Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In this study, we developed an end-to-end machine learning pipeline to analyze transcriptomic data from peripheral blood mononuclear cells and cerebrospinal fluid, integrating both bulk microarray and single-cell RNA sequencing datasets (concentrating on CD4+ and B-cells). After rigorous preprocessing, batch correction, and gene declustering, XGBoost classifiers were trained to distinguish MS patients from healthy controls. Explainable AI tools, namely SHapley Additive exPlanations (SHAP), were employed to identify key genes driving classification, and results were compared with Differential Expression Analysis (DEA). SHAP-prioritized genes were further investigated through interaction networks and pathway enrichment analyses. The models achieved strong performance, particularly in CSF B-cells (AUC=0.94) and microarray (AUC=0.86). SHAP gene selection proved to be complementary to classical DEA. Gene clusters identified across multiple datasets highlighted immune activation, non-canonical immune checkpoints (ITK, CLEC2D, KLRG1, CEACAM1), ribosomal and translational programs, ubiquitin-proteasome regulation, lipid trafficking, and Epstein-Barr virus-related pathways. Our integrative and explainable framework reveals complementary insights beyond conventional analysis and provides novel mechanistic hypotheses and potential biomarkers for MS pathogenesis.

Abstract Recent clinical trials have underscored the value of repeated minimal residual disease (MRD) measurements as a highly sensitive method for detecting subclinical disease and enabling dynamic risk stratification in hematologic malignancies. Despite its clinical potential, the complex and heterogeneous nature of MRD kinetics presents significant challenges for interpreting and integrating it into routine clinical decision‐making. In this study, we present a comprehensive, model‐based workflow for the longitudinal analysis of MRD trajectories designed to improve relapse risk prediction. We applied this newly developed workflow to a cohort of patients with mantle cell lymphoma (MCL). MRD measurements were collected from both bone marrow (BM) and peripheral blood (PB) over time, stored in the Fondazione Italiana Linfomi MCL0208 clinical trial. Using our functional MRD workflow, we defined four MRD dynamics that collapsed into two clinically relevant groups: favorable (rapid, sustained negativization) and unfavorable (persistent or fluctuating MRD). Patients with unfavorable profiles showed significantly shorter time to progression (TTP), with hazard ratio (HR) = 4.18 (95% CI: 2.44–7.14) in BM and HR = 5.71 (95% CI: 2.86–11.42) in PB. External validation in the European MCL Network “Younger trial” confirmed the predictive power of this stratification, with Kaplan–Meier analyses demonstrating significant prognostic discrimination. The most informative temporal windows for patient clustering vary by tissue. Early‐phase BM assessments offer greater discriminatory power, whereas late‐phase assessments are most informative in PB. These findings indicate that longitudinal MRD assessment in PB represents a clinically actionable strategy that could reduce dependence on invasive BM procedures.

Background Alterations in gut microbiome composition have been associated with multiple sclerosis (MS), but their impact on disease severity and early progression remains poorly understood. In this study we investigated whether gut microbiome profiling at diagnosis could identify microbial signatures associated with clinical and radiological features of early MS and provide prognostic information. Methods We analysed the gut microbiome of 53 treatment-naïve patients with MS (pwMS) and 55 healthy donors (HD) using shotgun metagenomic sequencing, combined with clinical features collected over 1 year from diagnosis. To clarify whether gut microbiome composition at MS onset could have prognostic relevance, pwMS were stratified according to lesion burden, lesion localisation, and magnetic resonance imaging (MRI) activity. Findings Overall beta diversity in Bacteria, Archaea, and Eukarya differed significantly between pwMS and HD (p-value <0.001, <0.02, <0.03, respectively). Within the MS group, glucocorticoid treatment at disease onset was the clinical factor most strongly associated with gut microbiota diversity. Stratification according to lesion burden, lesion localisation, and MRI activity identified two clinically distinct MS subgroups with different baseline clinical characteristics at onset (p-value <0.03) and different risk of early disease progression. The cluster associated with an unfavourable prognosis showed greater progression within 12 months and was enriched for motor symptoms and spinal cord lesions at diagnosis. Interpretation Our findings suggest that gut microbiome alterations are detectable at the earliest stages of MS and are associated with clinical and radiological features linked to short-term disease evolution. Gut microbial profiling may therefore represent a promising early prognostic biomarker and may help to identify candidate targets for early intervention and therapeutic development in MS, although further validation in larger longitudinal cohorts is needed. Funding This study was supported by grants from the Italian Multiple Sclerosis Foundation, the Cassa di Risparmio di Torino Foundation, and the Italian Ministry of University and Research.

Intratumor heterogeneity (ITH) arises from the combined effects of genetic alterations, clonal interactions, and environmental constraints, and plays a central role in therapeutic resistance and disease progression. While ITH has been extensively documented in empirical tumor data, the scientific debate regarding the biological mechanisms underlying this heterogeneity remains complex, highlighting the need for cancer evolution models that are sufficiently flexible and sophisticated to reproduce the observed behaviors and to give insights on the unobserved ones. Here, we present a stochastic modelling framework for tumor evolution that integrates genotypic inheritance with phenotype driven functional traits and resource mediated competition. Mutational events are associated with functional capabilities such as altered proliferation, increased mutation rates, limit evasion potential or enhanced control over shared resources, allowing multiple genotypes to converge on similar phenotypes. The model explicitly tracks subclonal lineages while incorporating environmental constraints that modulate growth and competition. The framework is defined through a mathematically rigorous construction and is accompanied by an efficient simulation algorithm. To facilitate exploration and reproducibility, we provide an open-source graphical user interface that allows users to configure model parameters, run simulations, and inspect clonal genealogies and population dynamics without requiring direct interaction with the underlying code. Using this model, we illustrate how ecological feedbacks can shape clonal dynamics over time, supporting an interpretation in which early tumor growth is dominated by stochastic expansion, while later evolution increasingly reflects selection for traits that alleviate environmental constraints. Rather than constituting a new evolutionary paradigm, this behaviour demonstrates how well-documented biological patterns can emerge naturally from a unified stochastic and ecological description. Overall, our approach offers a flexible and extensible platform for investigating how chance, functional traits, and environmental interactions jointly govern tumor heterogeneity.

SARS-CoV-2 has become an endemic disease, and we will have to face the continuous rise of new variants. Designing and evaluating the effects of new containment policies is of primary importance to keep social activities going as safely as possible according to the different stages of the pandemic. Therefore, we propose an Agent-Based Model to study the evolution of SARS-CoV-2 spread in a well-defined environment (of small/medium size, like a shop, a restaurant, an office, a school, with fewer than a hundred or a few hundred people) to assess the efficacy of different non-pharmaceutical interventions and vaccination strategies. Specifically, we focused on schools, given that the COVID-19 quarantine has resulted in substantial disruptions to education, leading to a transition to remote learning and worsening educational inequalities. We consider using face masks and several real-world testing protocols combined with quarantine policies. All protocols/policies have been evaluated at various stages of the pandemic evolution. Results show that testing campaigns are effective as far as the testing process is faster than the virus diffusion. Also, vaccination campaigns covering less than 40

Emerging hardware constraints are pushing workloads to become more composite. This transition involves new jobs where the HPC I/O systems are shared among multiple and concurrent jobs. This can generate load imbalances and contention in the end-to-end I/O paths, thus degrading the I/O system performance and the workloads. Recognizing this context, we define a simulation-based framework that alleviates resource contention in applications and ultimately allows us to design contention avoidance strategies. Specifically, by capturing behavior system-wide and extracting phases and characteristics of various performance metrics, we can mitigate contention by delaying the launch of applications. This framework leverages frequency domain analysis of performance metrics alongside clustering methods and is coupled with a comprehensive model of an HPC system implemented using Extended Stochastic Symmetric Nets.

This study aims to evaluate haematological parameters of patients who have contracted the COVID-19 infection. In particular, we considered patients who were already hospitalised at the time of nasopharyngeal swab (NF) testing, and patients at the Emergency Department and/or who required hospitalisation following a positive result from the NF swab. The collected data are defined as longitudinal data (i.e. constituted by measurements accumulated sequentially over time), mainly characterised by observation times that are irregular, different for each patient, and distributed non-uniformly across the observation interval. In light of these considerations, we exploit CONNECTOR, a data-driven framework designed for longitudinal data, which returns a grouping of the curves of the haemochromocytometric parameters, based on a functional clustering algorithm. These clusters are analysed in terms of disease outcome and survival, finding a good correlation to mortality rates. Finally, the CONNECTOR clusters are exploited to stratify the patients based on profiles of the haemochromocytometric parameters evolution over time, showing that comorbidities appear to have an impact on mortality independently of the outcome of the monitoring carried out through the laboratory tests considered.

Ordinary Differential Equations (ODEs) and Agent-Based Models (ABMs) represent nowadays the two main approaches for Immune System (IS) modeling. While the former approach does not allow for representing aleatory variations, the latter lacks a clear well-defined semantics, entailing possible biases on simulation results. We present here the application of our modeling pipeline, that has been designed to cope with these shortcomings, to a case-study about the competition between cancer and IS under the administration of a pre-clinical vaccine in transgenic mice. The pipeline involves the use of Extended Stochastic Symmetric Nets (ESSN) for a formal definition of the conceptual model, and allows to study the domain problem from a macro-perspective by means of the Stochastic Simulation Algorithm (SSA) or from a micro-perspective through an Agent Based Model with a clear defined semantics. The numerical results obtained in this study using SSA are presented and global sensitivity analysis is performed using Latin Hypercube Sampling - Partial Rank Correlation Coefficients (LHS-PRCC) to analyze and improve vaccine dosages and timings.

Predictive healthcare, driven by the availability of large medical datasets and computational advancements, plays a crucial role in enhancing patient care and optimizing healthcare system performance. However, training and deploying predictive models in healthcare presents substantial challenges, such as specialized expertise, and the need for state-of-the-art and costly hardware. To address these barriers, we introduce the Predictive Healthcare Platform (PHeP), an open-source platform designed to simplify the use of pre-trained models. Through a real-world case study, we showcase PHeP functionality in making advanced predictive healthcare accessible to users without extensive computational skills, or expensive computational resources. This will foster a wider adoption of predictive healthcare in medical practice.

Advancements in treating infectious diseases have improved in the past two decades, but the COVID-19 pandemic showed how quickly diseases can spread in today’s interconnected world. Computational epidemiology, using artificial intelligence and model simulations, helps experts analyse and control the spread of illnesses. This objective can be achieved using two different modelling paradigms: (i) macro-simulations , and (ii) micro-simulations. The former aims to characterize a system’s behaviour from a macroscopic perspective, utilizing mathematical methods like deterministic or stochastic processes to depict disease evolution and population dynamics. By concentrating on average or aggregated metrics, macro-simulations are especially useful for analysing broad trends and long-term implications. Differently, the latter aim at describing a system’s behaviour from a microscopic level in terms of its components and their interactions. Agent-Based Models are a common approach in this paradigm, representing individual agents with specific characteristics and decision-making rules. These models allow researchers to simulate complex, heterogeneous populations and capture localized or emergent phenomena, such as super-spreading events or the effects of targeted interventions [1] .

MOTIVATION:Computational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognizable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, e.g. to the cancer evolution study. RESULTS:To address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It enables functional studies by combining the analysis of the system's multi-level stable states with its fluctuating conditions. This approach helps to investigate the functional relationships and dependencies among biological entities. This is achieved, thanks to the hybridization of two analysis approaches that capture a system's different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposal's effectiveness was demonstrated by mechanistically simulating the metabolic output of Escherichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection. AVAILABILITY AND IMPLEMENTATION:GreatMod https://qbioturin.github.io/epimod/, epimod_FBAfunctions https://github.com/qBioTurin/epimod_FBAfunctions, first case study E. coli  https://github.com/qBioTurin/Ec_coli_modelling, second case study C. difficile  https://github.com/qBioTurin/EpiCell_CDifficile.

Biological phenomena are based on the precise and accurate cooperation of a non-random combination of molecules implicated in several pathways and networks. In the view of precision medicine, the plethora of omics data accrued sheds light on the comprehension of molecules cooperation. However, these data bring noise and redundancy that it is necessary to consider during the data analysis. A combination of omics data resources, integrated to parameterize mechanistic models, and multiphase Ensemble Feature Selection (EFS) is proposed. Through EFS, we characterized the metabolic heterogeneity of three distinct glycolysis-associated clusters (GACs) in colorectal cancer. Our study reveals that the EFS-derived genetic signatures associated with each GAC group also characterize three glycolysis profiles previously identified. GAC1 demonstrated unique separation, while GAC2 and GAC3 exhibited overlapping characteristics.

This paper introduces significant advancements in the GreatMod modeling framework, enhancing its capacity to simulate systems characterized by non-Markovian dynamics accurately. These enhancements include the definition of a novel graphical formalism tailored to represent such complex models, alongside an extension of the Stochastic Simulation Algorithm to accommodate their simulation efficiently. Moreover, we validate the robustness of these improvements through two case studies: the Susceptible-Infected-Recovered model and the Parallel-Producer-Consumer model.

Agent-based models are computational models that simulate the dynamic interactions, behaviours, and communication protocols among agents in a shared environment. The use of such models in the field of epidemiology has attracted much attention, allowing the evaluation of the effectiveness of possible interventions and vaccination strategies. However, setting up these environments typically requires a manual and technical process that can be both timeconsuming and complex. To address this challenge, we introduce Forge4Flame, a novel and user-friendly dashboard that simplifies the definition of agent-based models for FLAME GPU 2. Our goal is to make this modelling framework more accessible to a broader audience of researchers and public health professionals. Specifically, the tool streamlines model design, execution, and analysis by automatically generating the required FLAME GPU 2 code and incorporating valuable visualisation and post-processing features. Moreover, the integration of two different levels of population model was explored, allowing a detailed analysis of disease dynamics. This shows the tool's potential to enhance both the accessibility and scalability of agent-based models through Docker and Slurm for efficient distributed computing on highperformance computing systems. Finally, the effectiveness of this tool is demonstrated through a case study that investigates the COVID-19 emergency in a generic Italian middle school.

Agent-Based Modeling and Simulation (ABMS) has been increasingly applied in various research fields, thanks to the capability of these models to describe fine-grained realworld behavior and to the ease of interpretation by domain experts. However, such models lack a formal definition and well-defined semantics that are common to the different tools supporting ABMS. This may occasionally lead to greater complexity in interpreting the results with respect to other modeling approaches. To address this issue, an ABM semantics that adopts a continuous-time approach and a next-event time advance simulation algorithm has been formally defined and presented. Such an approach may lead to high computation times as it requires recalculations of activity rates for all the agents after each event. In this preliminary study, we exploit the FLAME GPU framework to evaluate the benefits that GPU computing may bring to the performance of our simulation algorithm.

Background The analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged. Results CREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.