WASABI

WASABI: a dynamic iterative framework for gene regulatory inferenc

Gene regulatory network inference from dynamic multi-scale data

L'inférence des réseaux de régulation de gènes (RRG) à partir de données d'expression est un défi majeur en biologie. L’arrivée des technologies de mesure de transcriptomique à l’échelle de la cellule a suscité de nombreux espoirs, mais paradoxalement elles montrent une nouvelle complexité du problème d’inférence des RRG qui limite encore les approches existantes. Nous avons commencé par montrer, à partir de données d'expression en cellules uniques acquises sur un modèle aviaire de différenciation érythrocytaire, que les RRG sont des systèmes stochastiques à l'échelle de la cellule et qu'il y a une évolution dynamique de cette stochasticité au cours du processus de différenciation (Richard et al, PLOS Comp.Biol., 2016). C'est pourquoi nous avons développé par la suite un modèle de RRG mécaniste qui inclus cette stochasticité afin d'exploiter au maximum l'information des données expérimentales à l'échelle de la cellule (Herbach et al, BMC Sys.Biol., 2017). Ce modèle décrit les interactions entre gènes comme un couplage de processus de Markov déterministes par morceaux. En régime stationnaire une formule explicite de la distribution jointe est dérivée du modèle et peut servir à inférer des réseaux simples. Afin d'exploiter l'information dynamique et d'intégrer d'autres données expérimentales (protéomique, demi-vie des ARN), j’ai développé à partir du modèle précédent une approche itérative, intégrative et parallèle, baptisée WASABI qui est basé sur le concept de vague d'expression (Bonnaffoux et al, en révision, 2018). Cette approche originale a été validée sur des modèles in-silico de RRG, puis sur nos données in-vitro. Les RRG inférés affichent une structure de réseau originale au regard de la littérature, avec un rôle central du stimulus et une topologie très distribuée et limitée. Les résultats montrent que WASABI surmonte certaines limitations des approches existantes et sera certainement utile pour aider les biologistes dans l’analyse et l’intégration de leurs données.Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations.In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from timestamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-byone through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in-silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in-vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. Together, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data

Inférence de réseaux de régulation de gènes à partir de données dynamiques multi-échelles

Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from timestamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-byone through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in-silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in-vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. Together, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data.L'inférence des réseaux de régulation de gènes (RRG) à partir de données d'expression est un défi majeur en biologie. L’arrivée des technologies de mesure de transcriptomique à l’échelle de la cellule a suscité de nombreux espoirs, mais paradoxalement elles montrent une nouvelle complexité du problème d’inférence des RRG qui limite encore les approches existantes. Nous avons commencé par montrer, à partir de données d'expression en cellules uniques acquises sur un modèle aviaire de différenciation érythrocytaire, que les RRG sont des systèmes stochastiques à l'échelle de la cellule et qu'il y a une évolution dynamique de cette stochasticité au cours du processus de différenciation (Richard et al, PLOS Comp.Biol., 2016). C'est pourquoi nous avons développé par la suite un modèle de RRG mécaniste qui inclus cette stochasticité afin d'exploiter au maximum l'information des données expérimentales à l'échelle de la cellule (Herbach et al, BMC Sys.Biol., 2017). Ce modèle décrit les interactions entre gènes comme un couplage de processus de Markov déterministes par morceaux. En régime stationnaire une formule explicite de la distribution jointe est dérivée du modèle et peut servir à inférer des réseaux simples. Afin d'exploiter l'information dynamique et d'intégrer d'autres données expérimentales (protéomique, demi-vie des ARN), j’ai développé à partir du modèle précédent une approche itérative, intégrative et parallèle, baptisée WASABI qui est basé sur le concept de vague d'expression (Bonnaffoux et al, en révision, 2018). Cette approche originale a été validée sur des modèles in-silico de RRG, puis sur nos données in-vitro. Les RRG inférés affichent une structure de réseau originale au regard de la littérature, avec un rôle central du stimulus et une topologie très distribuée et limitée. Les résultats montrent que WASABI surmonte certaines limitations des approches existantes et sera certainement utile pour aider les biologistes dans l’analyse et l’intégration de leurs données

A Cloud-aware autonomous workflow engine and its application to Gene Regulatory Networks inference

International audienceWith the recent development of commercial Cloud offers, Cloud solutions are today the obvious solution for many computing use-cases. However, high performance scientific computing is still among the few domains where Cloud still raises more issues than it solves. Notably, combining the workflow representation of complex scientific applications with the dynamic allocation of resources in a Cloud environment is still a major challenge. In the meantime, users with monolithic applications are facing challenges when trying to move from classical HPC hardware to elastic platforms. In this paper, we present the structure of an autonomous workflow manager dedicated to IaaS-based Clouds (Infrastructure as a Service) with DaaS storage services (Data as a Service). The solution proposed in this paper fully handles the execution of multiple workflows on a dynamically allocated shared platform. As a proof of concept we validate our solution through a biologic application with the WASABI workflow

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

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

TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks

International audienceBackground: Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data.Results: We apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions.Conclusion: These results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN model

Additional file 1 of Inferring gene regulatory networks from single-cell data: a mechanistic approach

Additional file 1. Supplementary information. This document contains details of the theoretical derivations and all the parameter values used in the examples. (PDF 362 kb

WASABI: a dynamic iterative framework for gene regulatory network inference

International audienceBackgroundInference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations.ResultsIn the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus.ConclusionsTogether, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data

Evidence for close molecular proximity between reverting and undifferentiated cells

International audienc

CD8 memory precursor cell generation is a continuous process

International audienceIn this work, we studied the generation of memory precursor cells following an acute infection by analyzing single-cell RNA-seq data that contained CD8 T cells collected during the postinfection expansion phase. We used different tools to reconstruct the developmental trajectory that CD8 T cells followed after activation. Cells that exhibited a memory precursor signature were identified and positioned on this trajectory. We found that these memory precursors are generated continuously with increasing numbers arising over time. Similarly, expression of genes associated with effector functions was also found to be raised in memory precursors at later time points. The ability of cells to enter quiescence and differentiate into memory cells was confirmed by BrdU pulse-chase experiment in vivo. Analysis of cell counts indicates that the vast majority of memory cells are generated at later time points from cells that have extensively divided