Biology subjects

Ruscone, M.

Publications and source records attributed to Ruscone, M..

8 recordsLinked to original sources

bioRxiv2026

Hck signaling drives long-distance ECM degradation through endo-exocytosis coupling in macrophages.

The diversity of strategies implicated in extracellular matrix (ECM) degradation supporting cell invasion has been poorly achieved. Unlike invasive breast cancer cells, which predominantly degrade the ECM locally via an invadosome-associated degradation, dynamic quantification of live imaging of degradation of physiological ECM such as fibrinogen showed that macrophages employ different modes of ECM degradation since digesting ECM both locally and at long distance. Long distance degradation occurring in macrophage is dependent on both matrix metalloproteases (MMPs) and cathepsins release. Optogenetic manipulation showed that HCK signaling specifically regulates positively this new mode of fibrinogen degradation by increasing cathepsins release and activating localized fusion of acidic CD63-endolysosome vesicles at the vicinity of clathrin hotspots at the rear of macrophages, appearing as a new exo-endocytosis coupling. Computational biology and in silico simulations support the importance of the fine spatiotemporal coordination between MMPs and cathepsin activities, and regulation of cathepsin activity by local acid release. Different mode of ECM degradation can thus coexist and this highlight the importance of understanding the cathepsins-MMPs synergy in shaping invasive behavior across different physio-pathological invasion processes.

cell biology↗

bioRxiv2026

PhysiCelldFBA: Linking single-cell genome-scale metabolism to spatially explicit multicellular dynamics

Genome-scale metabolic models can predict how individual cells allocate resources and respond to their environment, yet few frameworks link single-cell metabolism to the spatial organisation of multicellular systems. Here we introduce PhysiCelldFBA, an extension of the PhysiCell agent-based framework that couples genome-scale dynamic flux balance analysis to off-lattice multicellular simulations. Each simulated cell carries its own metabolic model, allowing local environmental conditions to shape metabolism while metabolic activity feeds back on the surrounding environment, cellular behaviour, and spatial organisation. We first validate this coupling by showing that glucose consumption, CO2 production, and biomass accumulation remain mass-balanced in a closed E. coli system, with simulated biomass agreeing with analytical predictions to within 1%. We then demonstrate how metabolic phenotypes emerge from this coupling across microbial and mammalian systems. Spatial nutrient gradients generate metabolic stratification and acetate cross-feeding in growing E. coli colonies; diffusion-limited metabolism produces proliferative, hypoxic, and necrotic zones across a broad panel of metabolites in a tumour-like tissue; distinct, organism-specific metabolic networks give rise to syntrophic cross-feeding and spatial niche formation in a two-species consortium; and metabolic state couples energy availability to transitions between cellular motility and growth. Across these examples, metabolic stratification, cross-feeding, and phenotypic adaptation emerge from local metabolic optimisation and environmental feedback rather than being explicitly prescribed. PhysiCelldFBA therefore provides a general framework for simulating genome-scale metabolism at single-cell resolution and linking intracellular metabolic state to cellular behaviour and emergent organisation across scales.

systems biology↗

bioRxiv2025

Intelligent Tool Orchestration for Rapid Mechanistic Model Prototyping: MCP Servers as AI-Biology Interfaces

The construction of multicellular mechanistic models in systems biology typically requires months of literature research, programming expertise, and deep knowledge of specialized computational tools. Here we present intelligent tool orchestration through Model Context Protocol (MCP) servers that enable Large Language Models (LLMs) agents to function as AI laboratory assistants for rapid model prototyping. We demonstrate this approach by constructing a multiscale model of cancer cell fate in response to TNF, entirely through natural language interactions, using an AI agent connected to MCP servers that interface with three complementary modeling software: NeKo for constructing gene regulatory networks from prior-knowledge databases, MaBoSS for simulating and analyzing Boolean models, and PhysiCell for setting up multicellular agent-based models. Our architecture encompasses more than 60 specialized tools, covering the entire workflow from data collection to multicellular simulation setup. From this use case we derived three key design principles for biological AI-tool integration: first, optimal tool granularity is best defined at biological decision points, where domain knowledge guides modeling choices; second, comprehensive session management is essential for tracking complex workflows and ensuring reproducibility across long interactions; and third, effective orchestration must be established from the start to let LLMs combine tools flexibly while maintaining biological coherence. In applying this framework across three different LLMs and scenarios, we observed consistent end-to-end orchestration as well as model-dependent differences in network size, dynamical behaviors, and integration details. This variability highlights both the portability of our approach and the importance of careful reporting and validation when deploying different LLMs in scientific contexts. While the resulting models require refinement, this work establishes the foundation for AI-assisted rapid prototyping in systems biology, enabling researchers to explore computational hypotheses at unprecedented speed while maintaining biological fidelity.

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↗

bioRxiv2024

NeKo: a tool for automatic network construction from prior knowledge

Biological networks provide a structured framework for analyzing the dynamic interplay and interactions between molecular entities, facilitating deeper insights into cellular functions and biological processes. Network construction often requires extensive manual curation based on scientific literature and public databases, a time-consuming and laborious task. To address this challenge, we introduce NeKo, a Python package to automate the construction of biological networks by integrating and prioritizing molecular interactions from various databases. NeKo allows users to provide their molecules of interest (e.g., genes, proteins or phosphosites), select interaction resources and apply flexible strategies to build networks based on prior knowledge. Users can filter interactions by various criteria, such as direct or indirect links and signed or unsigned interactions, to tailor the network to their needs and downstream analysis. We demonstrate some of NeKos capabilities in two use cases: first we construct a network based on transcriptomics from medulloblastoma; in the second, we model drug synergies. NeKo streamlines the network-building process, making it more accessible and efficient for researchers. The tool is available at https://sysbio-curie.github.io/Neko/.

bioinformatics↗

bioRxiv2023

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. Hopefully the model will be defined using some format specification, e.g., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects, e.g., cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged.

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

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↗