Laurence Calzone

This study identifies HCK signaling as a driver of long-distance ECM degradation in macrophages through endo-exocytosis coupling involving MMPs and cathepsins.

TabularQual provides a spreadsheet-based format for annotating logical models, with tools to convert between spreadsheets and SBML-qual, enhancing model reuse and interoperability.

This paper introduces a transfer learning method for compound preselection using phenotypic profiling, revealing activity cliffs and functional relationships across 112,480 compounds.

The paper describes the development and evaluation of an agentic framework using LLMs to explore molecular interaction maps through user-driven prototyping and testing.

AstroLogics analyzes Boolean model ensembles to identify differences in dynamics and regulation through simulation-based clustering and visualization.

The paper introduces BoNesis, a tool for inferring Boolean networks from transcriptome data to model cellular differentiation and reprogramming, demonstrating its application in hematopoiesis and adipocyte/osteoblast differentiation.

NeKo automates biological network construction by integrating molecular interactions from databases, enabling efficient network building for research applications.

This tutorial demonstrates how to use the CoLoMoTo software suite to perform reproducible dynamical analyses of logical models of biological networks.

The paper constructs a CF signalling network from transcriptomic data to identify dysregulated proteins and potential therapeutic targets in Cystic Fibrosis.

This paper introduces a method to link scRNA-Seq data with Boolean gene activation states, enabling both binarisation of real data and generation of synthetic data for model validation.

The paper introduces rROMA, a software package for computing gene set activity from omics data, and applies it to cystic fibrosis transcriptomic data to identify disease-related pathways and potential biases.

PhysiBoSS 2.0 integrates stochastic Boolean and agent-based models for multiscale cancer simulations, improving modularity and functionality.

The paper develops a dynamic Boolean model to simulate psoriasis development and identifies potential drug targets by predicting interventions that mimic current treatments.

The paper presents a multiscale model of cancer cell invasion using PhysiBoSS, integrating spatial and intracellular processes to study migration modes and validate against experimental data.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

UPMaBoSS simulates cell population dynamics by extending MaBoSS to model interactions and death/division, revealing TNF-induced resistance mechanisms.

The paper develops patient-specific Boolean models of prostate cancer signaling networks to identify actionable targets for personalized treatment, finding dose-dependent effects of inhibitors on key targets.

The paper develops a simplified cell cycle model to analyze single-cell data and predict cell line doubling time from trajectory length.

The paper develops personalized logical models to analyze cancer response to BRAF treatments, showing they correlate with experimental sensitivities using omics data without model training.

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

The paper provides an exact method to calculate stationary solutions and analyze parameter sensitivity in stochastic Boolean models without Monte Carlo simulations.

This study profiles the proteomes and phosphoproteomes of four prostate cell lines to identify differentially expressed proteins and phosphosites associated with resistance and cancer progression.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

PhysiBoSS integrates multi-scale agent-based modelling with Boolean networks to study cell population dynamics and treatment responses in 2D and 3D environments.

The paper demonstrates the use of the CoLoMoTo Interactive Notebook to analyze a Boolean model of tumor cell invasion, predicting mutations associated with metastasis.

The paper introduces a framework for reproducible analysis of qualitative biological networks using an interactive notebook environment.

The paper defines a set of informative gene signatures from pan-cancer data and introduces InfoSigMap to visualize their compositional and functional redundancies.

DeDaL creates data-driven and structure-driven network layouts in Cytoscape using dimension reduction and morphing techniques to align with molecular profiling data.

The paper describes a computational method to predict genetic interactions from Boolean models of biological networks using probabilistic simulations and analyzes their properties.