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Ventre E, Herbach U, Espinasse T, Benoit G, Gandrillon O. One model fits all: Combining inference and simulation of gene regulatory networks. PLoS Comput Biol 2023; 19:e1010962. [PMID: 36972296 PMCID: PMC10079230 DOI: 10.1371/journal.pcbi.1010962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 04/06/2023] [Accepted: 02/17/2023] [Indexed: 03/29/2023] Open
Abstract
The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions. We verify that CARDAMOM quantitatively reconstructs causal links when the data is simulated from HARISSA, and demonstrate its performance on experimental data collected on in vitro differentiating mouse embryonic stem cells. Overall, this integrated strategy largely overcomes the limitations of disconnected inference and simulation.
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Affiliation(s)
- Elias Ventre
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Lyon, France
- Inria Center Grenoble Rhône-Alpes, Équipe Dracula, Villeurbanne, France
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Ulysse Herbach
- Université de Lorraine, CNRS, Inria, IECL, Nancy, France
| | - Thibault Espinasse
- Inria Center Grenoble Rhône-Alpes, Équipe Dracula, Villeurbanne, France
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Gérard Benoit
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Lyon, France
| | - Olivier Gandrillon
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Lyon, France
- Inria Center Grenoble Rhône-Alpes, Équipe Dracula, Villeurbanne, France
- * E-mail:
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Herbach U, Bonnaffoux A, Espinasse T, Gandrillon O. Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC SYSTEMS BIOLOGY 2017; 11:105. [PMID: 29157246 PMCID: PMC5697158 DOI: 10.1186/s12918-017-0487-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 11/09/2017] [Indexed: 01/13/2023]
Abstract
Background 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. Results 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. Conclusions 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. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0487-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ulysse Herbach
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, Villeurbanne Cedex, F-6962, France
| | - Arnaud Bonnaffoux
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.,The CoSMo company, 5 passage du Vercors, Lyon, 69007, France
| | - Thibault Espinasse
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, Villeurbanne Cedex, F-6962, France
| | - Olivier Gandrillon
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, Lyon, F-69007, France. .,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.
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