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Argyris GA, Lluch Lafuente A, Tribastone M, Tschaikowski M, Vandin A. Reducing Boolean networks with backward equivalence. BMC Bioinformatics 2023; 24:212. [PMID: 37221494 DOI: 10.1186/s12859-023-05326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis. RESULTS We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance. CONCLUSIONS BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.
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Affiliation(s)
- Georgios A Argyris
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Alberto Lluch Lafuente
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | | | - Max Tschaikowski
- Department of Computer Science, University of Aalborg, Aalborg, Denmark
| | - Andrea Vandin
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
- Department of Excellence EMbeDS and Institute of Economics, Sant'Anna School for Advanced Studies, Pisa, Italy.
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2
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Aghamiri SS, Puniya BL, Amin R, Helikar T. A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery. Front Immunol 2023; 14:1112985. [PMID: 36993954 PMCID: PMC10040975 DOI: 10.3389/fimmu.2023.1112985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.
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Affiliation(s)
| | | | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
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3
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Zeigler AC, Chandrabhatla AS, Christiansen SL, Nelson AR, Holmes JW, Saucerman JJ. Network model-based screen for FDA-approved drugs affecting cardiac fibrosis. CPT Pharmacometrics Syst Pharmacol 2021; 10:377-388. [PMID: 33571402 PMCID: PMC8099443 DOI: 10.1002/psp4.12599] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/08/2020] [Accepted: 01/14/2021] [Indexed: 12/30/2022] Open
Abstract
Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.
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Affiliation(s)
- Angela C. Zeigler
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
| | | | | | - Anders R. Nelson
- Department of PharmacologyUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Jeffrey W. Holmes
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Division of Cardiovascular MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Jeffrey J. Saucerman
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Division of Cardiovascular MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA
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4
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Aghamiri SS, Singh V, Naldi A, Helikar T, Soliman S, Niarakis A. Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics 2021; 36:4473-4482. [PMID: 32403123 PMCID: PMC7575051 DOI: 10.1093/bioinformatics/btaa484] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 04/17/2020] [Accepted: 05/06/2020] [Indexed: 12/16/2022] Open
Abstract
Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability and implementation The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sara Sadat Aghamiri
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Vidisha Singh
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
| | - Aurélien Naldi
- Département de Biologie, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), ècole Normale Supérieure, CNRS, INSERM, Université PSL, Paris 75005, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay-île de France, Palaiseau 91120, France
| | - Anna Niarakis
- GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France
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Abudukelimu A, Barberis M, Redegeld F, Sahin N, Sharma RP, Westerhoff HV. Complex Stability and an Irrevertible Transition Reverted by Peptide and Fibroblasts in a Dynamic Model of Innate Immunity. Front Immunol 2020; 10:3091. [PMID: 32117197 PMCID: PMC7033641 DOI: 10.3389/fimmu.2019.03091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 12/17/2019] [Indexed: 12/12/2022] Open
Abstract
We here apply a control analysis and various types of stability analysis to an in silico model of innate immunity that addresses the management of inflammation by a therapeutic peptide. Motivation is the observation, both in silico and in experiments, that this therapy is not robust. Our modeling results demonstrate how (1) the biological phenomena of acute and chronic modes of inflammation may reflect an inherently complex bistability with an irrevertible flip between the two modes, (2) the chronic mode of the model has stable, sometimes unique, steady states, while its acute-mode steady states are stable but not unique, (3) as witnessed by TNF levels, acute inflammation is controlled by multiple processes, whereas its chronic-mode inflammation is only controlled by TNF synthesis and washout, (4) only when the antigen load is close to the acute mode's flipping point, many processes impact very strongly on cells and cytokines, (5) there is no antigen exposure level below which reduction of the antigen load alone initiates a flip back to the acute mode, and (6) adding healthy fibroblasts makes the transition from acute to chronic inflammation revertible, although (7) there is a window of antigen load where such a therapy cannot be effective. This suggests that triple therapies may be essential to overcome chronic inflammation. These may comprise (1) anti-immunoglobulin light chain peptides, (2) a temporarily reduced antigen load, and (3a) fibroblast repopulation or (3b) stem cell strategies.
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Affiliation(s)
- Abulikemu Abudukelimu
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands.,Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands.,Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.,Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom
| | - Frank Redegeld
- Division of Pharmacology, Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | - Nilgun Sahin
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands
| | - Raju P Sharma
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands
| | - Hans V Westerhoff
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands.,Molecular Cell Physiology, VU University Amsterdam, Amsterdam, Netherlands.,School for Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom.,Systems Biology Amsterdam, VU University Amsterdam, Amsterdam, Netherlands
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6
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Singh V, Kalliolias GD, Ostaszewski M, Veyssiere M, Pilalis E, Gawron P, Mazein A, Bonnet E, Petit-Teixeira E, Niarakis A. RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis. Database (Oxford) 2020; 2020:baaa017. [PMID: 32311035 PMCID: PMC7170216 DOI: 10.1093/database/baaa017] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 01/21/2020] [Accepted: 02/13/2020] [Indexed: 02/07/2023]
Abstract
Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.
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Affiliation(s)
- Vidisha Singh
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - George D Kalliolias
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
- Weill Cornell Medical Center, Weill Department of Medicine, 525 East 68th Street, New York, NY 10065, USA
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Maëva Veyssiere
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Eleftherios Pilalis
- eNIOS Applications P.C., R&D department, Alexandrou Pantou 25, 17671, Kallithea-Athens, Greece
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Eric Bonnet
- Centre National de Recherche en Génomique Humaine (CNRGH), CEA, 2 rue Gaston Crémieux, CP5706 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Elisabeth Petit-Teixeira
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Anna Niarakis
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
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7
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Using mechanistic models for the clinical interpretation of complex genomic variation. Sci Rep 2019; 9:18937. [PMID: 31831811 PMCID: PMC6908734 DOI: 10.1038/s41598-019-55454-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/28/2019] [Indexed: 02/07/2023] Open
Abstract
The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.
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Ostaszewski M, Gebel S, Kuperstein I, Mazein A, Zinovyev A, Dogrusoz U, Hasenauer J, Fleming RMT, Le Novère N, Gawron P, Ligon T, Niarakis A, Nickerson D, Weindl D, Balling R, Barillot E, Auffray C, Schneider R. Community-driven roadmap for integrated disease maps. Brief Bioinform 2019; 20:659-670. [PMID: 29688273 PMCID: PMC6556900 DOI: 10.1093/bib/bby024] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 03/02/2018] [Indexed: 01/07/2023] Open
Abstract
The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
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Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Ugur Dogrusoz
- Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Ronan M T Fleming
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, Netherlands
| | - Nicolas Le Novère
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, United Kingdom
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Thomas Ligon
- Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany
| | - Anna Niarakis
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, Evry 91025, France
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
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Ostaszewski M, Kieffer E, Danoy G, Schneider R, Bouvry P. Clustering approaches for visual knowledge exploration in molecular interaction networks. BMC Bioinformatics 2018; 19:308. [PMID: 30157777 PMCID: PMC6116538 DOI: 10.1186/s12859-018-2314-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 08/14/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually. RESULTS We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps. CONCLUSIONS In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.
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Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-Belval, Luxembourg
| | - Emmanuel Kieffer
- Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg
| | - Grégoire Danoy
- Computer Science and Communications Research Unit, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-Belval, Luxembourg
| | - Pascal Bouvry
- Computer Science and Communications Research Unit, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg
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10
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Singh V, Ostaszewski M, Kalliolias GD, Chiocchia G, Olaso R, Petit-Teixeira E, Helikar T, Niarakis A. Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model. GENOMICS AND COMPUTATIONAL BIOLOGY 2017; 4:e100050. [PMID: 29951575 PMCID: PMC6016388 DOI: 10.18547/gcb.2018.vol4.iss1.e100050] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts' activation.
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Affiliation(s)
- Vidisha Singh
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, 91025, Evry, France
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - George D. Kalliolias
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, USA; Department of Medicine, Weill Cornell Medical College, New York City, USA
| | - Gilles Chiocchia
- Faculty of Health Sciences Simone Veil, INSERM U1173, University of Versailles Saint-Quentin-en-Yvelines, Montigny-le-Bretonneux, France
| | - Robert Olaso
- Centre National de Recherche en Génomique Humaine (CNRGH), CEA, Evry, France
| | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Anna Niarakis
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, 91025, Evry, France
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11
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Cardelli L, Tribastone M, Tschaikowski M, Vandin A. Maximal aggregation of polynomial dynamical systems. Proc Natl Acad Sci U S A 2017; 114:10029-10034. [PMID: 28878023 PMCID: PMC5617256 DOI: 10.1073/pnas.1702697114] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ordinary differential equations (ODEs) with polynomial derivatives are a fundamental tool for understanding the dynamics of systems across many branches of science, but our ability to gain mechanistic insight and effectively conduct numerical evaluations is critically hindered when dealing with large models. Here we propose an aggregation technique that rests on two notions of equivalence relating ODE variables whenever they have the same solution (backward criterion) or if a self-consistent system can be written for describing the evolution of sums of variables in the same equivalence class (forward criterion). A key feature of our proposal is to encode a polynomial ODE system into a finitary structure akin to a formal chemical reaction network. This enables the development of a discrete algorithm to efficiently compute the largest equivalence, building on approaches rooted in computer science to minimize basic models of computation through iterative partition refinements. The physical interpretability of the aggregation is shown on polynomial ODE systems for biochemical reaction networks, gene regulatory networks, and evolutionary game theory.
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Affiliation(s)
- Luca Cardelli
- Microsoft Research, Cambridge CB1 2FB, United Kingdom
- Department of Computing, University of Oxford, Oxford OX1 3QD, United Kingdom
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12
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Mula RVR, Machiah D, Holland L, Wang X, Parihar H, Sharma AC, Selvaraj P, Shashidharamurthy R. Immune Complex-Induced, Nitric Oxide-Mediated Vascular Endothelial Cell Death by Phagocytes Is Prevented with Decoy FcγReceptors. PLoS One 2016; 11:e0153620. [PMID: 27101012 PMCID: PMC4839578 DOI: 10.1371/journal.pone.0153620] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 03/31/2016] [Indexed: 01/05/2023] Open
Abstract
Autoimmune vasculitis is an endothelial inflammatory disease that results from the deposition of immune-complexes (ICs) in blood vessels. The interaction between Fcgamma receptors (FcγRs) expressed on inflammatory cells with ICs is known to cause blood vessel damage. Hence, blocking the interaction of ICs and inflammatory cells is essential to prevent the IC-mediated blood vessel damage. Thus we tested if uncoupling the interaction of FcγRs and ICs prevents endothelium damage. Herein, we demonstrate that dimeric FcγR-Igs prevented nitric oxide (NO) mediated apoptosis of human umbilical vein endothelial cells (HUVECs) in an in vitro vasculitis model. Dimeric FcγR-Igs significantly inhibited the IC-induced upregulation of inducible nitric oxide synthase (iNOS) and nitric oxide (NO) release by murine monocytic cell line. However, FcγR-Igs did not affect the exogenously added NO-induced upregulation of pro-apoptotic genes such as Bax (15 fold), Bak (35 fold), cytochrome-C (11 fold) and caspase-3 (30 fold) in HUVECs. In conclusion, these data suggest that IC-induced NO could be one of the major inflammatory mediator promoting blood vessel inflammation and endothelial cell death during IC-mediated vasculitis which can be effectively blocked by dimeric decoy FcγRs.
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Affiliation(s)
- Ramanjaneya V. R. Mula
- Department of Pharmaceutical Sciences, Philadelphia College of Osteopathic Medicine - School of Pharmacy, Suwanee, Georgia, United States of America
| | - Deepa Machiah
- Department of Molecular Pathology Laboratory, Yerkes National Primate Research Centre, Atlanta, Georgia, United States of America
| | - Lauren Holland
- Department of Pharmaceutical Sciences, Philadelphia College of Osteopathic Medicine - School of Pharmacy, Suwanee, Georgia, United States of America
| | - Xinyu Wang
- Department of Pharmaceutical Sciences, Philadelphia College of Osteopathic Medicine - School of Pharmacy, Suwanee, Georgia, United States of America
| | - Harish Parihar
- Department of Pharmacy Practice, Philadelphia College of Osteopathic Medicine - School of Pharmacy, Suwanee, Georgia, United States of America
| | - Avadhesh C. Sharma
- Department of Pharmaceutical Sciences, Philadelphia College of Osteopathic Medicine - School of Pharmacy, Suwanee, Georgia, United States of America
| | - Periasamy Selvaraj
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Rangaiah Shashidharamurthy
- Department of Pharmaceutical Sciences, Philadelphia College of Osteopathic Medicine - School of Pharmacy, Suwanee, Georgia, United States of America
- * E-mail:
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