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Stoll G, Naldi A, Noël V, Viara E, Barillot E, Kroemer G, Thieffry D, Calzone L. UPMaBoSS: A Novel Framework for Dynamic Cell Population Modeling. Front Mol Biosci 2022; 9:800152. [PMID: 35309516 PMCID: PMC8924294 DOI: 10.3389/fmolb.2022.800152] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/21/2022] [Indexed: 11/13/2022] Open
Abstract
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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Affiliation(s)
- Gautier Stoll
- Equipe Labellisée Par La Ligue Contre Le Cancer, Université de Paris, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Paris, France
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France
| | - Aurélien Naldi
- Institut de Biologie de L’ENS (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM U900, Paris, France
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France
| | | | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, France
- INSERM U900, Paris, France
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France
| | - Guido Kroemer
- Equipe Labellisée Par La Ligue Contre Le Cancer, Université de Paris, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Paris, France
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France
- Pôle de Biologie, Hôpital européen Georges Pompidou, AP-HP, Paris, France
| | - Denis Thieffry
- Institut de Biologie de L’ENS (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM U900, Paris, France
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France
- *Correspondence: Laurence Calzone,
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Selvaggio G, Cristellon S, Marchetti L. A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps. Front Mol Biosci 2022; 8:760077. [PMID: 34988115 PMCID: PMC8721169 DOI: 10.3389/fmolb.2021.760077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity to describe the underlying kinetic processes. We propose a hybrid modeling approach that integrates ordinary differential equations and logical formalism to describe distinct biological layers and their communication. We focused on a multicellular system as a case study by applying the hybrid formalism to the well-known Delta-Notch signaling pathway. We used a differential equation model to describe the intracellular pathways while the cell-cell interactions were defined by logic rules. The hybrid approach herein employed allows us to combine the pros of different modeling techniques by overcoming the lack of quantitative information with a qualitative description that discretizes activation and inhibition processes, thus avoiding complexity.
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Affiliation(s)
- Gianluca Selvaggio
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Serena Cristellon
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Mathematics, University of Trento, Trento, Italy
| | - Luca Marchetti
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
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Selvaggio G, Chaouiya C, Janody F. In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer. Int J Mol Sci 2021; 22:ijms22094897. [PMID: 34063110 PMCID: PMC8125147 DOI: 10.3390/ijms22094897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 12/13/2022] Open
Abstract
The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.
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Affiliation(s)
- Gianluca Selvaggio
- Fondazione the Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto, Italy;
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
| | - Claudine Chaouiya
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- CNRS, Centrale Marseille, I2M, Aix Marseille University, 13397 Marseille, France
- Correspondence: (C.C.); (F.J.)
| | - Florence Janody
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
- IPATIMUP—Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
- Correspondence: (C.C.); (F.J.)
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Floc'hlay S, Molina MD, Hernandez C, Haillot E, Thomas-Chollier M, Lepage T, Thieffry D. Deciphering and modelling the TGF-β signalling interplays specifying the dorsal-ventral axis of the sea urchin embryo. Development 2021; 148:dev.189944. [PMID: 33298464 DOI: 10.1242/dev.189944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 11/16/2020] [Indexed: 11/20/2022]
Abstract
During sea urchin development, secretion of Nodal and BMP2/4 ligands and their antagonists Lefty and Chordin from a ventral organiser region specifies the ventral and dorsal territories. This process relies on a complex interplay between the Nodal and BMP pathways through numerous regulatory circuits. To decipher the interplay between these pathways, we used a combination of treatments with recombinant Nodal and BMP2/4 proteins and a computational modelling approach. We assembled a logical model focusing on cell responses to signalling inputs along the dorsal-ventral axis, which was extended to cover ligand diffusion and enable multicellular simulations. Our model simulations accurately recapitulate gene expression in wild-type embryos, accounting for the specification of ventral ectoderm, ciliary band and dorsal ectoderm. Our model simulations further recapitulate various morphant phenotypes, reveal a dominance of the BMP pathway over the Nodal pathway and stress the crucial impact of the rate of Smad activation in dorsal-ventral patterning. These results emphasise the key role of the mutual antagonism between the Nodal and BMP2/4 pathways in driving early dorsal-ventral patterning of the sea urchin embryo.
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Affiliation(s)
- Swann Floc'hlay
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | | | - Céline Hernandez
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | - Emmanuel Haillot
- Institut Biologie Valrose, Université Côte d'Azur, 06108 Nice, France
| | - Morgane Thomas-Chollier
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France.,Institut Universitaire de France (IUF), 75005 Paris, France
| | - Thierry Lepage
- Institut Biologie Valrose, Université Côte d'Azur, 06108 Nice, France
| | - Denis Thieffry
- Department of Biology, Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
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Howell RSM, Klemm C, Thorpe PH, Csikász-Nagy A. Unifying the mechanism of mitotic exit control in a spatiotemporal logical model. PLoS Biol 2020; 18:e3000917. [PMID: 33180788 PMCID: PMC7685450 DOI: 10.1371/journal.pbio.3000917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 11/24/2020] [Accepted: 10/09/2020] [Indexed: 11/18/2022] Open
Abstract
The transition from mitosis into the first gap phase of the cell cycle in budding yeast is controlled by the Mitotic Exit Network (MEN). The network interprets spatiotemporal cues about the progression of mitosis and ensures that release of Cdc14 phosphatase occurs only after completion of key mitotic events. The MEN has been studied intensively; however, a unified understanding of how localisation and protein activity function together as a system is lacking. In this paper, we present a compartmental, logical model of the MEN that is capable of representing spatial aspects of regulation in parallel to control of enzymatic activity. We show that our model is capable of correctly predicting the phenotype of the majority of mutants we tested, including mutants that cause proteins to mislocalise. We use a continuous time implementation of the model to demonstrate that Cdc14 Early Anaphase Release (FEAR) ensures robust timing of anaphase, and we verify our findings in living cells. Furthermore, we show that our model can represent measured cell-cell variation in Spindle Position Checkpoint (SPoC) mutants. This work suggests a general approach to incorporate spatial effects into logical models. We anticipate that the model itself will be an important resource to experimental researchers, providing a rigorous platform to test hypotheses about regulation of mitotic exit.
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Affiliation(s)
- Rowan S M Howell
- The Francis Crick Institute, London, United Kingdom.,Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom
| | - Cinzia Klemm
- School of Biological and Chemical Sciences, Queen Mary University, London, United Kingdom
| | - Peter H Thorpe
- School of Biological and Chemical Sciences, Queen Mary University, London, United Kingdom
| | - Attila Csikász-Nagy
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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Boolean analysis of lateral inhibition. J Math Biol 2020; 81:463-486. [PMID: 32728826 PMCID: PMC7427764 DOI: 10.1007/s00285-020-01515-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 12/09/2019] [Indexed: 11/03/2022]
Abstract
We study Boolean networks which are simple spatial models of the highly conserved Delta-Notch system. The models assume the inhibition of Delta in each cell by Notch in the same cell, and the activation of Notch in presence of Delta in surrounding cells. We consider fully asynchronous dynamics over undirected graphs representing the neighbour relation between cells. In this framework, one can show that all attractors are fixed points for the system, independently of the neighbour relation, for instance by using known properties of simplified versions of the models, where only one species per cell is defined. The fixed points correspond to the so-called fine-grained "patterns" that emerge in discrete and continuous modelling of lateral inhibition. We study the reachability of fixed points, giving a characterisation of the trap spaces and the basins of attraction for both the full and the simplified models. In addition, we use a characterisation of the trap spaces to investigate the robustness of patterns to perturbations. The results of this qualitative analysis can complement and guide simulation-based approaches, and serve as a basis for the investigation of more complex mechanisms.
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Letort G, Montagud A, Stoll G, Heiland R, Barillot E, Macklin P, Zinovyev A, Calzone L. PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling. Bioinformatics 2020; 35:1188-1196. [PMID: 30169736 PMCID: PMC6449758 DOI: 10.1093/bioinformatics/bty766] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 07/28/2018] [Accepted: 08/30/2018] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Due to the complexity and heterogeneity of multicellular biological systems, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful. We present PhysiBoSS, an open source software which combines intracellular signalling using Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell). RESULTS PhysiBoSS provides a flexible and computationally efficient framework to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from single-cell genotype to single-cell phenotype and emergent multicellular behaviour. PhysiBoSS thus becomes very useful when studying heterogeneous population response to treatment, mutation effects, different modes of invasion or isomorphic morphogenesis events. To concretely illustrate a potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment. We explored the effect of different treatments and the behaviour of several resistant mutants. We highlighted the importance of spatial information on the population dynamics by considering the effect of competition for resources like oxygen. AVAILABILITY AND IMPLEMENTATION PhysiBoSS is freely available on GitHub (https://github.com/sysbio-curie/PhysiBoSS), with a Docker image (https://hub.docker.com/r/gletort/physiboss/). It is distributed as open source under the BSD 3-clause license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gaelle Letort
- Institut Curie, PSL Research University, Paris, France.,INSERM, U900, Paris, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, France
| | - Arnau Montagud
- Institut Curie, PSL Research University, Paris, France.,INSERM, U900, Paris, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, France
| | - Gautier Stoll
- Université Paris Descartes/Paris V, Sorbonne Paris Cité, Paris, France.,Gustave Roussy Cancer Campus, Villejuif, France.,INSERM, U1138, Paris, France.,Equipe 11 Labellisée par la Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, Paris, France
| | - Randy Heiland
- Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, France.,INSERM, U900, Paris, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, France
| | - Paul Macklin
- Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, France.,INSERM, U900, Paris, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France.,INSERM, U900, Paris, France.,CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, Paris, France
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