Biology subjects

Noel, V.

Publications and source records attributed to Noel, V..

12 recordsLinked to original sources

bioRxiv2026

TabularQual: A spreadsheet-based format for annotating and curating logical models in SBML-qual

Logical models are widely used to study regulatory and signaling systems, yet their reuse, annotation, and exchange across tools remain challenging. Although SBML Level 3 Qualitative Models (SBML-qual) provides a standard representation, its XML-based syntax is difficult to inspect and edit directly. Here we introduce TabularQual, a spreadsheet-based, community-driven representation for Boolean and multi-valued logical models, together with a bidirectional converter between spreadsheets and SBML-qual. The converter is accessible programmatically and via a web interface to support diverse user workflows. We further describe two integration strategies that enable existing modeling tools to operate with TabularQual, either through SBML-qual exchange or via direct support. Case studies using the Stress Knowledge Map and CaSQ demonstrate how this integration supports model construction, curation, and reuse. TabularQual provides a practical bridge between human-readable model representations and standardized executable formats, supporting reproducibility, interoperability, and community-driven model development.

systems biology↗

bioRxiv2025

AstroLogics: A simulation-based analysis framework for monotonous Boolean model ensemble

Boolean networks (BNs) have emerged as versatile tools for modeling cellular regulatory mechanisms due to their ability to capture key biological features despite their simplicity. Multiple BN synthesis methods, which aim to infer BNs with dynamics that corresponding to the experimental data, have emerged in a recent decade. However, these methods often posed a challenge as multiple BN candidates or "model ensemble" can be generated. While these ensembles are valuable for representing cell populations and their heterogeneity, current methods typically treat them as single components without examining their constituent features. We present AstroLogics, a novel framework designed to analyze and identify differences in both dynamical behavior and logical regulation within BN model ensembles. The framework calculates dynamical distances between BNs through exploration of their state transition graphs (STGs), enabling clustering of similarly functioning models that may represent different cellular fates or signaling mechanisms. AstroLogics also identifies key logical properties that govern each cluster, highlighting the core regulatory structures that differentiate model behaviors. Our approach leverages MaBoSS, a stochastic simulation tool that implements the Boolean Kinetic Monte-Carlo algorithm to address time interpretation in BNs. This probabilistic approximation method allows efficient probing of BN dynamics through stochastic simulations, overcoming the computational limitations of exhaustive STG analysis. Our framework also provides powerful visualization and classification capabilities for model ensembles. Through multiple use cases, we demonstrate how AstroLogics facilitates comprehensive analysis of model diversity, and discovery of key regulatory structures within BN ensembles.

bioinformatics↗

bioRxiv2025

Open Benchmarking for Cell-Based Multiscale Models: Lessons from a Community Initiative

The emergence of virtual human twins (VHT) in biomedical research has sparked interest in multiscale in silico modelling frameworks, particularly in their application bridging cellular to tissue levels. Among the diverse array of multiscale modelling tools, off-lattice center-based agent-based models (CBM) offer a promising approach due to their depiction of cells in 3D space, closely resembling biological reality. Despite the proliferation of CBM tools addressing various biomedical challenges, a comprehensive and systematic comparison among them has been elusive. This paper presents a community-driven benchmark initiative aimed at evaluating and comparing CBM for biomedical applications, akin to successful efforts in other scientific domains such as the Critical Assessment of Protein Structure Prediction (CASP). Enlisting developers from leading tools like BioDynaMo, Chaste, PhysiCell,TiSim, and CompuTiX, we devised a benchmark scope, defined metrics, and established reference datasets to ensure a meaningful and equitable evaluation. Unit tests targeting different solvers within these tools were designed, ranging from diffusion and mechanics to cell cycle simulations and growth scenarios. Results from these tests demonstrate varying tool implementations in handling diffusion, mechanics, and cell cycle equations, emphasising the need for standardised benchmarks and interoperability. Discussions among the community underscore the necessity for defining gold standards, fostering interoperability, and drawing lessons from analogous benchmarking experiences. The outcomes, disseminated through a public platform in collaboration with OpenEBench, aim to catalyse advancements in computational biology, offering a comprehensive resource for tool evaluation and guiding future developments in cell-level simulations. This initiative endeavours to strengthen and expand the computational biology simulation community through continued dissemination and performance-oriented benchmarking efforts to enable the use of VHT in biomedicine.

systems biology↗

bioRxiv2025

Reproducible Boolean model analyses and simulations with the CoLoMoTo software suite: a tutorial.

This tutorial provides stepwise instructions to install the 20 tools integrated in the CoLoMoTo software suite, to develop reproducible dynamical analyses of logical models of complex biological molecular networks. The tutorial specifically focuses on the analysis of a previously published model of the regulatory network controlling mammalian cell proliferation. It includes chunks of python code to reproduce several of the results and figures published in the original article and further extend these results with the help of a selection of tools included in the CoLoMoTo suite. The notebook covers the visualisation of the network with the tool GINsim, an attractor analysis with bioLQM, the computation of synchronous attractors with BNS, the extraction of modules from the full model, MaBoSS simulations of the wild-type model, as well as of selected mutants, and finally the delineation of compressed probabilistic state transition graphs. The integration of all these analyses in an executable Jupyter notebook greatly eases their reproducibility, as well as the inclusion of further extensions. This notebook can further be used as a template and enriched with other ColoMoTo tools to enable comprehensive dynamical analyses of biological network models.

bioinformatics↗

bioRxiv2024

MaBoSS for HPC environments: Implementations of the continuous time Boolean model simulator for large CPUclusters and GPU accelerators

Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. We present two new developments in the continuous time Boolean simulators: Ma-BoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.

bioinformatics↗

bioRxiv2023

PhysiMeSS - A New PhysiCell Addon for Extracellular Matrix Modelling

The extracellular matrix is a complex assembly of macro-molecules, such as collagen fibres, which provides structural support for surrounding cells. In the context of cancer metastasis, it represents a barrier for the cells, that the migrating cells needs to degrade in order to leave the primary tumor and invade further tissues. Agent-based frameworks, such as PhysiCell, are often use to represent the spatial dynamics of tumor evolution. However, typically they only implement cells as agents, which are represented by either a circle (2D) or a sphere (3D). In order to accurately represent the extracellular matrix as a network of fibres, we require a new type of agent represented by a segment (2D) or a cylinder (3D). In this article, we present PhysiMeSS, an addon of PhysiCell, which introduces a new type of agent to describe fibres, and their physical interactions with cells and other fibres. PhysiMeSS implementation is publicly available at https://github.com/PhysiMeSS/PhysiMeSS, as well as in the official Physi-Cell repository. We also provide simple examples to describe the extended possibilities of this new framework. We hope that this tool will serve to tackle important biological questions such as diseases linked to dis-regulation of the extracellular matrix, or the processes leading to cancer metastasis.

systems biology↗

bioRxiv2022

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2022

Multiscale model of the different modes of cancer cell invasion

Mathematical models of biological processes implicated in cancer are built using the knowledge of complex networks of signaling pathways, describing the molecular regulations inside different cell types, such as tumor cells, immune and other stromal cells. If these models mainly focus on intracellular information, they often omit a description of the spatial organization among cells and their interactions, and with the tumoral microenvironment. We present here a model of tumor cell invasion simulated with PhysiBoSS, a multiscale framework which combines agent-based modeling and continuous time Markov processes applied on Boolean network models. With this model, we aim to study the different modes of cell migration by considering both spatial information obtained from the agent-based simulation and intracellular regulation obtained from the Boolean model. Our multiscale model integrates the impact of gene mutations with the perturbation of the environmental conditions and allows the visualization of the results with 2D and 3D representations. The model successfully reproduces single and collective migration processes and is validated on published experiments on cell invasion. In silico experiments are suggested to search for possible targets that can block the more invasive tumoral phenotypes.

bioinformatics↗

bioRxiv2022

The origin and evolution of mitochondrial tropism in Midichloria bacteria

Midichloria are intracellular bacterial symbionts of ticks. Some representatives of this genus have the unique capability to colonize mitochondria in the cells of their hosts. Hypotheses on the nature of this interaction have proven difficult to test, partly due to a lack of data. Indeed, until now, mitochondrial tropism information and genomes were available only for symbionts of three and two tick host species, respectively. Here we analyzed the mitochondrial tropism of three additional Midichloria and sequenced nine novel genomes, showing that the tropism is pnon-monophyletic, either due to losses of the trait or multiple parallel acquisitions. Comparative genome analyses support the first hypothesis, as the genomes of non-mitochondrial symbionts appear to be reduced subsets of those capable of colonizing the organelles. We detect genomic signatures of mitochondrial tropism, showing a set of candidate genes characteristic of the strains capable of mitochondrial colonization. These include the type IV secretion system and the flagellum, which could allow the secretion of unique effectors, direct interaction with, or invasion of the mitochondria. Other genes, including putative adhesion molecules, proteins possibly involved in actin polymerization, cell wall and outer membrane proteins, are only present in mitochondrial symbionts. The bacteria could use these to manipulate host structures, including mitochondrial membranes, in order to fuse with the organelles or manipulate the mitochondrial network.

evolutionary biology↗

bioRxiv2022

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former version and expands its functionalities such as using user-defined models and cells specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies. PhysiBoSS 2.0 is open-source and publicly available on GitHub (https://github.com/PhysiBoSS/PhysiBoSS) under the BSD 3-clause license with several repositories of accompanying interoperable tools. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (https://nanohub.org/tools/pba4tnf/).

systems biology↗

bioRxiv2019

Exact calculation of stationary solution and parameter sensitivity analysis of stochastic continuous time Boolean models

MotivationSolutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all asymptotic solutions. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways that have not been analyzed yet in the literature. These two fundamental uncertainties call for an exact calculation method for this class of models.\n\nResultsWe show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses an exact matrix calculation method previously applied in the context of chemical kinetics. Using this approach, we also analyze the under-explored question of the effect of transition rates on the stationary solutions and show the latter can be sensitive to parameter changes. The analysis distinguishes processes that are robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights.\n\nContactmihaly.koltai@curie.fr or emmanuel.barillot@curie.fr\n\nSupplementary informationSupplementary data available at bioRxiv online.\n\nAvailability and implementationThe calculation method described in the article is available as the ExaStoLog MATLAB package on GitHub at https://github.com/sysbio-curie/exact-stoch-log-mod

systems biology↗

bioRxiv2017

Fibroblast Growth Factor 2 lethally sensitizes cancer cells to stress-targeted therapeutic inhibitors

In malignant transformation, cellular stress response pathways are dynamically mobilized to counterbalance oncogenic activity, keeping cancer cells viable. Therapeutic disruption of this riskily balanced homeostasis might change the outcome of many human cancers, particularly those for which no effective therapy is available. Here, we report the use of Fibroblast Growth Factor 2 (FGF2) to demonstrate that further mitogenic activation disrupts cellular homeostasis and strongly sensitizes cancer cells to stress-targeted therapeutic inhibitors. We show that FGF2 enhanced replication and proteotoxic stresses in a K-Ras-driven murine cancer cell model, and combinations of FGF2 and proteasome or DNA damage response-checkpoint inhibitors triggered cell death. CRISPR/Cas9-mediated K-Ras depletion suppressed the malignant phenotype and prevented these synergic toxicities in these murine cells. Moreover, in a panel of human Ewings sarcoma family tumor cells, sub-lethal concentrations of bortezomib (proteasome-inhibitor) or VE-821 (ATR-inhibitor) induced cell death when combined with FGF2. Sustained MAPK-ERK1/2 overactivation induced by FGF2 underlies these synthetic lethalities, once late pharmacological inhibition of this pathway restored cell homeostasis and prevented these described synergies. Our results highlight how mitotic signaling pathways frequently overridden in malignant transformation might be exploited to disrupt the risky robustness of cancer cells, ultimately sensitizing them to stress-targeted therapies. This approach provides a new therapeutic rationale for human cancers, with important implications for tumors still lacking effective treatment, and for those that frequently relapse after available therapies.

cancer biology↗