Therapeutic synergy emerges from interactions between molecular drug action, intracellular signaling, and tissue-level transport dynamics. We developed a multiscale model integrating these scales to predict schedule-dependent drug combination effect in the AGS cell line. Calibrated solely on single-drug growth curves, the model accurately predicted population-level outcomes of drug combinations without combination-specific training. This demonstrates the model’s capacity to suggest mechanistic multiscale insights into the logic of drug combinations within the AGS cell line, establishing a computational platform for the systematic in silico exploration of virtual multiscale experiments on drug diffusion and dosing schedules. Cross-scale analysis revealed that combination therapy efficacy flows across scales: population-level pharmacokinetics dictate the sequence of molecular target engagement within individual cells, determining collective cell-fate decisions. Our simulations predict that inhibiting the PI3K/AKT axis before MEK is more effective than the reverse order, disabling a pro-survival rebound and locking cells into an apoptotic state. Ultimately, by capturing phenomena that single-scale approaches cannot, this framework generates translationally relevant hypotheses, providing a versatile platform for optimizing drug scheduling, formulation, and combination strategies where matched molecular Boolean models and phenotypic data are available.

Digital twins (DTs) represent the logical extension of systems biology modelling toward clinical application; however, current implementations are predominantly static, organ specific, and limited to single-instance personalisation. This review identifies five primary barriers impeding the development of adaptive, body-scale DTs: (1) parameter explosion driven by complexity, (2) weak structural identifiability, (3) lack of bidirectional multiscale coupling, (4) challenges in integrating heterogeneous asynchronous data, and (5) computational intractability for real-time applications. While each barrier has partial solutions, none is fully resolved. Hybrid mechanistic learning frameworks, such as neural ordinary differential equations, can uncover unknown couplings while maintaining interpretability. Modular architectures help manage complexity and identifiability, and uncertainty quantification supports riskaware clinical reasoning. Agentic systems offer a promising, though still exploratory, avenue that requires further development before clinical deployment. Progress in this field requires three key shifts: prioritising modular, identifiable models over encyclopaedic coverage; integrating mechanistic modelling with data-driven methods; and designing DTs as adaptive reasoning tools that support, rather than replace, clinical evaluation.

Agent-based cellular models simulate tissue evolution by capturing the behavior of individual cells, their interactions with neighboring cells, and their responses to the surrounding microenvironment. An important challenge in the field is scaling cellular resolution models to real-scale tumor simulations, which is critical for the development of digital twin models of diseases and requires the use of High-Performance Computing (HPC) since every time step involves trillions of operations. We hereby present a scalable HPC solution for the molecular diffusion modeling using an efficient implementation of state-of-the-art Finite Volume Method (FVM) frameworks. The paper systematically evaluates a novel scalable Biological Finite Volume Method (BioFVM) library and presents an extensive performance analysis of the available solutions. Results shows that our HPC proposal reach almost 200x speedup and up to 36

Summary:Rapid development of genomic technologies in recent years enables personalised medicine to become an essential part of healthcare. Advanced computational methods are required to extract relevant insights that can be applied in clinical settings. This presents a challenge for clinicians and biomedical researchers, who need specialised training to adopt these tools. Within the context of PerMedCoE, the first European Centre of Excellence in Personalised Medicine, we developed and delivered a competency-based training programme to support professionals in the life sciences to work with modelling and simulation tools that integrate omics data to identify biological processes relevant to disease. We identified a set of required competencies in the field and built a series of career profiles with specific competence levels in these. The competencies and profiles contributed to define the focus and target audience of the training activities delivered: a combination of self-paced learning resources, webinars and online and face-to-face synchronous courses. The outputs of the programme (competencies, career profiles and training materials) can be used by biomedical professionals for their own career development or to train others. In addition, the approach can be adopted by other fields with rapid technological advancements and a constant need to upskill professionals. Availability and implementation:The competency framework is reproduced in full in this paper as supplementary material and available on the Competency Hub at https://competency.ebi.ac.uk/framework/permedcoe/2.1.

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. ### Competing Interest Statement The authors have declared no competing interest. European Commission, https://ror.org/00k4n6c32, PerMedCoE 951773, CREXDATA 101092749, EDITH-CSA 101083771, Hanami EUROHPC-JU-2022-INCO-04-01, ARTEMIS UK BBSRC (Biotechnology and Biological Sciences Research Council), BB/V018930/1, BB/V01840X/1, BB/V018647/1 Australian Research Council, DP230100380, FT230100352 Generalitat Valenciana, CIDEXG/2023/22 Ministerio de Ciencia e Innovación, https://ror.org/05r0vyz12, CEX2021-001148-S BMBF (German federal ministry of Education and Research), LiSyM-Cancer, LiSyM-Cancer II

PhysiBoSS é uma plataforma de código aberto que integra a modelagem baseada em agentes de populações celulares com redes Booleanas estocásticas intracelulares, permitindo simulações multiescalares de comportamentos biológicos complexos. Para promover o compartilhamento e o versionamento de modelos, apresentamos o banco de dados PhysiBoSS-Models: um repositório curado de modelos multiescalares construídos com o PhysiBoSS. Ao oferecer uma API Python simples, o PhysiBoSS-Models permite baixar e simular modelos preexistentes com facilidade por meio de ferramentas como o PhysiCell Studio. Ao fornecer acesso padronizado a modelos validados, o PhysiBoSS-Models facilita a reutilização, validação e comparação de desempenho, apoiando pesquisas em biologia.

PhysiBoSS is an open-source platform that integrates agent-based modeling of cell populations with intracellular stochastic Boolean networks, enabling multiscale simulations of complex biological behaviors. To promote model sharing and versioning, we present the PhysiBoSS-Models database: a curated repository for multiscale models built with PhysiBoSS. By providing a simple Python API, PhysiBoSS-Models provides an easy way to download and simulate preexisting models through tools such as PhysiCell Studio. By providing standardized access to validated models, PhysiBoSS-Models facilitates reuse, validation, and benchmarking, supporting research in biology.

Digital twins, initially developed for industrial applications, are set to make significant advancements in medicine and healthcare. They have demonstrated promising potential for drug development and personalised care, especially in cardiovascular diagnostics and insulin-dependent diabetes management. A particularly compelling application lies in immune responses and immune-mediated diseases, given the immune system’s essential role in preserving human health, from fighting infections to managing autoimmune diseases. Creating Immune Digital Twins (IDTs) holds great promise for medicine and healthcare. At the same time, the development of a reliable and robust IDT presents significant challenges due to the inherent complexity and polymorphism of the human immune system, the difficulties in measuring patients’ immune state in vivo, and the intrinsic difficulties associated with modelling complex biological systems and processes.The Working Group “Building Immune Digital Twins” (BIDT WG) aims to address these challenges by fostering transdisciplinary collaborations among immunologists, clinicians, experimentalists, computational biologists, and engineers. The international network is leveraging its cross-disciplinary expertise to build the components required for a working IDT model. Moreover, the BIDT WG focuses on creating an open-access model repository for publicly available immune-related computational models and their required metadata. The group is also active in cataloguing open-access tools, methodologies, and software to identify interoperability gaps in the current modelling landscape.Consequently, this work can drive transformative innovations in precision medicine, unlocking new possibilities for the diagnosis, treatment, and management of immune-mediated diseases.

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.

Introduction: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. Methods:Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results:Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion:The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature and scales of the system under study. For instance, signalling and regulatory networks can be simulated using Boolean modelling, whereas multicellular systems can be studied using agent-based modelling. Herein, we present PhysiBoSS 2.0, a hybrid agent-based modelling framework that allows simulating signalling and regulatory networks within individual cell agents. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS 1.0 and was conceived as an add-on that expands the PhysiCell functionalities by enabling the simulation of intracellular cell signalling using MaBoSS while keeping a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 also expands the set of functionalities offered to the users, including custom models and cell specifications, mechanistic submodels of substrate internalisation and detailed control over simulation parameters. Together with PhysiBoSS 2.0, we introduce PCTK, a Python package developed for handling and processing simulation outputs, and generating summary plots and 3D renders. PhysiBoSS 2.0 allows studying the interplay between the microenvironment, the signalling pathways that control cellular processes and population dynamics, suitable for modelling cancer. We show different approaches for integrating Boolean networks into multi-scale simulations using strategies to study the drug effects and synergies in models of cancer cell lines and validate them using experimental data. PhysiBoSS 2.0 is open-source and publicly available on GitHub with several repositories of accompanying interoperable tools.

Hemodialysis treatment is a live complex adaptive system undergoing continuous change. Patients' hemodynamic response is influenced by multiple agents (from genomics, cellular abnormalities and treatments, to healthcare infrastructures and social resources), acting in ways that are not always totally predictable, but whose actions are interconnected and can change the context for other agents. Consequently, each patient requires a personalized treatment that may not fit general recommendations based on a wide interpretation and extrapolation of expert-based guidelines. However, the problem resides in how to best analyze this complexity using computational models that could be used for an individualized streaming decision support system in dialysis. Simple models show good results in a single variable prediction, but are ineffective when not full information is at our disposal at the moment of prediction. In addition, some clinical variables have a complex and dynamic behavior, so a cut-off point obtained in a single observation cannot be established and requires continuous evaluation over time. In this review, we aim to describe non-linear and complex clinical variables and put forward some methods to predict individual patient response during dialysis treatment using computational models. We also describe new architectural developments for the personalization of treatments and the potential application of the concept of Digital Twins to dialysis. Finally, we summarize the state of the art for personalized dialysis and point which future technological are needed.

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 silicoexperiments are suggested to search for possible targets that can block the more invasive tumoral phenotypes.

This work presents a comprehensive performance analysis and optimization of a multiscale agent-based cellular simulation. The optimizations applied are guided by detailed performance analysis and include memory management, load balance, and a locality-aware parallelization. The outcome of this paper is not only the speedup of 2.4x achieved by the optimized version with respect to the original PhysiCell code, but also the lessons learned and best practices when developing parallel HPC codes to obtain efficient and highly performant applications, especially in the computational biology field.

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell-cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells' spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells' spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.

Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a Genetic Algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.

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.

Agent-based modelling has proven its usefulness in several biomedical projects by explaining and uncovering mechanisms in diseases. Nevertheless, the scenarios addressed in these models usually consider a small number of cells, lack cell-specific characterisation and dynamic interactions and have a simplistic environment description. Tools that enable scalable, real-sized simulations of biological systems that require complex setups are needed to have simulations closer to biomedical scenarios that can capture cell-to-cell heterogeneity and system-wide emerging properties. To deliver simulations at the giga-scale (109 cells), different tools have implemented technologies to run in high-performance computing clusters. We hereby review these efforts and detail the main areas of improvement the field needs to focus on to have simulations that are a step closer to having digital twins.

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.