Author
Andrew Finney
Other affiliations: Ansys, University of Hertfordshire
Bio: Andrew Finney is an academic researcher from California Institute of Technology. The author has contributed to research in topics: SBML & Systems Biology Ontology. The author has an hindex of 18, co-authored 33 publications receiving 6132 citations. Previous affiliations of Andrew Finney include Ansys & University of Hertfordshire.
Papers
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California Institute of Technology1, University of Hertfordshire2, University of California, Berkeley3, Jet Propulsion Laboratory4, University of Cambridge5, Centre national de la recherche scientifique6, University of Auckland7, GlaxoSmithKline8, Max Planck Society9, Stellenbosch University10, University of Connecticut Health Center11, Virginia Bioinformatics Institute12, University of California, Irvine13, Keio University14, Princeton University15
TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
Abstract: Motivation: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. Results: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. ∗ To whom correspondence should be addressed. Availability: The specification of SBML Level 1 is freely available from http://www.sbml.org/.
3,205 citations
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Memorial Sloan Kettering Cancer Center1, Bilkent University2, SRI International3, Université libre de Bruxelles4, Ontario Institute for Cancer Research5, New York University6, National Institutes of Health7, National Autonomous University of Mexico8, Boston University9, Cold Spring Harbor Laboratory10, Johns Hopkins University11, University of Toronto12, University of Rennes13, Rothamsted Research14, Cell Signaling Technology15, Broad Institute16, Food and Drug Administration17, Virginia Tech18, Oregon Health & Science University19, United States Environmental Protection Agency20, Argonne National Laboratory21, University of Connecticut22, Harvard University23, National Institute of Standards and Technology24, University of Cambridge25, Konrad Lorenz Institute for Evolution and Cognition Research26, National University of Ireland, Galway27, Maastricht University28, University of Auckland29, Syngenta30, Stanford University31, Yale University32, Loyola Marymount University33, St. John's University34, Columbia University35, SRA International36, Novartis37, University of Ottawa38, Vertex Pharmaceuticals39, Medical College of Wisconsin40, Gladstone Institutes41, Cornell University42, Takeda Pharmaceutical Company43, University of Chicago44, Total S.A.45, Kyoto University46, California Institute of Technology47
TL;DR: Thousands of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases, and this large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
Abstract: Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
673 citations
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European Bioinformatics Institute1, California Institute of Technology2, National Centre for Biological Sciences3, Cornell University4, National Autonomous University of Mexico5, University of Auckland6, Max Planck Society7, Virginia Bioinformatics Institute8, Keck Graduate Institute of Applied Life Sciences9, Stellenbosch University10, GlaxoSmithKline11, Purdue University12
TL;DR: These rules define procedures for encoding and annotating models represented in machine-readable form to enable users to have confidence that curated models are an accurate reflection of their associated reference descriptions and to facilitate model reuse and composition into large subcellular models.
Abstract: Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.
612 citations
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TL;DR: The SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta-tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities are described.
Abstract: Researchers in quantitative systems biology make use of a large number of different software packages for modelling, analysis, visualization, and general data manipulation. In this paper, we describe the Systems Biology Workbench (SBW), a software framework that allows heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' capabilities via a fast binary encoded-message system. Our goal was to create a simple, high performance, opensource software infrastructure which is easy to implement and understand. SBW enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe in this paper the SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta-tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities.
308 citations
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European Bioinformatics Institute1, University of Jena2, University of Rostock3, University of Tübingen4, Carleton University5, Ansys6, Virginia Bioinformatics Institute7, University of Manchester8, University of Auckland9, Newcastle University10, ETH Zurich11, University of Vienna12, California Institute of Technology13
TL;DR: Three ontologies created specifically to address the needs of the systems biology community are described, including the Systems Biology Ontology, which provides semantic information about the model components, and the Kinetic Simulation Algorithm Ontology and the Terminology for the Description of Dynamics, which categorizes dynamical features of the simulation results and general systems behavior.
Abstract: The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.
298 citations
Cited by
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TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
32,980 citations
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TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
Abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
6,201 citations
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TL;DR: The Reactome Knowledgebase provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations—an extended version of a classic metabolic map, in a single consistent data model.
Abstract: The Reactome Knowledgebase (www.reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently.
5,065 citations
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations
01 Jan 2000
3,536 citations