Biology subjects

Pauleve, L.

Publications and source records attributed to Pauleve, L..

12 recordsLinked to original sources

bioRxiv2026

Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT

Predictive logical models have made substantial contributions to biomedical research by facilitating the identification of regulatory mechanisms driving biological processes. Yet, their construction remains a major bottleneck, as it typically relies on extensive prior knowledge at the genome scale. The emergence of high-throughput omics technologies has opened new opportunities for data-driven logical model construction by providing high-resolution observations of biological states. In parallel, formal constraint-based approaches have emerged, reformulating executable model inference as a constraint-satisfaction problem. However, transforming omics observations into biologically meaningful Boolean state abstractions and dynamical constraints suitable for logical synthesis engines remains challenging, particularly for poorly understood biological systems and multi-condition experimental settings. To address these challenges, we introduce scBOLT, a semi-automated framework for Boolean constraint engineering and logical model inference from multi-condition single-cell transcriptomes. Beyond these methodological contributions, scBOLT substantially improves computational tractability, enabling the inference of Boolean networks comprising several hundred components within practical runtimes.

systems biology↗

bioRxiv2026

Why Boolean network control tools disagree: a taxonomy of control problems

Boolean networks are discrete dynamical models that use Boolean states and logical functions to represent the dynamics of biological systems. A primary application of Boolean networks is to identify controls (e.g., genetic mutations or knockouts) that drive the system toward a desired phenotype. However, existing computational tools often produce inconsistent results because they rely on differing modeling assumptions. To better understand these differences, we survey existing tools and propose a taxonomy of control problems. Our taxonomy unveils hidden coverage relationships among their solutions that arise from these modeling assumptions. We provide a computational framework to empirically assess these relationships by comparing their predicted controls on a suite of artificial and biological models. Finally, we develop a coverage-consistent metric, the mutation co-occurrence score, to prioritize mutations based on their predicted impact on the phenotype. A case study on T-LGL leukemia highlights how an ensemble prediction of the score across multiple tools identifies key mutations associated with apoptosis. Author summaryBoolean networks let us model gene and protein regulation with simple on/off logic. That simplicity makes them useful for asking a practical question: which controls (e.g., genetic interventions, knockouts, or forced activations) can guarantee a desired cellular phenotype such as survival or apoptosis. In practice, different software tools often return inconsistent control sets, creating a practical barrier to reproducibility and reliability of predictions. Here we provide a comprehensive survey to navigate this landscape. We provide a comparison framework to assess coverage relationships, both theoretically and empirically, among the solutions of control tools. We classify the tools based on our new taxonomy that highlights the core differences among them. For many tools, we can theoretically determine their coverage relationships based on which components are fixed together to drive the phenotype. These new findings clearly explain subtle differences among solutions produced by various tools. We then develop a coverage-consistent metric for mutations called the mutation co-occurrence score. This new metric helps prioritize control targets based on their predicted impact on the phenotype. We also demonstrate that averaging scores from multiple tools gives reliable predictions. Our code is extensible to future tools and will facilitate the comparison of Boolean network control tools in biological research.

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

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

Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming

Boolean networks provide robust explainable and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes. Yet, the construction of such models is extremely challenging, as it requires integrating prior knowledge with experimental observation of transcriptome, potentially relating thousands of genes. We present a general methodology, implemented in the software tool BoNesis, for the qualitative modeling of gene regulation behind the observed state changes from transcriptome data and prior knowledge of the gene regulatory network. BoNesis allows computing ensembles of Boolean networks, where each of them is able to reproduce the modeled differentiation process. We illustrate the scalability and versatility of BoNesis with two applications: the modeling of hematopoiesis from single-cell RNA-Seq data, and modeling the differentiation of bone marrow stromal cells into adipocytes and osteoblasts from bulk RNA-seq time series data. For this later case, we took advantage of ensemble modeling to predict combinations of reprogramming factors for trans-differentiation that are robust to model uncertainties due to variations in experimental replicates and choice of binarization method. Moreover, we performed an in silico assessment of the fidelity and efficiency of the reprogramming, and conducted preliminary experimental validation.

systems biology↗

bioRxiv2024

Seed2LP: seed inference in metabolic networks for reverse ecology applications

A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualised phenotypes using nutrient information. We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customisable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms. Seed2LP is available on https://github.com/bioasp/seed2lp.

bioinformatics↗

bioRxiv2024

Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge

Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explaining and predictive models in ecology. We tackled this challenge by employing qualitative modeling frameworks and logic methods for the synthesis of mathematical models that can integrate both observations and expert knowledge on the system. After devising a formal link between ecological networks and the causal structure of Boolean networks, we applied a generic model synthesis engine to infer Boolean models that are able to reproduce the observed dynamics of a protist community. Our inference method supports optimization criteria to derive most parsimonious and most precise models. It is also able to integrate prior knowledge on the ecological network, adding constraints on impossible interactions, which is necessary to obtain realistic predictions. Such constraints may however prove to be too strict, in which case our method is able to conclude on the absence of a model compatible with both the observations and the input hypotheses. We demonstrated our methodology on experimental data of a protist system, and showed its ability to recover essential and sufficient ecological interactions to explain the observed dynamics.

ecology↗

bioRxiv2023

scBoolSeq: Linking scRNA-Seq Statistics and Boolean Dynamics

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expressions in cells, as scRNA-Seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-Seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-Seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-Seq datasets, including dropout events, with Boolean states is a challenging task. We present O_SCPLOWSCC_SCPLOWBO_SCPLOWOOLC_SCPLOWSO_SCPLOWEQC_SCPLOW, a method for the bidirectional linking of scRNA-Seq data and Boolean activation state of genes. Given a reference scRNA-Seq dataset, O_SCPLOWSCC_SCPLOWBO_SCPLOWOOLC_SCPLOWSO_SCPLOWEQC_SCPLOW computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, O_SCPLOWSCC_SCPLOWBO_SCPLOWOOLC_SCPLOWSO_SCPLOWEQC_SCPLOW can perform both binarisation of scRNA-Seq datasets, and generate synthetic scRNA-Seq datasets from Boolean trajectories, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of O_SCPLOWSCC_SCPLOWBO_SCPLOWOOLC_SCPLOWSO_SCPLOWEQC_SCPLOWs binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-Seq data generated by O_SCPLOWSCC_SCPLOWBO_SCPLOWOOLC_SCPLOWSO_SCPLOWEQC_SCPLOW with BO_SCPLOWOOLC_SCPLOWODE from the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-Seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in a two-dimensional projection of the data. Author summaryThe qualitative and logical modeling of cell dynamics has brought precious insight on gene regulatory mechanisms that drive cellular differentiation and fate decisions by predicting cellular trajectories and mutations for their control. However, the design and validation of these models is impeded by the quantitative nature of experimental measurements of cellular states. In this paper, we provide and assess a new methodology, O_SCPLOWSCC_SCPLOWBO_SCPLOWOOLC_SCPLOWSO_SCPLOWEQC_SCPLOW for bridging single-cell level pseudocounts of RNA transcripts with Boolean classification of gene activity levels. Our method, implemented as a Python package, enables both to binarise scRNA-Seq data in order to match quantitative measurements with states of logicals models, and to generate synthetic data from Boolean trajectories in order to benchmark inference methods. We show that O_SCPLOWSCC_SCPLOWBO_SCPLOWOOLC_SCPLOWSO_SCPLOWEQC_SCPLOW accurately captures main statistical features of scRNA-Seq data, including measurement dropouts, improving significantly the state of the art. Overall, scBoolSeq brings a statistically-grounded method for enabling the inference and validation of qualitative models from scRNA-Seq data.

systems biology↗

bioRxiv2020

A Detailed Map of Coupled Circadian Clock and Cell Cycle with Qualitative Dynamics Validation

The temporal coordination of biological processes by the circadian clock is an important mechanism, and its disruption has negative health outcomes, including cancer. Experimental and theoretical evidence suggests that the oscillators driving the circadian clock and the cell cycle are coupled through phase locking. We present a detailed and documented SBGN PD map of known mechanisms related to the regulation of the circadian clock, and its coupling with an existing cell cycle SBGN PD map which includes main interactions of the mammalian cell cycle. The coherence of the merged map has been validated with a qualitative dynamics analysis. We verified that the coupled circadian clock and cell cycle maps allow reproducing the observed sequence of phase markers. Moreover, we predicted mutations that contribute to regulating checkpoints of the two oscillators. Strikingly, our approach underlines the potential key role of the core clock protein NR1D1 in regulating cell cycle progression. We predicted that its activity influences negatively the progression of the cell cycle from phase G2 to M. It is consistent with the earlier experimental finding that pharmacological activation of NR1D1 inhibits tumour cell proliferation and shows that our approach can identify biologically relevant species in the context of large and complex networks.

systems biology↗

bioRxiv2020

Reconciling Qualitative, Abstract, and Scalable Modeling of Biological Networks

Predicting the behaviors of complex biological systems, underpinning processes such as cellular differentiation, requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach which enables reasoning on the qualitative dynamics of networks accounting for many species. Several dynamical approaches have been proposed to interpret the logic of the regulations encoded by the BNs. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations are not adequate to represent quantitative systems, being able to both predict spurious behaviors and miss others. We introduce a new paradigm, the Most Permissive Boolean Networks (MPBNs), which offer the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.

systems biology↗

bioRxiv2018

Prediction of Mutations to Control Pathways Enabling Tumour Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial)

Boolean and multi-valued logical formalisms are increasingly used to model complex cellular networks. To ease the development and analysis of logical models, a series of software tools have been proposed, often with specific assets. However, combining these tools typically implies a series of cumbersome software installation and model conversion steps. In this respect, the CoLoMoTo Interactive Notebook provides a joint distribution of several logical modelling software tools, along with an interactive web Python interface easing the chaining of complementary analyses. In this protocol, we demonstrate the assets of this approach through the analysis of a computational model of biological network. Our computational workflow combines (1) the importation of a GINsim model and its display, (2) its format conversion using the Java library BioLQM, (3) the formal prediction of mutations using the OCaml software Pint, (4) the model checking using the C++ software NuSMV, (5) quantitative stochastic simulations using the C++ software MaBoSS, and (6) the visualisation of results using the Python library matplotlib. Starting with a recent Boolean model of the signalling network controlling tumour cell invasion and migration, our model analysis culminates with the prediction of sets of mutations presumably involved in a metastatic phenotype.

systems biology↗

bioRxiv2018

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualisation of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to software tools including GINsim, BioLQM, Pint, MaBoSS, and Cell Collective for the modelling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.

bioinformatics↗