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| Building multiscale models with PhysiBoSS, an agent-based modeling tool |
| Marco Ruscone, Andrea Checcoli, Randy Heiland, Emmanuel Barillot, Paul Macklin, Laurence Calzone, Vincent Noël |
| arXiv preprint |
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| Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. They aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms -- coupled with a graphical interface -- is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to easily construct such models, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as a Supplementary Material and all models are provided at: https://physiboss.github.io/tutorial. |
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| PhysiCell Studio: a graphical tool to make agent-based modeling more accessible |
| Randy Heiland, Daniel Bergman, Blair Lyons, Julie Cass, Heber L Rocha, Marco Ruscone, Vincent Noël, Paul Macklin |
| Gigabytes, PhysiCell Ecosystem |
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| Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification – 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 GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: 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. There is both a desktop and cloud version. 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. |
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| MaBoSS for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators |
| Adam Šmelko, Miroslav Kratochvíl, Emmanuel Barillot, Vincent Noël |
| BMC bioinformatics, 25:1, 199 |
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| Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations. These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community. |
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| Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates |
| Eirini Tsirvouli, Vincent Noël, Åsmund Flobak, Laurence Calzone, Martin Kuiper |
| Iscience 27:2 |
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| Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation, and a perturbed keratinocyte life cycle. Despite the availability of drugs for psoriasis management, the disease remains incurable. Treatment response variability calls for new tools and approaches to comprehend the mechanisms underlying disease development. We present a Boolean multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network analysis, the model predictions suggest that targeting neutrophil activation in conjunction with inhibition of either prostaglandin E2 (PGE2) or STAT3 shows promise comparable to interleukin-17 (IL-17) inhibition, one of the most effective treatment options for moderate and severe cases. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling in psoriasis and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification. |
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| Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches |
| Anna Niarakis, Marek Ostaszewski, Alexander Mazein, Inna Kuperstein, Martina Kutmon, Marc E Gillespie, Akira Funahashi, Marcio Luis 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 L Willighagen, Alberto Valdeolivas, Aurelien Dugourd, Francesco Messina, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Sylvain Soliman, Sara Sadat Aghamiri, Bhanwar Lal Puniya, Aurelien 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, Luis Cristobal Monraz Gomez, Bertrand De Meulder, Jean Marie Ravel, Bijay Jassal, Venkata Satagopam, Guanming Wu, Martin Golebiewski, Piotr Gawron, Laurence Calzone, Jacques S Beckmann, Chris T 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, COVID-19 Disease Map Community |
| Frontiers in Immunology, 14, 1282859 |
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| 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. |
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| PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks |
| Miguel Ponce-de-Leon, Arnau Montagud, Vincent Noel, Gerard Pradas, Annika Meert, Emmanuel Barillot, Laurence Calzone, Alfonso Valencia |
| npj Systems Biology and Applications, 9:54 (2023) |
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| 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. |
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| Multiscale model of the different modes of cancer cell invasion |
| Marco Ruscone, Arnau Montagud, Philippe Chavrier, Olivier Destaing, Isabelle Bonnet, Andrei Zinovyev, Emmanuel Barillot, Vincent Noël, Laurence Calzone |
| Bioinformatics, 39:6, June 2023 |
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| Mathematical models of biological processes altered in cancer are built using the knowledge of complex networks of signaling pathways, detailing 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 and to predict means to block it by considering not only spatial information obtained from the agent-based simulation but also 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. |
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| Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology |
| Anna Niarakis, Dagmar Waltemath, James Glazier, Falk Schreiber, Sarah M. Keating, David Nickerson, Claudine Chaouiya, Anne Siegel, Vincent Noël, Henning Hermjakob, Tomáˇs Helikar, Sylvain Soliman and Laurence Calzone |
| Briefings in Bioinformatics, 2022, 23(4), 1–11 |
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| Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field. |
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| UPMaBoSS: A Novel Framework for Dynamic Cell Population Modeling |
| Gautier Stoll, Aurélien Naldi, Vincent Noël, Eric Viara, Emmanuel Barillot, Guido Kroemer, Denis Thieffry and Laurence Calzone |
| Frontiers in Molecular Biosciences |
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| Mathematical modeling aims at understanding the effects of biological perturbations, suggesting ways to intervene and to reestablish proper cell functioning in diseases such as cancer or in autoimmune disorders. This is a difficult task for obvious reasons: the level of details needed to describe the intra-cellular processes involved, the numerous interactions between cells and cell types, and the complex dynamical properties of such populations where cells die, divide and interact constantly, to cite a few. Another important difficulty comes from the spatial distribution of these cells, their diffusion and motility. All of these aspects cannot be easily resolved in a unique mathematical model or with a unique formalism. To cope with some of these issues, we introduce here a novel framework, UPMaBoSS (for Update Population MaBoSS), dedicated to modeling dynamic populations of interacting cells. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks. A novel software layer is added to account for cell interactions and population dynamics, but without considering the spatial dimension. This modeling approach can be seen as an intermediate step towards more complex spatial descriptions. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time. To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within the CoLoMoTo Docker image, which contains all software and models used for this study. |
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| UPMaBoSS: A Novel Framework for Dynamic Cell Population Modeling |
| Gautier Stoll, Aurélien Naldi, Vincent Noël, Eric Viara, Emmanuel Barillot, Guido Kroemer, Denis Thieffry and Laurence Calzone |
| Frontiers in Molecular Biosciences |
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| Mathematical modeling aims at understanding the effects of biological perturbations, suggesting ways to intervene and to reestablish proper cell functioning in diseases such as cancer or in autoimmune disorders. This is a difficult task for obvious reasons: the level of details needed to describe the intra-cellular processes involved, the numerous interactions between cells and cell types, and the complex dynamical properties of such populations where cells die, divide and interact constantly, to cite a few. Another important difficulty comes from the spatial distribution of these cells, their diffusion and motility. All of these aspects cannot be easily resolved in a unique mathematical model or with a unique formalism. To cope with some of these issues, we introduce here a novel framework, UPMaBoSS (for Update Population MaBoSS), dedicated to modeling dynamic populations of interacting cells. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks. A novel software layer is added to account for cell interactions and population dynamics, but without considering the spatial dimension. This modeling approach can be seen as an intermediate step towards more complex spatial descriptions. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time. To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within the CoLoMoTo Docker image, which contains all software and models used for this study.
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| PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks |
| Miguel Ponce-de-Leon, Arnau Montagud, Vincent Noel, Gerard Pradas, Annika Meert, Emmanuel Barillot, Laurence Calzone, Alfonso Valencia |
| Preprint, bioRxiv (2022) |
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| 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. |
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| COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms |
| 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 Rex, 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 Owen, 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 Sacco, 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, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Willighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D'Eustachio, Julio Saez‐Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, COVID‐19 Disease Map Community |
| Molecular systems biology, 17(10), e10387 |
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| 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. |
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| Setting the basis of best practices and standards for curation and annotation of logical models in biology—highlights of the [BC] 2 2019 CoLoMoTo/SysMod Workshop |
| Anna Niarakis, Martin Kuiper, Marek Ostaszewski, Rahuman S Malik Sheriff, Cristina Casals-Casas, Denis Thieffry, Tom C Freeman, Paul Thomas, Vasundra Touré, Vincent Noël, Gautier Stoll, Julio Saez-Rodriguez, Aurélien Naldi, Eugenia Oshurko, Ioannis Xenarios, Sylvain Soliman, Claudine Chaouiya, Tomáš Helikar, Laurence Calzone |
| Briefings in bioinformatics 22.2 (2021): 1848-1859. |
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| The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements. |
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| Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers |
| Jonas Béal, Lorenzo Pantolini, Vincent Noël, Emmanuel Barillot, Laurence Calzone |
| PLOS Computational Biology 17.1 (2021): e1007900. |
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| The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles. We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets. This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis. |
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| WebMaBoSS: A Web Interface for Simulating Boolean Models Stochastically |
| Vincent Noël, Marco Ruscone, Gautier Stoll, Eric Viara, Andrei Zinovyev, Emmanuel Barillot, Laurence Calzone |
| Frontiers in molecular biosciences 8 (2021). |
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| WebMaBoSS is an easy-to-use web interface for conversion, storage, simulation and analysis of Boolean models that allows to get insight from these models without any specific knowledge of modeling or coding. It relies on an existing software, MaBoSS, which simulates Boolean models using a stochastic approach: it applies continuous time Markov processes over the Boolean network. It was initially built to fill the gap between Boolean and continuous formalisms, i.e., providing semi-quantitative results using a simple representation with a minimum number of parameters to fit. The goal of WebMaBoSS is to simplify the use and the analysis of Boolean models coping with two main issues: 1) the simulation of Boolean models of intracellular processes with MaBoSS, or any modeling tool, may appear as non-intuitive for non-experts; 2) the simulation of already-published models available in current model databases (e.g., Cell Collective, BioModels) may require some extra steps to ensure compatibility with modeling tools such as MaBoSS. With WebMaBoSS, new models can be created or imported directly from existing databases. They can then be simulated, modified and stored in personal folders. Model simulations are performed easily, results visualized interactively, and figures can be exported in a preferred format. Extensive model analyses such as mutant screening or parameter sensitivity can also be performed. For all these tasks, results are stored and can be subsequently filtered to look for specific outputs. This web interface can be accessed at the address: https://maboss.curie.fr/webmaboss/ and deployed locally using docker. This application is open-source under LGPL license, and available at https://github.com/sysbio-curie/WebMaBoSS. |
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| Exact solving and sensitivity analysis of stochastic continuous time Boolean models |
| Mihály Koltai, Vincent Noël, Andrei Zinovyev, Laurence Calzone, Emmanuel Barillot |
| BMC bioinformatics 21.1 (2020): 1-22. |
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| Background: Solutions to stochastic Boolean models are usually estimated by Monte
Carlo simulations, but as the state space of these models can be enormous, there is an
inherent uncertainty about the accuracy of Monte Carlo estimates and whether
simulations have reached all attractors. Moreover, these models have timescale
parameters (transition rates) that the probability values of stationary solutions depend
on in complex ways, raising the necessity of parameter sensitivity analysis. We address
these two issues by an exact calculation method for this class of models.
Results: We show that the stationary probability values of the attractors of stochastic
(asynchronous) continuous time Boolean models can be exactly calculated. The
calculation does not require Monte Carlo simulations, instead it uses graph theoretical
and matrix calculation methods previously applied in the context of chemical kinetics.
In this version of the asynchronous updating framework the states of a logical model
define a continuous time Markov chain and for a given initial condition the stationary
solution is fully defined by the right and left nullspace of the master equation’s kinetic
matrix. We use topological sorting of the state transition graph and the dependencies
between the nullspaces and the kinetic matrix to derive the stationary solution without
simulations. We apply this calculation to several published Boolean models to analyze
the under-explored question of the effect of transition rates on the stationary solutions
and show they can be sensitive to parameter changes. The analysis distinguishes
processes robust or, alternatively, sensitive to parameter values, providing both
methodological and biological insights.
Conclusion: Up to an intermediate size (the biggest model analyzed is 23 nodes)
stochastic Boolean models can be efficiently solved by an exact matrix method,
without using Monte Carlo simulations. Sensitivity analysis with respect to the model’s
timescale parameters often reveals a small subset of all parameters that primarily
determine the stationary probability of attractor states. |
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| Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision |
| Stéphanie Chevalier, Vincent Noël, Laurence Calzone, Andrei Zinovyev, Loïc Paulevé |
| International Conference on Computational Methods in Systems Biology, pp. 193-209 |
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| The construction of models of biological networks from prior knowledge and experimental data often leads to a multitude of candidate models. Devising a single model from them can require arbitrary choices, which may lead to strong biases in subsequent predictions. We introduce here a methodology for a) synthesizing Boolean model ensembles satisfying a set of biologically relevant constraints and b) reasoning on the dynamics of the ensembles of models. The synthesis is performed using Answer-Set Programming, extending prior work to account for solution diversity and universal constraints on reachable fixed points, enabling an accurate specification of desired dynamics. The sampled models are then simulated and the results are aggregated through averaging or can be analyzed as a multi-dimensional distribution. We illustrate our approach on a previously published Boolean model of a molecular network regulating the cell fate decisions in cancer progression. It appears that the ensemble-based approach to Boolean modelling brings new insights on the variability of synergistic interacting mutations effect concerning propensity of a cancer cell to metastasize. |
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| Dynamical Boolean Modeling of Immunogenic Cell Death |
| Andrea Checcoli, Jonathan G Pol, Aurelien Naldi, Vincent Noël, Emmanuel Barillot, Guido Kroemer, Denis Thieffry, Laurence Calzone, Gautier Stoll |
| Frontiers in Physiology |
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| As opposed to the standard tolerogenic apoptosis, immunogenic cell death (ICD) constitutes a type of cellular demise that elicits an adaptive immune response. ICD has been characterized in malignant cells following cytotoxic interventions, such as chemotherapy or radiotherapy. Briefly, ICD of cancer cells releases some stress/danger signals that attract and activate dendritic cells (DCs). The latter can then engulf and cross-present tumor antigens to T lymphocytes, thus priming a cancer-specific immunity. This series of reactions works as a positive feedback loop where the antitumor immunity further improves the therapeutic efficacy by targeting cancer cells spared by the cytotoxic agent. However, not all chemotherapeutic drugs currently approved for cancer treatment are able to stimulate bona fide ICD: some commonly used agents, such as cisplatin or 5-fluorouracil, are unable to activate all features of ICD. Therefore, a better characterization of the process could help identify some gene or protein candidates to target pharmacologically and suggest combinations of drugs that would favor/increase antitumor immune response. To this end, we have built a mathematical model of the major cell types that intervene in ICD, namely cancer cells, DCs, CD8+ and CD4+ T cells. Our model not only integrates intracellular mechanisms within each individual cell entity, but also incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell types. The model is based on a discrete Boolean formalism and is simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type. With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, our model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response. All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and parameter sensitivity analyses. |
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| Fibroblast Growth Factor 2 lethally sensitizes cancer cells to stress‐targeted therapeutic inhibitors |
| Matheus H Dias, Cecília S Fonseca, Julianna D Zeidler, Layra L Albuquerque, Marcelo S da Silva, Eduardo Cararo‐Lopes, Marcelo S Reis, Vincent Noël, Edmilson O Dos Santos, Ian A Prior, Hugo A Armelin |
| Molecular oncology |
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| In malignant transformation, cellular stress-response pathways are dynamically mobilized to counterbalance oncogenic activity, keeping cancer cells viable. Therapeutic disruption of this vulnerable homeostasis might change the outcome of many human cancers, particularly those for which no effective therapy is available. Here, we report the use of fibroblast growth factor 2 (FGF2) to demonstrate that further mitogenic activation disrupts cellular homeostasis and strongly sensitizes cancer cells to stress-targeted therapeutic inhibitors. We show that FGF2 enhanced replication and proteotoxic stresses in a K-Ras-driven murine cancer cell model, and combinations of FGF2 and proteasome or DNA damage response-checkpoint inhibitors triggered cell death. CRISPR/Cas9-mediated K-Ras depletion suppressed the malignant phenotype and prevented these synergic toxicities in these murine cells. Moreover, in a panel of human Ewing's sarcoma family tumor cells, sublethal concentrations of bortezomib (proteasome inhibitor) or VE-821 (ATR inhibitor) induced cell death when combined with FGF2. Sustained MAPK-ERK1/2 overactivation induced by FGF2 appears to underlie these synthetic lethalities, as late pharmacological inhibition of this pathway restored cell homeostasis and prevented these described synergies. Our results highlight how mitotic signaling pathways which are frequently overridden in malignant transformation might be exploited to disrupt the robustness of cancer cells, ultimately sensitizing them to stress-targeted therapies. This approach provides a new therapeutic rationale for human cancers, with important implications for tumors still lacking effective treatment, and for those that frequently relapse after treatment with available therapies. |
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| An interdisciplinary approach for designing of kinetic models of the Ras/MAPK signaling pathway |
| Marcelo S. Reis, Vincent Noël, Matheus H.S. Dias, Layra L. Albuquerque, Amanda S. Guimarães, Lulu Wu, Junior Barrera and Hugo A. Armelin. |
| Kinase Signaling Networks, Methods in Molecular Biology, vol. 1636, chap. 28 |
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| We present in this article a methodology for designing kinetic models of molecular signaling networks, which was exemplarily applied for modeling one of the Ras/MAPK signaling pathways in the mouse Y1 adrenocortical cell line. The methodology is interdisciplinary, that is, it was developed in a way that both dry and wet lab teams worked together along the whole modeling process. |
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| Glyceraldehyde 3-Phosphate Dehydrogenase-Telomere Association Correlates with Redox Status in Trypanosoma cruzi |
| Ricardo Pariona-Llanos, Raphael Souza Pavani, Marcelo Reis, Vincent Noel, Ariel Mariano Silber, Hugo Aguirre Armelin, Maria Isabel Nogueira Cano, Maria Carolina Elias |
| Plos one |
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| Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is a classical metabolic enzyme involved in energy production and plays a role in additional nuclear functions, including transcriptional control, recognition of misincorporated nucleotides in DNA and maintenance of telomere structure. Here, we show that the recombinant protein T. cruzi GAPDH (rTcGAPDH) binds single-stranded telomeric DNA. We demonstrate that the binding of GAPDH to telomeric DNA correlates with the balance between oxidized and reduced forms of nicotinamide adenine dinucleotides (NAD+/NADH). We observed that GAPDH-telomere association and NAD+/NADH balance changed throughout the T. cruzi life cycle. For example, in replicative epimastigote forms of T. cruzi, which show similar intracellular concentrations of NAD+ and NADH, GAPDH binds to telomeric DNA in vivo and this binding activity is inhibited by exogenous NAD+. In contrast, in the T. cruzi non-proliferative trypomastigote forms, which show higher NAD+ concentration, GAPDH was absent from telomeres. In addition, NAD+ abolishes physical interaction between recombinant GAPDH and synthetic telomere oligonucleotide in a cell free system, mimicking exogenous NAD+ that reduces GAPDH-telomere interaction in vivo. We propose that the balance in the NAD+/NADH ratio during T. cruzi life cycle homeostatically regulates GAPDH telomere association, suggesting that in trypanosomes redox status locally modulates GAPDH association with telomeric DNA. |
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| A hybrid mammalian cell cycle model |
| V. Noël, S. Vakulenko, O. Radulescu |
| Electronic Proceedings in Theoretical Computer Science 125 : 68-83, HSB 2013, Taormina |
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| Hybrid modeling provides an effective solution to cope with multiple time scales dynamics in systems biology. Among the applications of this method, one of the most important is the cell cycle regulation. The machinery of the cell cycle, leading to cell division and proliferation, combines slow growth, spatio-temporal re-organisation of the cell, and rapid changes of regulatory proteins concentrations induced by post-translational modifications. The advancement through the cell cycle comprises a well defined sequence of stages, separated by checkpoint transitions. The combination of continuous and discrete changes justifies hybrid modelling approaches to cell cycle dynamics. We present a piecewise-smooth version of a mammalian cell cycle model, obtained by hybridization from a smooth biochemical model. The approximate hybridization scheme, leading to simplified reaction rates and binary event location functions, is based on learning from a training set of trajectories of the smooth model. We discuss several learning strategies for the parameters of the hybrid model. |
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| Modèles réduits et hybrides de réseaux de réactions biochimiques: applications à la modélisation du cycle cellulaire |
| V. Noël |
| Thèse de doctorat. Université Rennes 1. |
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| La modélisation des systèmes biologiques, particulièrement à l'échelle moléculaire, est une problématique nouvelle, issue de l'apport des techniques à haut débit. Le défi en modélisation mathématique est de pouvoir analyser le comportement de ces systèmes dynamiques de très grande dimension. L'enjeu est de taille, car la compréhension du fonctionnement normal et pathologique des cellules au niveau moléculaire, ouvre la voie aux thérapies ciblés pour des maladies systémiques telles que le cancer. Pour s'affranchir des problèmes liés à l'imprécision des valeurs des paramètres, cette thèse propose de travailler avec des ordres, plutôt qu'avec des valeurs précises de paramètres. Ceci conduit naturellement à l'utilisation de l'analyse tropicale pour obtenir des modèles réduits et hybrides. Ces développements ouvrent des nouvelles perspectives sur le plan mathématique, concernant l'étude de systèmes dynamiques. Cette étude propose quelques résultats concernant la tropicalisation des systèmes d'équations différentielles. Une autre partie de la thèse est consacrée à l'étude numérique des systèmes hybrides. La question ici est comment construire un modèle hybride qui reproduit un comportement expérimental donné, aussi comment identifier un modèle hybride à partir de séries temporelles. Cette thèse propose un algorithme original d'identification. Cet algorithme sépare le problème en deux sous-problèmes, notamment l'identification des paramètres des modes et l'identification des paramètres de commande des modes. Des applications à relativement grande échelle sont abordées par cette approche, notamment un modèle de cycle cellulaire chez les mammifères. |
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| Tropicalization and Tropical equilibration for chemical kinetics |
| V. Noël, D. Grigoriev, S. Vakulenko, O. Radulescu |
| In Press, AMS Contemporary Mathematics, Tropical 2012, Moscow |
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| Systems biology uses large networks of biochemical reactions to model the functioning of biological cells from the molecular to the cellular scale. The dynamics of dissipative reaction networks with many well separated time scales can be described as a sequence of successive equilibrations of different subsets of variables of the system. Polynomial systems with separation are equilibrated when at least two monomials, of opposite signs, have the same order of magnitude and dominate the others. These equilibrations and the corresponding truncated dynamics, obtained by eliminating the dominated terms, find a natural formulation in tropical analysis and can be used for model reduction. |
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| Reduction of dynamical biochemical reaction networks in computational biology |
| O. Radulescu, A. N. Gorban, A. Zinovyev, V. Noël |
| Frontiers in Genetics 3, 131 |
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| Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. s. Networks with many well separated time scales, can be reduced to simpler models, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state and quasi-equilibrium approximations, and provide practical recipes for model reduction of linear and nonlinear networks. We also discuss the application of model reduction to the problem of parameter identification, via backward pruning machine learning techniques. |
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| Hybrid models of the cell cycle molecular machinery |
| V. Noël, D. Grigoriev, S. Vakulenko, O. Radulescu |
| Electronic Proceedings in Theoretical Computer Science 92 : 88-105, HSB 2012, Newcastle |
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| Piecewise smooth hybrid systems, involving continuous and discrete variables, are suitable models for describing the multiscale regulatory machinery of the biological cells. In hybrid models, the discrete variables can switch on and off some molecular interactions, simulating cell progression through a series of functioning modes. The advancement through the cell cycle is the archetype of such an organized sequence of events. We present an approach, inspired from tropical geometry ideas, allowing to reduce, hybridize and analyse cell cycle models consisting of polynomial or rational ordinary differential equations. |
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| Tropical geometries and dynamics of biochemical networks. Application to hybrid cell cycle models |
| V. Noël, D. Grigoriev, S. Vakulenko, O. Radulescu |
| Electronic Notes in Theoretical Computer Science 284, pp. 75-91, SASB 2011, Venezia |
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| We use the Litvinov-Maslov correspondence principle to reduce and hybridize networks of biochemical reactions. We apply this method to a cell cycle oscillator model. The reduced and hybridized model can be used as a hybrid model for the cell cycle. We also propose a practical recipe for detecting quasi-equilibrium QE reactions and quasi-steady state QSS species in biochemical models with rational rate functions and use this recipe for model reduction. Interestingly, the QE/QSS invariant manifold of the smooth model and the reduced dynamics along this manifold can be put into correspondence to the tropical variety of the hybridization and to sliding modes along this variety, respectively. |
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| Algorithm for Identification of Piecewise Smooth Hybrid Systems : Application to Eukaryotic Cell Cycle Regulation |
| V. Noël, S. Vakulenko, O. Radulescu |
| Lecture Notes in Computer Science 6833, pp. 225-236, WABI 2011, Saakbrücken |
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| We discuss piecewise smooth hybrid systems as models for regulatory networks in molecular biology. These systems involve both continuous and discrete variables. The discrete variables allow to switch on and off some of the molecular interactions in the model of the biological system. Piecewise smooth hybrid models are well adapted to approximate the dynamics of multiscale dissipative systems that occur in molecular biology. We show how to produce such models by a top down approach that use biological knowledge for a guided choice of important variables and interactions. Then we propose an algorithm for fitting parameters of the piecewise smooth models from data. We illustrate some of the possibilities of this approach by proposing hybrid versions of eukaryotic cell cycle regulation. |
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| Piecewise smooth hybrid systems as models for networks in molecular biology |
| V. Noël, S. Vakulenko, O. Radulescu |
| Proceedings of JOBIM 2010, p57-62, Montpellier |
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| We discuss piecewise smooth hybrid systems as models for regulatory networks in molecular biology. These systems involve both continuous and discrete variables. In the context of gene networks, the discrete variables allow to switch on and off some of the molecular interactions in the model of the biological system. Piecewise smooth hybrid models are well adapted to approximate the dynamics of multiscale dissipative systems that occur in molecular biology. We show how to produce such models by a top down approach that use biological knowledge for a guided choice of important variables and interactions. Then we propose an algorithm for fitting parameters of the piecewise smooth models from data. We illustrate some of the possibilities of this approach by proposing a minimal piecewise smooth model for the cell cycle |
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