggle-switch, and we show that it can indeed fit real single-cell data. As a first step toward inference, our approach was applied to a number of simple two-gene networks simulated in silico from the mechanistic model and satisfactorily recovered the original networks. Our results demonstrate that functional interactions between genes can be inferred from the distribution of a mechanistic, dynamical stochastic model that is able to describe gene expression in individual cells. This approach seems promising in relation to the current explosion of single-cell expression data.

References in corpus (10)

• Analytical distributions for stochastic gene expression
• Stochastic gene expression as a many body problem
• Intrinsic noise in systems with switching environments
• Self consistent proteomic field theory of stochastic gene switches
• Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization
• Quantitative ergodicity for some switched dynamical systems
• Bursting noise in gene expression dynamics: Linking microscopic and mesoscopic models
• Cox process representation and inference for stochastic reaction-diffusion processes
• Dichotomous noise models of gene switches
• A Model of Gene Expression Based on Random Dynamical Systems Reveals Modularity Properties of Gene Regulatory Networks

Cited by in corpus (12)

• A stochastic and dynamical view of pluripotency in mouse embryonic stem cells
• Stochastic gene expression with a multistate promoter: breaking down exact distributions
• Stochastic Hybrid Models of Gene Regulatory Networks - A PDE Approach
• Model reduction methods for classical stochastic systems with fast-switching environments: reduced master equations, stochastic differential equations, and applications
• Stochastic filtering for multiscale stochastic reaction networks based on hybrid approximations
• Inferring stochastic rates from heterogeneous snapshots of particle positions
• Efficient stochastic simulation of gene regulatory networks using hybrid models of transcriptional bursting
• Quasi-stationary behavior for an hybrid model of chemostat: the Crump-Young model
• Network Medicine in the age of biomedical big data
• Push-forward method for piecewise deterministic biochemical simulations
• Non-equilibrium time-dependent solution to discrete choice with social interactions
• Stationarity and inference in multistate promoter models of stochastic gene expression via stick-breaking measures