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Computational Medical Bioinformatics
MOJ Applied Bionics and Biomechanics Mini Review Open Access Computational medical bioinformatics Abstract Volume 2 Issue 4 - 2018 Biotechnology is a technological discipline based on Biology and applied to meet the Rodrigo Arturo Marquet Rivera, Guillermo needs of human life. Which can be considered essential, as is health. This discipline has developed in the last decades a series of medical applications that are interrelated Urriolagoitia Sosa, Rosa Alicia Hernández with other fields of science, which are not precisely those inherent in the health Vázquez, Beatriz Romero Ángeles, Juan sciences. One of the main ones is Computational Bioinformatics, which has become Alejandro Vázquez Feijoo, Guillermo a useful tool in the practice of the different medical areas through the generation of Urriolagoitia-Calderón biomodels. Instituto Politécnico Nacional, México Correspondence: Guillermo Urriolagoitia Sosa, Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Sección de Estudios de Posgrado e Investigación Unidad Profesional Adolfo López Mateos, Zacatenco. Av. IPN s/n Edificio 5, 2° piso Col. Lindavista, Delegación Gustavo A. Madero, C.P. 07320, México, Email [email protected] Received: June 30, 2018 | Published: August 17, 2018 Introduction the images obtained from them allow a visualization and manipulation that offers better results (Figure 1). Computational Bioinformatics is a novel tool that can be applied in the study of the behaviour presented by the different levels of Proposal organization of living matter (cells, tissues and organs), under the effects of stimuli and external agents, such as burdens mechanical, With the images obtained from the imaging studies, it is possible to which influence the behaviour of cellular metabolism.1 Through generate biomodels that allow a better exploration of the anatomical the generation of anatomical biomodels, is interested in solving structures to be treated, observe them with greater precision and biological problems in a multidisciplinary and interdisciplinary way. -
Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology
Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology Michael L. Blinova,g, John H. Gennarib,g, Jonathan R. Karrc,d,g, Ion I. Morarua,g, David P. Nickersone,g, Herbert M. Saurof,g,h aCenter for Cell Analysis and Modeling, UConn Health, Farmington, CT, US bDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, US cIcahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, US dDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, US eAuckland Bioengineering Institute, University of Auckland, Auckland, NZ fDepartment of Bioengineering, University of Washington, Seattle, WA, US gThese authors contributed equally to this work hCorrespondence: [email protected] Abstract Although reproducibility is a core tenet of the scientific method, it remains challenging to re- produce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For exam- ple, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified sev- eral domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology. -
Data Extraction for the Reaction Kinetics Database SABIO-RK$
Perspectives in Science (]]]]) ], ]]]–]]] Available online at www.sciencedirect.com www.elsevier.com/locate/pisc REVIEW Data extraction for the reaction kinetics database SABIO-RK$ Ulrike Wittign, Renate Kania, Meik Bittkowski, Elina Wetsch, Lei Shi, Lenneke Jong, Martin Golebiewski, Maja Rey, Andreas Weidemann, Isabel Rojas, Wolfgang Müller Scientific Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany Received 5 March 2013; accepted 4 November 2013; Available online XX, XX, 2014 KEYWORDS Abstract Database; SABIO-RK (http://sabio.h-its.org/) is a web-accessible, manually curated database that has Reaction kinetics; been established as a resource for biochemical reactions and their kinetic properties with a Biocuration; focus on supporting the computational modeling to create models of biochemical reaction Ontology networks. SABIO-RK data are mainly extracted from literature but also directly submitted from lab experiments. In most cases the information in the literature is distributed across the whole publication, insufficiently structured and often described without standard terminology. Therefore the manual extraction of knowledge from the literature requires biological experts to understand the paper and interpret the data. The database offers the literature data in a structured format including annotations to controlled vocabularies, ontologies and external databases which supports modellers, as well as experimentalists, in the very time consuming process of collecting information from different publications. Here we describe the data extraction and curation efforts needed for SABIO-RK and give recommendations for publishing kinetic data in a complete and structured manner. & 2014 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). -
Artificial Intelligence in Biological Modelling François Fages
Artificial Intelligence in Biological Modelling François Fages To cite this version: François Fages. Artificial Intelligence in Biological Modelling. A Guided Tour of Artificial Intelligence Research, 2020, Volume III: Interfaces and Applications of Artificial Intelligence, 978-3-030-06170-8_8. 10.1007/978-3-030-06170-8_8. hal-01409753v2 HAL Id: hal-01409753 https://hal.inria.fr/hal-01409753v2 Submitted on 11 May 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. AI in Biological Modelling François Fages Abstract Systems Biology aims at elucidating the high-level functions of the cell from their biochemical basis at the molecular level. A lot of work has been done for collecting genomic and post-genomic data, making them available in databases and ontologies, building dynamical models of cell metabolism, signalling, division cy- cle, apoptosis, and publishing them in model repositories. In this chapter we review different applications of AI to biological systems modelling. We focus on cell pro- cesses at the unicellular level which constitutes most of the work achieved in the last two decades in the domain of Systems Biology. We show how rule-based languages and logical methods have played an important role in the study of molecular inter- action networks and of their emergent properties responsible for cell behaviours. -
Expanding Horizons to Include More Modelling Approaches and Formats Mihai Glont1, Tung V
D1248–D1253 Nucleic Acids Research, 2018, Vol. 46, Database issue Published online 2 November 2017 doi: 10.1093/nar/gkx1023 BioModels: expanding horizons to include more modelling approaches and formats Mihai Glont1, Tung V. N. Nguyen1, Martin Graesslin2, Robert Halke¨ 3,RazaAli1, Jochen Schramm2, Sarala M. Wimalaratne1, Varun B. Kothamachu1,4, Nicolas Rodriguez4, Maciej J. Swat1, Jurgen Eils2, Roland Eils2, Camille Laibe1, Rahuman S. Malik-Sheriff1,*, Vijayalakshmi Chelliah1, Nicolas Le Novere` 4,* and Henning Hermjakob1,* 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK, 2Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg, BioQuant, IPMB and DKFZ Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany, 3University of Rostock, Rostock, Germany and 4Babraham Institute, Cambridge CB22 3AT, UK Received September 25, 2017; Editorial Decision October 17, 2017; Accepted October 18, 2017 ABSTRACT a resource that facilitates the exchange, reuse and repurpos- ing of the models. Since its inception, BioModels’ content BioModels serves as a central repository of mathe- has been steadily increasing, making it a central portal that matical models representing biological processes. It provides reproducible, high-quality, freely accessible mod- offers a platform to make mathematical models easily els published in the scientific literature. BioModels hosts shareable across the systems modelling community, over 8400 models from the scientific literature submitted thereby supporting model reuse. To facilitate host- by authors as well as internal and external BioModels cu- ing a broader range of model formats derived from rators. This includes a recent submission of 6750 patient- diverse modelling approaches and tools, a new in- specific genome-scale metabolic models derived from tu- frastructure for BioModels has been developed that mour samples of individual patients (2). -
"Using Views of Systems Biology Cloud: Application
Theory Biosci. (2011) 130:45–54 DOI 10.1007/s12064-010-0108-6 ORIGINAL PAPER Using views of Systems Biology Cloud: application for model building Oliver Ruebenacker • Michael Blinov Received: 17 November 2009 / Accepted: 4 July 2010 / Published online: 21 August 2010 Ó Springer-Verlag 2010 Abstract A large and growing network (‘‘cloud’’) of PubMed or database records for pathways, substances and interlinked terms and records of items of Systems Biology processes. A typical model creation (such as in Virtual Cell knowledge is available from the web. These items include modeling and simulation framework (Moraru et al. 2008)) pathways, reactions, substances, literature references, involves specifying species (represented by one type of organisms, and anatomy, all described in different data node), reactions (represented by another type of node), and sets. Here, we discuss how the knowledge from the cloud specify which species serve as reactants, products or cat- can be molded into representations (views) useful for data alysts (represented by different types of arrows connecting visualization and modeling. We discuss methods to create species and reactions). If the model is intended to be and use various views relevant for visualization, modeling, reused, then the user will use knowledge from the literature and model annotations, while hiding irrelevant details to add annotations, such as assigning UniProt (http:// without unacceptable loss or distortion. We show that uniprot.org/) or ChEBI (Degtyarenko et al. 2009) identifi- views are compatible with understanding substances and ers to species nodes and PubMed identifiers to reaction processes as sets of microscopic compounds and events nodes. The connection between the reality described in the respectively, which allows the representation of special- article or database, the model elements (species, reaction, izations and generalizations as subsets and supersets participation, etc.) and the annotation (UniProt and Pub- respectively. -
Organigramm Des Rektorats Einrichtungen Des Rektorats Der
Organizational chart of the University of Veterinary Medicine, Vienna Governing Bodies of the University Senate Rectorate University Council Research and Teaching Department 1 Department 2 Department 3 Department 4 Department 5 ________________________________________________________ ________________________________________________________ ________________________________________________________ ________________________________________________________ ________________________________________________________ Department of Biomedical Sciences Department of Pathobiology Department/University Clinic for Farm Department/University Clinic for Department of Interdisciplinary Life Animals and Veterinary Public Health Companion Animals and Horses Sciences Institute of Computational Medicine Institute of Morphology Institute for in-vivo and in-vitro models Institute of Microbiology Institute of Food Safety, Food Technology and University Clinic* for Small Animals Research Institute of Wildlife Ecology Institute of Medical Biochemistry Functional Microbiology Veterinary Public Health Anaesthesiology and perioperative Intensive- Conservation Medicine Institute of Pharmacology and Toxicology Institute of Immunology Food Microbiology Care Medicine Konrad Lorenz Institute of Ethology Clinical Pharmacology Institute of Parasitology Food Hygiene and Technology Diagnostic Imaging Ornithology Institute of Physiology, Patho physiology and Institute of Pathology Veterinary Public Health and Epidemiology Obstetrics, Gynaecology and Andrology -
SABIO-RK: a Data Warehouse for Biochemical Reactions and Their Kinetics
Journal of Integrative Bioinformatics 2007 http://journal.imbio.de/ SABIO-RK: A data warehouse for biochemical reactions and their kinetics. Olga Krebs*, Martin Golebiewski, Renate Kania, Saqib Mir, Jasmin Saric, Andreas Weidemann, Ulrike Wittig and Isabel Rojas Scientific Databases and Visualisation Group, EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany Abstract Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics), a curated database with information about biochemical reactions and their kinetic (http://creativecommons.org/licenses/by-nc-nd/3.0/). properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic License use at http://sabio.villa-bosch.de/SABIORK/. Unported 1 Introduction 3.0 Systems biology deals with the analysis and prediction of the dynamic behaviour of biological networks through mathematical modelling based on experimental data [1]. It focuses on the connections and interactions of the components in the cell and in general in the organism, all as part of one system. The modelling and simulation of biochemical reaction -
Systems Biology Graphical Notation Systems Biology Markup
Systems Biology Markup Language (SBML) 2057 S models and tacit rule-based models have long been used in product development and safety testing in Systems Biology Graphical Notation various engineering disciplines, such as aerospace engineering and electronic circuit design. It can be ▶ SBGN envisaged that predictive approaches is expected to increase significantly in the coming years in drug dis- covery too. The extent of omics-scale data and the advances in systems technologies to enable compre- Systems Biology Markup Language hension of such large complex data in the form of (SBML) meaningful biological models are promising to help in this process. The power of systems biology methods Michael Hucka is such that it may become possible in the not-too Computing and Mathematical Sciences, distant future, that a disease could get diagnosed in California Institute of Technology, Pasadena, a clinical setting and characterized at the systems level CA, USA with precise genotype and phenotype definitions, lead- ing all the way up to predictive quantitative titrations of the available remedies and finally personalized Synonyms prescriptions. SBML References Boran D, Iyengar R (2010) Systems approaches to polyphar- Definition macology and drug discovery. Curr Opin Drug Discov Dev 13(3):297–309 SBML (the Systems Biology Markup Language) is Butcher EC, Berg EL et al (2004) Systems biology in drug a representation format, based upon XML, used for discovery. Nat Biotechnol 22(10):1253–1259 Forst CV (2006) Host-pathogen systems biology. Drug Discov communicating and storing computational models of Today 11:220–227 biological processes (Hucka et al. 2003). SBML can Glaab E, Baudot A et al (2012) EnrichNet: network-based represent many different classes of biological phenom- gene set enrichment analysis. -
Principled Annotation of Quantitative Models in Systems Biology
Principled annotation of quantitative models in Systems Biology Nicolas Le Novère, EMBL-EBI Computational model in SysBio Computational model in SysBio Not a bimolecular interaction Computational model in SysBio Not a bimolecular interaction complexes Computational model in SysBio Not a bimolecular interaction complexes Non-phos and phos of the same Computational model in SysBio Why not C2P? Not a bimolecular interaction complexes Non-phos and phos of the same Computational model in SysBio Tyson JJ (1991) Modeling the cell division cycle: cdc2 and cyclin interactions. Proc. Natl. Acad. Sci. U.S.A. 88: 7328-7332 http://www.ebi.ac.uk/biomodels/ BIOMD0000000005 What do-we want to do with it f(x,y) u(x,y) g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) What do-we want to do with it f(x,y) u(x,y) g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) Encapsulation a(i,j) b(i,j) g(i,j) What do-we want to do with it f(x,y) u(x,y) Communication g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) Encapsulation a(i,j) b(i,j) g(i,j) What do-we want to do with it f(x,y) u(x,y) Communication g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) Standards of representation Encapsulation Interfaces (ontologies) a(i,j) Data resources b(i,j) g(i,j) Is SBML enough? What's missing? An SBML model lists participants, but does not identify them. -
Controlled Vocabularies and Semantics in Systems Biology
Molecular Systems Biology 7; Article number 543; doi:10.1038/msb.2011.77 Citation: Molecular Systems Biology 7: 543 & 2011 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/11 www.molecularsystemsbiology.com PERSPECTIVE Controlled vocabularies and semantics in systems biology Me´lanie Courtot1,19, Nick Juty2,19, Christian Knu¨pfer3,19, provides semantic information about the model compo- Dagmar Waltemath4,19, Anna Zhukova2,19, Andreas Dra¨ger5, nents. The Kinetic Simulation Algorithm Ontology (KiSAO) Michel Dumontier6, Andrew Finney7, Martin Golebiewski8, supplies information about existing algorithms available Janna Hastings2, Stefan Hoops9, Sarah Keating2, Douglas B for the simulation of systems biology models, their characterization and interrelationships. The Terminology Kell10,11, Samuel Kerrien2, James Lawson12, Allyson Lister13,14, for the Description of Dynamics (TEDDY) categorizes James Lu15, Rainer Machne16, Pedro Mendes10,17, 14 2 10,17 dynamical features of the simulation results and general Matthew Pocock , Nicolas Rodriguez , Alice Villeger , systems behavior. The provision of semantic information 13 2 2 Darren J Wilkinson , Sarala Wimalaratne , Camille Laibe , extends a model’s longevity and facilitates its reuse. It 18 2, Michael Hucka and Nicolas Le Nove`re * provides useful insight into the biology of modeled processes, and may be used to make informed decisions 1 Terry Fox Laboratory, Vancouver, Canada, on subsequent simulation experiments. 2 Department of Computational Neurobiology, EMBL European Bioinformatics -
Biological Pathways Exchange Language Level 3, Release Version 1 Documentation
BioPAX – Biological Pathways Exchange Language Level 3, Release Version 1 Documentation BioPAX Release, July 2010. The BioPAX data exchange format is the joint work of the BioPAX workgroup and Level 3 builds on the work of Level 2 and Level 1. BioPAX Level 3 input from: Mirit Aladjem, Ozgun Babur, Gary D. Bader, Michael Blinov, Burk Braun, Michelle Carrillo, Michael P. Cary, Kei-Hoi Cheung, Julio Collado-Vides, Dan Corwin, Emek Demir, Peter D'Eustachio, Ken Fukuda, Marc Gillespie, Li Gong, Gopal Gopinathrao, Nan Guo, Peter Hornbeck, Michael Hucka, Olivier Hubaut, Geeta Joshi- Tope, Peter Karp, Shiva Krupa, Christian Lemer, Joanne Luciano, Irma Martinez-Flores, Zheng Li, David Merberg, Huaiyu Mi, Ion Moraru, Nicolas Le Novere, Elgar Pichler, Suzanne Paley, Monica Penaloza- Spinola, Victoria Petri, Elgar Pichler, Alex Pico, Harsha Rajasimha, Ranjani Ramakrishnan, Dean Ravenscroft, Jonathan Rees, Liya Ren, Oliver Ruebenacker, Alan Ruttenberg, Matthias Samwald, Chris Sander, Frank Schacherer, Carl Schaefer, James Schaff, Nigam Shah, Andrea Splendiani, Paul Thomas, Imre Vastrik, Ryan Whaley, Edgar Wingender, Guanming Wu, Jeremy Zucker BioPAX Level 2 input from: Mirit Aladjem, Gary D. Bader, Ewan Birney, Michael P. Cary, Dan Corwin, Kam Dahlquist, Emek Demir, Peter D'Eustachio, Ken Fukuda, Frank Gibbons, Marc Gillespie, Michael Hucka, Geeta Joshi-Tope, David Kane, Peter Karp, Christian Lemer, Joanne Luciano, Elgar Pichler, Eric Neumann, Suzanne Paley, Harsha Rajasimha, Jonathan Rees, Alan Ruttenberg, Andrey Rzhetsky, Chris Sander, Frank Schacherer,