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Henry V, Moszer I, Dameron O, Vila Xicota L, Dubois B, Potier MC, Hofmann-Apitius M, Colliot O. Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6137817. [PMID: 33590873 DOI: 10.1093/database/baab004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 01/17/2021] [Accepted: 01/27/2021] [Indexed: 11/12/2022]
Abstract
Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders' pathophysiology at the molecular level adapted to omics measurements. However, the expressiveness of disease maps could be increased to help in avoiding ambiguities and misinterpretations and to reinforce their interoperability with other knowledge resources. Ontology is an adequate framework to overcome this limitation, through their axiomatic definitions and logical reasoning properties. We introduce the Disease Map Ontology (DMO), an ontological upper model based on systems biology terms. We then propose to apply DMO to Alzheimer's disease (AD). Specifically, we use it to drive the conversion of AlzPathway, a disease map devoted to AD, into a formal ontology: Alzheimer DMO. We demonstrate that it allows one to deal with issues related to redundancy, naming, consistency, process classification and pathway relationships. Furthermore, we show that it can store and manage multi-omics data. Finally, we expand the model using elements from other resources, such as clinical features contained in the AD Ontology, resulting in an enriched model called ADMO-plus. The current versions of DMO, ADMO and ADMO-plus are freely available at http://bioportal.bioontology.org/ontologies/ADMO.
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Affiliation(s)
- Vincent Henry
- Inria Paris, Aramis Project-Team, Paris 75013, France.,Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,ICONICS Core Facility, Paris Brain Institute, Paris 75013, France
| | - Ivan Moszer
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,ICONICS Core Facility, Paris Brain Institute, Paris 75013, France
| | - Olivier Dameron
- Univ Rennes, CNRS, Inria, IRISA-UMR 6074, Rennes 35000, France
| | - Laura Vila Xicota
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,Alzheimer's and Prion Diseases Team, Paris Brain Institute, Paris 75013, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Department of Neurology, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris 75013, France
| | - Marie-Claude Potier
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,Alzheimer's and Prion Diseases Team, Paris Brain Institute, Paris 75013, France
| | | | - Olivier Colliot
- Inria Paris, Aramis Project-Team, Paris 75013, France.,Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France
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Niarakis A, Kuiper M, Ostaszewski M, Malik Sheriff RS, Casals-Casas C, Thieffry D, Freeman TC, Thomas P, Touré V, Noël V, Stoll G, Saez-Rodriguez J, Naldi A, Oshurko E, Xenarios I, Soliman S, Chaouiya C, Helikar T, Calzone L. 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. Brief Bioinform 2020; 22:1848-1859. [PMID: 32313939 DOI: 10.1093/bib/bbaa046] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/20/2020] [Accepted: 03/08/2020] [Indexed: 12/14/2022] Open
Abstract
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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Leedale J, Sharkey KJ, Colley HE, Norton ÁM, Peeney D, Mason CL, Sathish JG, Murdoch C, Sharma P, Webb SD. A Combined In Vitro/In Silico Approach to Identifying Off-Target Receptor Toxicity. iScience 2018; 4:84-96. [PMID: 30240756 PMCID: PMC6147237 DOI: 10.1016/j.isci.2018.05.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 04/19/2018] [Accepted: 05/15/2018] [Indexed: 12/20/2022] Open
Abstract
Many xenobiotics can bind to off-target receptors and cause toxicity via the dysregulation of downstream transcription factors. Identification of subsequent off-target toxicity in these chemicals has often required extensive chemical testing in animal models. An alternative, integrated in vitro/in silico approach for predicting toxic off-target functional responses is presented to refine in vitro receptor identification and reduce the burden on in vivo testing. As part of the methodology, mathematical modeling is used to mechanistically describe processes that regulate transcriptional activity following receptor-ligand binding informed by transcription factor signaling assays. Critical reactions in the signaling cascade are identified to highlight potential perturbation points in the biochemical network that can guide and optimize additional in vitro testing. A physiologically based pharmacokinetic model provides information on the timing and localization of different levels of receptor activation informing whole-body toxic potential resulting from off-target binding.
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Affiliation(s)
- Joseph Leedale
- EPSRC Liverpool Centre for Mathematics in Healthcare, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.
| | - Kieran J Sharkey
- EPSRC Liverpool Centre for Mathematics in Healthcare, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
| | - Helen E Colley
- School of Clinical Dentistry, University of Sheffield, Sheffield S10 2TA, UK
| | - Áine M Norton
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK
| | - David Peeney
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK
| | - Chantelle L Mason
- Department of Applied Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
| | - Jean G Sathish
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK; Immuno and Molecular Toxicology, Drug Safety Evaluation, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, NJ 08903, USA
| | - Craig Murdoch
- School of Clinical Dentistry, University of Sheffield, Sheffield S10 2TA, UK
| | - Parveen Sharma
- MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool L69 3GE, UK.
| | - Steven D Webb
- Department of Applied Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
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O'Hara L, O'Shaughnessy PJ, Freeman TC, Smith LB. Modelling steroidogenesis: a framework model to support hypothesis generation and testing across endocrine studies. BMC Res Notes 2018; 11:252. [PMID: 29690918 PMCID: PMC5937803 DOI: 10.1186/s13104-018-3365-y] [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: 03/06/2018] [Accepted: 04/20/2018] [Indexed: 11/10/2022] Open
Abstract
Objective Steroid hormones are responsible for the control of a wide range of physiological processes such as development, growth, reproduction, metabolism, and aging. Because of the variety of enzymes, substrates and products that take part in steroidogenesis and the compartmentalisation of its constituent reactions, it is a complex process to visualise and document. One of the goals of systems biology is to quantitatively describe the behaviour of complex biological systems that involve the interaction of many components. This can be done by representing these interactions visually in a pathway model and then optionally constructing a mathematical model of the interactions. Results We have used the modified Edinburgh Pathway Notation to construct a framework diagram describing human steroidogenic pathways, which will be of use to endocrinologists. To demonstrate further utility, we show how such models can be parameterised with empirical data within the software Graphia Professional, to recapitulate specific examples of steroid hormone production, and also to mimic gene knockout. These framework models support in silico hypothesis generation and testing with utility across endocrine endpoints, with significant potential to reduce costs, time and animal numbers, whilst informing the design of planned studies. Electronic supplementary material The online version of this article (10.1186/s13104-018-3365-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Laura O'Hara
- MRC Centre for Reproductive Health, The Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK. .,The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG, UK.
| | - Peter J O'Shaughnessy
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G61 1QH, UK
| | - Tom C Freeman
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG, UK
| | - Lee B Smith
- MRC Centre for Reproductive Health, The Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh, EH16 4TJ, UK.,School of Environmental and Life Sciences, University of Newcastle, Callaghan, NSW, 2308, Australia
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Abstract
A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net-based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.
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Abstract
There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system’s dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems. This Community Page presents a biologist-friendly method for compiling detailed graphical models of biological pathways from the literature, including their subsequent parameterization for use in dynamic simulations of their activity.
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Stroma-Derived Connective Tissue Growth Factor Maintains Cell Cycle Progression and Repopulation Activity of Hematopoietic Stem Cells In Vitro. Stem Cell Reports 2015; 5:702-715. [PMID: 26527384 PMCID: PMC4649380 DOI: 10.1016/j.stemcr.2015.09.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Revised: 09/21/2015] [Accepted: 09/22/2015] [Indexed: 02/08/2023] Open
Abstract
Hematopoietic stem cells (HSCs) are preserved in co-cultures with UG26-1B6 stromal cells or their conditioned medium. We performed a genome-wide study of gene expression changes of UG26-1B6 stromal cells in contact with Lineage⁻ SCA-1⁺ KIT⁺ (LSK) cells. This analysis identified connective tissue growth factor (CTGF) to be upregulated in response to LSK cells. We found that co-culture of HSCs on CTGF knockdown stroma (shCtgf) shows impaired engraftment and long-term quality. Further experiments demonstrated that CD34⁻ CD48⁻ CD150⁺ LSK (CD34⁻ SLAM) cell numbers from shCtgf co-cultures increase in G0 and senescence and show delayed time to first cell division. To understand this observation, a CTGF signaling network model was assembled, which was experimentally validated. In co-culture experiments of CD34⁻ SLAM cells with shCtgf stromal cells, we found that SMAD2/3-dependent signaling was activated, with increasing p27(Kip1) expression and downregulating cyclin D1. Our data support the view that LSK cells modulate gene expression in the niche to maintain repopulating HSC activity.
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Abstract
BioLayout Express (3D) is a network analysis tool designed for the visualisation and analysis of graphs derived from biological data. It has proved to be powerful in the analysis of gene expression data, biological pathways and in a range of other applications. In version 3.2 of the tool we have introduced the ability to import, merge and display pathways and protein interaction networks available in the BioPAX Level 3 standard exchange format. A graphical interface allows users to search for pathways or interaction data stored in the Pathway Commons database. Queries using either gene/protein or pathway names are made via the cPath2 client and users can also define the source and/or species of information that they wish to examine. Data matching a query are listed and individual records may be viewed in isolation or merged using an 'Advanced' query tab. A visualisation scheme has been defined by mapping BioPAX entity types to a range of glyphs. Graphs of these data can be viewed and explored within BioLayout as 2D or 3D graph layouts, where they can be edited and/or exported for visualisation and editing within other tools.
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Affiliation(s)
- Derek W. Wright
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, Scotland, EH25 9RG, UK
| | - Tim Angus
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, Scotland, EH25 9RG, UK
| | - Anton J. Enright
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Tom C. Freeman
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, Scotland, EH25 9RG, UK
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Waltemath D, Bergmann FT, Chaouiya C, Czauderna T, Gleeson P, Goble C, Golebiewski M, Hucka M, Juty N, Krebs O, Le Novère N, Mi H, Moraru II, Myers CJ, Nickerson D, Olivier BG, Rodriguez N, Schreiber F, Smith L, Zhang F, Bonnet E. Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE). Stand Genomic Sci 2014. [PMCID: PMC4149000 DOI: 10.4056/sigs.5279417] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology.
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Affiliation(s)
- Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Correspondence: Dagmar Waltemath (), Eric Bonnet ()
| | - Frank T. Bergmann
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA
| | - Claudine Chaouiya
- Instituto Gulbenkian de Ciência - IGC, Rua da Quinta Grande, Oeiras, Portugal
| | | | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, United Kingdom
| | - Carole Goble
- School of Computer Science, The University of Manchester, Manchester, UK
| | | | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA
| | - Nick Juty
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Olga Krebs
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Nicolas Le Novère
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- The Babraham Institute, Babraham Research Campus, Cambridge, United Kingdom
| | - Huaiyu Mi
- Department of preventive medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ion I. Moraru
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, USA
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, USA
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Brett G. Olivier
- Systems Bioinformatics, VU University Amsterdam, Amsterdam, The Netherlands
| | - Nicolas Rodriguez
- The Babraham Institute, Babraham Research Campus, Cambridge, United Kingdom
| | - Falk Schreiber
- IPK Gatersleben, Gatersleben, Germany
- Martin Luther University Halle-Wittenberg, Halle, Germany
- Monash University, Melbourne, Australia
| | - Lucian Smith
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA
| | - Fengkai Zhang
- Computational Biology Unit, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, Maryland, USA
| | - Eric Bonnet
- Institut Curie, Paris, France
- INSERM U900, 75248 Paris, France
- Mines ParisTech, 77300 Fontainebleau, France
- Correspondence: Dagmar Waltemath (), Eric Bonnet ()
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Sharp GC, Ma H, Saunders PTK, Norman JE. A computational model of lipopolysaccharide-induced nuclear factor kappa B activation: a key signalling pathway in infection-induced preterm labour. PLoS One 2013; 8:e70180. [PMID: 23936158 PMCID: PMC3736540 DOI: 10.1371/journal.pone.0070180] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Accepted: 06/16/2013] [Indexed: 11/19/2022] Open
Abstract
Preterm birth is the single biggest cause of significant neonatal morbidity and mortality, and the incidence is rising. Development of new therapies to treat and prevent preterm labour is seriously hampered by incomplete understanding of the molecular mechanisms that initiate labour at term and preterm. Computational modelling provides a new opportunity to improve this understanding. It is a useful tool in (i) identifying gaps in knowledge and informing future research, and (ii) providing the basis for an in silico model of parturition in which novel drugs to prevent or treat preterm labour can be "tested". Despite their merits, computational models are rarely used to study the molecular events initiating labour. Here, we present the first attempt to generate a dynamic kinetic model that has relevance to the molecular mechanisms of preterm labour. Using published data, we model an important candidate signalling pathway in infection-induced preterm labour: that of lipopolysaccharide (LPS) -induced activation of Nuclear Factor kappa B. This is the first model of this pathway to explicitly include molecular interactions upstream of Nuclear Factor kappa B activation. We produced a formalised graphical depiction of the pathway and built a kinetic model based on ordinary differential equations. The kinetic model accurately reproduced published in vitro time course plots of Lipopolysaccharide-induced Nuclear Factor kappa B activation in mouse embryo fibroblasts. In this preliminary work we have provided proof of concept that it is possible to build computational models of signalling pathways that are relevant to the regulation of labour, and suggest that models that are validated with wet-lab experiments have the potential to greatly benefit the field.
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Affiliation(s)
- Gemma C Sharp
- Medical Research Council (MRC) Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom.
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Why and How to Expand the Role of Systems Biology in Pharmaceutical Research and Development. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2012; 736:533-42. [DOI: 10.1007/978-1-4419-7210-1_31] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Macrophages.com: an on-line community resource for innate immunity research. Immunobiology 2011; 216:1203-11. [PMID: 21924793 DOI: 10.1016/j.imbio.2011.07.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2011] [Accepted: 07/18/2011] [Indexed: 01/03/2023]
Abstract
Macrophages play a major role in tissue remodelling during development, wound healing and tissue homeostasis, and are central to innate immunity and to the pathology of tissue injury and inflammation. Given this fundamental role in many aspects of biological function, an enormous wealth of information has accumulated on these fascinating cells in the literature and other public repositories. With the escalation of genome-scale data derived from macrophages and related haematopoietic cell types, there is a growing need for an integrated resource that seeks to compile, organise and analyse our collective knowledge of macrophage biology. Here we describe a community-driven web-based resource, macrophages.com that aims to provide a portal onto various types of Omics data to facilitate comparative genomic studies, promoter and transcriptional network analyses, models of macrophage pathways together with other information on these cells. To this end, the website combines public and in-house analyses of expression data with pre-analysed views of co-expressed genes as supported by the network analysis tool BioLayout Express(3D), as well as providing access to maps of pathways active in macrophages. Macrophages.com also provides access to an extensive image library of macrophages in adult/embryonic tissue sections prepared from normal and transgenic mice. In addition, the site links to the Human Protein Atlas database so as to provide direct access to protein expression patterns in human macrophages. Finally, an integrated gene-centric portal provides the tools for rapid promoter analysis studies based on a comprehensive set of CAGE-derived transcription start site (TSS) sequences in human and mouse genomes as generated by the Functional Annotation of Mammalian genomes (FANTOM) projects initiated by the RIKEN Omics Science Center. Our aim is to continue to grow the macrophages.com resource using publicly available data, as well as in-house generated knowledge. In so doing we aim to provide a user-friendly community website and a community portal for access to comprehensive sets of macrophage-related data.
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Luna A, Sunshine ML, van Iersel MP, Aladjem MI, Kohn KW. PathVisio-MIM: PathVisio plugin for creating and editing Molecular Interaction Maps (MIMs). ACTA ACUST UNITED AC 2011; 27:2165-6. [PMID: 21636591 DOI: 10.1093/bioinformatics/btr336] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION A plugin for the Java-based PathVisio pathway editor has been developed to help users draw diagrams of bioregulatory networks according to the Molecular Interaction Map (MIM) notation. Together with the core PathVisio application, this plugin presents a simple to use and cross-platform application for the construction of complex MIM diagrams with the ability to annotate diagram elements with comments, literature references and links to external databases. This tool extends the capabilities of the PathVisio pathway editor by providing both MIM-specific glyphs and support for a MIM-specific markup language file format for exchange with other MIM-compatible tools and diagram validation. AVAILABILITY The PathVisio-MIM plugin is freely available and works with versions of PathVisio 2.0.11 and later on Windows, Mac OS X and Linux. Information about MIM notation and the MIMML format is available at http://discover.nci.nih.gov/mim. The plugin, along with diagram examples, instructions and Java source code, may be downloaded at http://discover.nci.nih.gov/mim/mim_pathvisio.html.
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Affiliation(s)
- Augustin Luna
- Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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Raza S, McDerment N, Lacaze PA, Robertson K, Watterson S, Chen Y, Chisholm M, Eleftheriadis G, Monk S, O'Sullivan M, Turnbull A, Roy D, Theocharidis A, Ghazal P, Freeman TC. Construction of a large scale integrated map of macrophage pathogen recognition and effector systems. BMC SYSTEMS BIOLOGY 2010; 4:63. [PMID: 20470404 PMCID: PMC2892459 DOI: 10.1186/1752-0509-4-63] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2010] [Accepted: 05/14/2010] [Indexed: 11/24/2022]
Abstract
Background In an effort to better understand the molecular networks that underpin macrophage activation we have been assembling a map of relevant pathways. Manual curation of the published literature was carried out in order to define the components of these pathways and the interactions between them. This information has been assembled into a large integrated directional network and represented graphically using the modified Edinburgh Pathway Notation (mEPN) scheme. Results The diagram includes detailed views of the toll-like receptor (TLR) pathways, other pathogen recognition systems, NF-kappa-B, apoptosis, interferon signalling, MAP-kinase cascades, MHC antigen presentation and proteasome assembly, as well as selected views of the transcriptional networks they regulate. The integrated pathway includes a total of 496 unique proteins, the complexes formed between them and the processes in which they are involved. This produces a network of 2,170 nodes connected by 2,553 edges. Conclusions The pathway diagram is a navigable visual aid for displaying a consensus view of the pathway information available for these systems. It is also a valuable resource for computational modelling and aid in the interpretation of functional genomics data. We envisage that this work will be of value to those interested in macrophage biology and also contribute to the ongoing Systems Biology community effort to develop a standard notation scheme for the graphical representation of biological pathways.
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Affiliation(s)
- Sobia Raza
- Division of Pathway Medicine, University of Edinburgh, The Chancellor's Building, College of Medicine, 49 Little France Crescent, Edinburgh EH16 4SB, UK
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