Biology subjects

Ravel, P.

Publications and source records attributed to Ravel, P..

5 recordsLinked to original sources

bioRxiv2026

An extension of Modular Response Analysis for global perturbations and robust connectivity inference of gene regulatory networks.

Understanding how gene regulatory networks respond to global cell perturbations remains a central challenge in systems biology and network inference. Modular Response Analysis (MRA) provides a mathematical framework to infer gene-to-gene directed connectivity graphs from perturbation experiments; however, classical MRA captures direct gene-to-gene influences, and does not explicitly account for global stimuli that simultaneously change the graph. Here, we introduce MRA+, an extension of MRA, that incorporates the effect of global perturbations into gene-to-gene graph inference. MRA+ assumes a sequential experimental design in which targeted gene perturbations are followed by the application of a global stimulus, enabling the separation of connectivity changes from direct gene induction. The method estimates network connectivity under induced conditions and quantifies gene-specific induction strengths, which represent contributions to expression changes arising from mechanisms external to the inferred network. In the case of single-cell expression data, we present a bootstrap strategy to assess the robustness of inferred connectivity coefficients and propose a complementary criterion based on sign stability to interpret weak or non-significant estimates. Together, these developments provide a general framework for robust inference of gene connectivity graphs in the presence of global perturbations, applicable to diverse biological and experimental contexts.

systems biology↗

bioRxiv2024

Testing and overcoming the limitations of Modular Response Analysis

Modular Response Analysis (MRA) is an effective method to infer biological networks from perturbation data. However, it has several limitations, such as strong sensitivity to noise, need of performing independent perturbations that hit a single node at a time, and linear approximation of dependencies within the network. Previously, we addressed MRA sensitivity to noise by revisiting MRA as a multilinear regression problem. Here, we provide new contributions to complement this theory. First, we overcame the need of perturbations to be independent, thereby augmenting MRA applicability. Second, using analysis of variance (ANOVA) and lack of fit tests, we assessed MRA compatibility with the data and identified the primary source of errors. If nonlinearity prevails, we propose a polynomial extension to the model. Third, we demonstrated how to effectively use the prior knowledge of the network studied. Finally, we added these innovations to our R software package MRARegress to provide a complete, extended theory around MRA and to facilitate its access by the community.

bioinformatics↗

bioRxiv2022

Modular response analysis reformulated as a multilinear regression problem

MotivationModular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system and results are sensitive to noise in the data and perturbation intensities. Applications to networks of 10 nodes or more are difficult due to noise propagation. ResultsWe propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential, additional perturbations in a larger, over determined and more stable system of equations. More relevant confidence intervals on network parameters can be obtained and we show competitive performance for networks of size up to 100. Prior knowledge integration in the form of known null edges further improves these results. Availability and implementationThe R code used to obtain the presented results is available from GitHub: https://github.com/J-P-Borg/BioInformatics ContactPatrice.ravel@umontpellier.fr

systems biology↗

bioRxiv2021

Application of Modular Response Analysis to Medium- to Large-Size Biological Systems

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions. Author SummaryThe knowledge of gene and protein regulatory networks in specific cell types, including pathologic cells, is an important endeavor in the post-genomic era. A particular type of data obtained through the systematic perturbation of the actors of such networks enables the reconstruction of the latter and is becoming available at a large scale (networks comprised of almost 1,000 genes). In this work, we benchmark the performance of a classical methodology for such data called modular response analysis, which has been so far applied to networks of modest sizes. We also propose improvements to increase performance and to accelerate computations on large problems.

systems biology↗

bioRxiv2020

aiMeRA: A generic modular response analysis R package and its application to estrogen and retinoic acid receptors crosstalk

Modular response analysis (MRA) is a widely used modeling technique to uncover coupling strengths in molecular networks under a steady-state condition by means of perturbation experiments. We propose an extension of this methodology to search genomic data for new associations with a network modeled by MRA and to improve the predictive accuracy of MRA models. These extensions are illustrated by exploring the cross talk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers such as breast. We also present a novel, rigorous and elegant mathematical derivation of MRA equations, which is the foundation of this work and of an R package that is freely available at https://github.com/bioinfo-ircm/aiMeRA/. This mathematical analysis should facilitate MRA understanding by newcomers. Author summaryEstrogen and retinoic acid receptors play an important role in several hormone-driven cancers and share co-regulators and co-repressors that modulate their transcription factor activity. The literature shows evidence for crosstalk between these two receptors and suggests that spatial competition on the promoters could be a mechanism. We used MRA to explore the possibility that key co-repressors, i.e., NRIP1 (RIP140) and LCoR could also mediate crosstalk by exploiting new quantitative (qPCR) and RNA sequencing data. The transcription factor role of the receptors and the availability of genome-wide data enabled us to explore extensions of the MRA methodology to explore genome-wide data sets a posteriori, searching for genes associated with a molecular network that was sampled by perturbation experiments. Despite nearly two decades of use, we felt that MRA lacked a systematic mathematical derivation. We present here an elegant and rather simple analysis that should greatly facilitate newcomers understanding of MRA details. Moreover, an easy-to-use R package is released that should make MRA accessible to biology labs without mathematical expertise. Quantitative data are embedded in the R package and RNA sequencing data are available from GEO.

systems biology↗