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L'exposició a la contaminació de l'aire és el principal risc ambiental per a la salut a causa dels seus efectes perjudicials respiratoris i cardiovasculars. Avaluar l'exposició d'una població és una tasca difícil a causa dels seus complexos patrons de mobilitat. Ignorar aquest factor podria conduir a biaixos sistemàtics a l'hora d'avaluar l'efecte de la contaminació de l'aire en les condicions de salut. En aquest estudi, hem utilitzat un conjunt de dades públic de mobilitat representatiu de la població de Catalunya per estimar l'exposició a la contaminació atmosfèrica basada en la mobilitat (estimacions dinàmiques) i quantificar el biaix comès quan s’ignoren les dinàmiques poblacionals (estimacions estàtiques). Dades detallades de qualitat de l'aire de 2022 per a NO₂, O₃, PM2.5 i PM10 van ser utilitzades per computar l'exposició a la contaminació de l'aire, i es van comparar estimacions estàtiques i dinàmiques. Entre el 84 i el 95% de les àrees de mobilitat van mostrar estimacions d'exposició dinàmica significativament diferents per a algun dels quatre contaminants. La magnitud d'aquestes diferències no va ser prou gran com per suposar un impacte rellevant en la salut quan es consideren les poblacions agregades. No obstant això, algunes de les poblacions mòbils van estar exposades a nivells de contaminació de l'aire insegurs durant un nombre addicional de dies important del que esperaríem en un marc estàtic, especialment per al NO₂ (fins a 60 dies addicionals). Espacialment, les àrees que envolten l'Àrea Metropolitana de Barcelona (AMB) van tendir a tenir estimacions d'exposició dinàmica incrementades per a NO₂, PM2.5 i PM10 —i disminuides per a O₃— a causa dels seus fluxos de persones que van a treballar a la AMB. Aquest estudi evidencia i quantifica l'efecte negatiu de l’AMB sobre l'exposició als contaminants atmosfèrics de les poblacions circumdants. A més, destaquem la importància d'utilitzar dades de mobilitat amb alta resolució espacial., La exposición a la contaminación del aire es el principal riesgo ambiental para la salud debido a sus efectos respiratorios y cardiovasculares perjudiciales. Evaluar la exposición de una población es una tarea difícil debido a sus complejos patrones de movilidad. Ignorar este factor podría generar sesgos sistemáticos al evaluar el efecto de la contaminación del aire en las condiciones de salud. En este estudio, utilizamos un conjunto de datos público de movilidad representativo de la población de Cataluña para estimar la exposición a la contaminación atmosférica basada en movilidad (estimaciones dinámicas) y cuantificar el sesgo cometido cuando se ignoran las dinámicas poblacionales (estimaciones estáticas). Datos detallados de calidad del aire de 2022 de NO₂, O₃, PM2.5 y PM10 fueron utilizados para computar la exposición a la contaminación del aire, y se compararon estimaciones estáticas y dinámicas. Entre el 84 y el 95% de las áreas de movilidad mostraron estimaciones de exposición dinámica significativamente diferentes para alguno de los cuatro contaminantes. La magnitud de estas diferencias no fue lo suficientemente grande como para suponer un impacto relevante en la salud al considerar las poblaciones agregadas. Sin embargo, algunas de las poblaciones móviles estuvieron expuestas a niveles inseguros de contaminación del aire durante un número de días adicional importante respecto a lo que esperaríamos en un marco estático, especialmente para NO₂ (hasta 60 días adicionales). Espacialmente, las áreas que rodean el Área Metropolitana de Barcelona (AMB) tendieron a tener estimaciones de exposición dinámica incrementadas para NO₂, PM2.5 y PM10—y reducidas para O₃—debido a sus flujos de personas que van a trabajar a la AMB. Este estudio evidencia y cuantifica el efecto negativo de la AMB sobre la exposición a contaminantes atmosféricos en las poblaciones aledañas. Además, destacamos la importancia de utilizar datos de movilidad con alta resolución espacial., Air pollution exposure is the leading environmental health risk due to its detrimental respiratory and cardiovascular effects. Assessing a population's exposure is a challenging task because of their complex mobility patterns. Ignoring this factor could lead to systematic biases when evaluating the effect of air pollution on health outcomes. In this study, we used a public mobility dataset representative of the population of Catalonia to estimate mobility-informed air pollution exposure (dynamic estimates) and quantify the bias committed when population dynamics are neglected (static estimates). Fine-grained air quality data of 2022 for NO₂, O₃, PM2.5 and PM10 was projected over the mobility areas to compute air pollution exposure, and static and dynamic estimates were compared. Between 84 and 95% of the mobility areas showed significantly different dynamic exposure estimates for any of the four pollutants. The magnitude of these differences was not large enough to entail a relevant health impact when considering the aggregated populations (the mobile population supposed 10% of the population of an area, on average). However, some of the mobile populations were exposed to unsafe air pollution levels for an important additional number of days than we would expect on a static setting, especially for NO₂ (up to 60 extra days). Spatially, the areas surrounding the Barcelona Metropolitan Area (BMA) tended to have increased dynamic exposure estimates for NO₂, PM2.5 and PM10—and decreased for O₃—caused by their flows of people going to work to the BMA. This study evidences and quantifies the negative effect of the BMA on the exposure to air pollutants in the surrounding populations. In addition, we highlight the importance of using mobility data with high spatial resolution when assessing dynamic air pollution exposure at the population level. If the resolution is not high enough, although more accurate, the dynamic estimates will not differ much from the static ones.

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., Peer Reviewed

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., The authors thank Paul Macklin and Randy Heiland for their support with the PhysiCell code and discussions. The authors acknowledge the technical expertise and assistance provided by the Spanish Supercomputing Network (Red Española de Supercomputación), as well as the computer resources used: the LaPalma Supercomputer (RES-BCV-2019-2-0008), located at the Instituto de Astrofísica de Canarias, and MareNostrum4 (RES-BCV-2019-2-0013, RES-BCV-2020-3-0016, RES-BCV-2021-2-0020), located at the Barcelona Supercomputing Center. This work has received funding from the Horizon 2020 projects: INFORE (ID: 825070), PerMedCoE (ID: 951773) and CREXDATA (ID: 101092749). This work has received funding from the Horizon 2020 projects INFORE (ID: 825070) and PerMedCoE (ID: 951773) and from the Horizon Europe project CREXDATA (ID: 101092749). This work was funded in part by the French government under the management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” programme, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute)., Peer Reviewed, Postprint (published version)

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. 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 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. 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., The author(s) declare financial support was received for the research, authorship, and/or publication of this article. AN acknowledges support from SANOFI-AVENTIS R&D via the CIFRE contract, n° 2020/0766. MK, FH, NP, FE, and CE acknowledge the support of the ZonMw COVID-19 programme (Grant No.10430012010015). JD Spanish Ministry of Science and Innovation (Grant no. PID2020-117979RB-I00) and Instituto de Salud Carlos III (Grant no. IMP/00019). MAi, KK, FS: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -Project-ID 251654672 - TRR 161 and under Germany’s Excellence Strategy - EXC 2117 - 422037984. FM: “5 per 1000–2021” grant of the Italian Ministry of Health (Grant No. 5M-2021-23683787) and European Commission with HORIZON programme, BY-COVID project (Grant No. 101046203—BY-COVID). National Institute for Infectious Diseases Lazzaro Spallanzani–IRCCS received financial support from the Italian Ministry of Health grant “Ricerca Corrente”. JP, LF: IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA:964537 RISKHUNT3R]; Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya; Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, “Away to make Europe”). AMo, MP and AV acknowledge the support of the European Commission under the INFORE project (H2020-ICT825070) and the PerMedCoE (H2020-ICT-951773). Contributions by TH and BLP were supported by NIH grant #R35GM119770 to TH. MaGo acknowledges funding from Deutsche Forschungsgemeinschaft (DFG) through grants no. 442326535 (NFDI4Health) and 451265285 (NFDI4Health Task Force COVID-19), from the European Commission through the Horizon 2020 framework program under grant no. 8, Peer Reviewed, "Article signat per més de 50 autors/es: Anna Niarakis, Marek Ostaszewski, Alexander Mazein, Inna Kuperstein, Martina Kutmon, Marc E. Gillespie, Akira Funahashi, Marcio L. Acencio, Ahmed Hemedan, Michael Aichem, Karsten Klein, Tobias Czauderna, Felicia Burtscher, Takahiro G. Yamada, Yusuke Hiki, Noriko F. Hiroi, Finterly Hu, Nhung Pham, Friederike Ehrhart, Egon Willighagen, Alberto Valdeolivas, Aurelien Dugourd, Francesco Messina, Marina Esteban Medina, Maria Peña-Chilet, Kinza Rian, Sylvain Soliman, Sara S. Aghamiri, Bhanwar Lal Puniya, Aurélien Naldi, Tomáš Helikar, Vidisha Singh, Marco Fariñas Fernández, Viviam Bermudez, Eirini Tsirvouli, Arnau Montagud, Vincent Noël, Miguel Ponce De Leon, Dieter Maier, Angela Bauch, Benjamin M. Gyori, John A. Bachman, Augustin Luna, Janet Piñero, Laura I. Furlong, Irina Balaur, Adrien Rougny, Yohan Jarosz, Rupert W. Overall, Robert Phair, Livia Perfetto, Lisa Matthews, Devasahayam Arokia Balaya Rex, Marija Orlic-Milacic, Cristobal Luis, Bertrand De Meulder, Jean M. Ravel, Bijay Jassal, Venkata P. Satagopam, Guanming Wu, Martin Golebiewski, Piotr Gawron, Laurence Calzone, Jacques S. Beckmann, Chris Evelo, Peter D'eustachio, Falk Schreiber, Julio Saez-Rodriguez, Joaquin Dopazo, Martin Kuiper, Alfonso Valencia, Olaf Wolkenhauer, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider ", Postprint (published version)

We introduce RATS (Resource Allocator for Tumor Simulations), the first optimizer for the execution of tumor simulations over HPC infrastructures. The optimization framework of RATS incorporates 3 vital performance criteria (i) expected utility of a simulation in terms of effective drug combination on the simulated tumor, (ii) simulation execution time and (iii) number of cores required for achieving that execution time. RATS is to be used by life scientists at the Barcelona Supercomputing Center to not only remove the burden of blindly guessing the core hours we need to reserve from HPC admins to study various tumor treatment methodologies, but also to help in more rapidly distinguishing effective drug combinations, thus, potentially cutting time to market for new cancer therapies., E. Streviniotis, N. Giatrakos and Y. Kotidis were supported by the EU project EVENFLOW under Horizon Europe agreement No. 101070430. T. Ntiniakou and M. Ponce de Leon were supported by the EU project CREXDATA under Horizon Europe agreement No. 101092749., Peer Reviewed, Postprint (author's final draft)

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., This work has received funding from the EU Horizon 2020 RIA program INFORE under grant agreement No 825070, Peer Reviewed, Postprint (author's final draft)

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., This research has received funding from the Horizon 2020 INFORE Project, GA n° 825070 and the Horizon 2020 PerMedCoE Project, GA n° 951773., Peer Reviewed, Postprint (published version)

The world has gone through unprecedented changes since the global pandemic hit. During the early phase of the pandemic, the absence of known drugs or pharmaceutical treatments forced governments to introduce different policies in order to help reduce contagion rates and manage the economic consequences of the pandemic. This paper analyses the causal impact on mobility and COVID19 incidence from policy makers in Cataluña, Spain. We use anonymized phone-based mobility data together with reported incidence and apply a series of causal impact models frequently used in econometrics and policy evaluation in order to measure the policies impact. We analyse the case of Cataluña and the public policy decision of closing all bars and restaurants down for a 5 week period between 2020-16-10 and 2020-23-11. We find that this decision led to a significant reduction in mobility. It not only led to reductions in mobility but from a behavioural economics standpoint, we highlight how people responded to the policy decision. Moreover, the policy of closing bars and restaurants slowed the incidence rate of COVID19 after a time lag has been taken into account. These findings are significant since governments worldwide want to restrict movements of people in order to slow down COVID19 incidence without infringing on their rights directly., Peer Reviewed, Postprint (published version)

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

PhysiCell/BioFVM is an opopen-sourcend agent-based software package supporting 2D/3D simulations for multi-cellular biological systems. It is completely written in C++ and enjoys shared-memory parallelization through OpenMP. An attempt is being made to re-structure the code-base to add support for Distributed parallelization through MPI. The biggest bottleneck in the current version of the simulation package is the serial Thomas solver that is used to solve the triadiagonal system of linear equations resulting from the Finite Volume Discretization Method (FVM) of the reaction-diffusion equations modelling the secretion, ingestion of substrates from cells/agents. Our aim in this project is to replace the serial solver with an efficient distributed-parallel solver. For this purpose we experiment with the Cyclic Reduction (CR) algorithm on a shared-memory system but after understanding its limitations with regard to the problem size, we settle on a modified version of the Thomas solver as our preferred choice in distributed-parallel settings. Our experiments show that the Cyclic Reduction algorithm implemented using OpenMP is able to outperform the serial Thomas solver at a certain thread count on a single node. However, we do not extend this algorithm to support distributed parallelism due to the aforementioned problem. Further, we implement an MPI-only version of the modified Thomas algorithm that promises good scalability on multiple nodes of our HPC cluster - the MareNostrum 4 (MN4) supercomputer at the Barcelona Supercomputing Center (BSC). We project and optimistically conclude that for large problem sizes and a high core count, the parallel modified Thomas algorithm can offer significant reduction in the time to solution for complex 3D simulations in PhysiCell/BioFVM.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions., This work was supported by the Generalitat de Catalunya through the project PDAD14/20/00001, and by the H2020 programme under Grant Agreement 825070 (INFORE) and the INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF)., Peer Reviewed, Postprint (published version)

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective., We would like to thank Andjela Tatarovic, architect, and Gina Crovetto, a researcher in the field of cancer, for their help with the design of the top-level view diagrams. We would like to acknowledge the Responsible and Reproducible Research (R3) team of the Luxembourg Centre for Systems Biomedicine for supporting the project and providing necessary communication and data sharing resources. The work presented in this paper was carried out using the ELIXIR Luxembourg tools and services. This study was supported by the Luxembourg National Research Fund (FNR) COVID-19 Fast-Track grant programme, grant COVID-19/2020-1/14715687/CovScreen (E. Glaab); European Commission, INFORE grant H2020-ICT-825070 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia); European Commission, PerMedCoE grant H2020-ICT-951773 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia) the Federal Ministry of Education and Research (BMBF, Germany) and the Baden-Württemberg Ministry of Science, the Excellence Strategy of the German Federal and State Governments (A. Renz); German Center for Infection Research (DZIF), grant no 8020708703 (A. Dräger); The Netherlands Organisation for Health Research and Development (ZonMw), grant no 10430012010015, (M. Kutmon, S. Coort, F. Ehrhart, N. Pham, E.L. Willighagen, C.T. Evelo); H2020 Marie Skłodowska-Curie Actions, grant number 765274 (J. Scheel); National Institutes of Health, USA (NIH), grant number U41 HG003751 (L.D. Stein). The development of Reactome is supported by grants from the US National Institutes of Health (U41 HG003751) and the European Molecular Biology Laboratory., Peer Reviewed, "Article signat per més de 50 autors/es: Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta-Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, Francesco Messina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang,Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, Devasahayam Arokia Balaya,Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic-Milacic, Andrea Senff Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean-Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen51,Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco57, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Postprint (published version)

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 set-ups 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, This work has been partially supported by the European Commission under the INFORE project (H2020-ICT-825070) and the PerMedCoE project (H2020-ICT-951773)., Peer Reviewed, Postprint (published version)

Multi-scale simulations require parallelization to address large-scale problems, such as real-sized tumor simulations. BioFVM is a software package that solves diffusive transport Partial Differential Equations for 3-D biological simulations successfully applied to tissue and cancer biology problems. Currently, BioFVM is only shared-memory parallelized using OpenMP, greatly limiting the execution of large-scale jobs in HPC clusters. We present BioFVM-X: an enhanced version of BioFVM capable of running on multiple nodes. BioFVM-X uses MPI+OpenMP to parallelize the generic core kernels of BioFVM and shows promising scalability in large 3-D problems with several hundreds diffusible substrates and ≈ 0.5 billion voxels. The BioFVM-X source code, examples and documentation, are available under the BSD 3-Clause license at https://gitlab.bsc.es/gsaxena/biofvm_x., The research leading to these results has received funding from EU H2020 Programme under the PerMedCoE project, grant agreement number 951773 and the INFORE project, grant agreement number 825070. The authors would like to thank Paul Macklin and Randy Heiland from Indiana University for their constant support and advice regarding BioFVM., Peer Reviewed, Postprint (published version)

Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded SIR model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies., This material is based upon work supported by the European Commission’s Horizon 2020 Program, H2020-ICT-12-2018-2020, “INFORE - Interactive Extreme-Scale Analytics and Forecasting” (ref. 825070). The authors would like to acknowledge the Spanish Ministry of Transport, Mobility and Urban Agenda for assistance and support with mobility data., Peer Reviewed, Postprint (published version)

We present INforE, a prototype supporting non-expert programmers in performing optimized, cross-platform, streaming analytics at scale. INforE offers: a) a new extension to the RapidMiner Studio for graphical design of Big streaming Data workflows, (b) a novel optimizer to instruct the execution of workflows across Big Data platforms and clusters, (c) a synopses data engine for interactivity at scale via the use of data summaries, (d) a distributed, online data mining and machine learning module. To our knowledge INforE is the first holistic approach in streaming settings. We demonstrate INforE in the fields of life science and financial data analysis., This work has received funding from the EU Horizon 2020 research and innovation program INFORE under grant agreement No 825070, Peer Reviewed, Postprint (author's final draft)

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism-which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca. Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid iBSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with iBSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions.

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism-which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca. Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid iBSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with iBSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions.