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Computational immunology offers in silico strategies for understanding of complex processes occurring in the natural immune system of a living organism that are difficult to explore by traditional in vivo or in vitro techniques. The monograph introduces conceptual languages and approaches for modelling biological processes. The Agent Modelling Language is investigated for conceptualisation of immune processes. AML-based diagrams represent properties and processes occurring in a lymph node.
Book Synopsis Conceptual Modelling in Computational Immunology by : Martina Husáková
Download or read book Conceptual Modelling in Computational Immunology written by Martina Husáková and published by Tomáš Bruckner. This book was released on 2015-09-10 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational immunology offers in silico strategies for understanding of complex processes occurring in the natural immune system of a living organism that are difficult to explore by traditional in vivo or in vitro techniques. The monograph introduces conceptual languages and approaches for modelling biological processes. The Agent Modelling Language is investigated for conceptualisation of immune processes. AML-based diagrams represent properties and processes occurring in a lymph node.
The natural immune system is an amazing complex system aiming at the homeostasis maintenance of a living organism. The non-linear, dynamic and complex nature of this system renders the behaviour far from predictable. This fact complicates exploration of immune processes and their understanding with traditional in vivo or in vitro strategies. Techniques of computer science are a promising alternative for the investigation of the natural immune system. Computational immunology investigates an inner life of the natural immune system with the assistance of various approaches of computer science (artificial or computational intelligence), mathematics, physics or statistics. It offers in silico strategies helping with the understanding of phenomena that are difficult to explore through traditional techniques. The monograph introduces the historical context of this research area and the first computer science-based applications. It concentrates mainly on conceptual modelling of various biological processes with the usage of particular conceptual languages and approaches (concept maps, entity-relationship diagrams, ontologies, topic maps, SBML, CellML, SBGN, statecharts and UML) differing in the degree of formality and use. Conceptual models are crucial, because they highlight the most important "players" of immune processes and relations between them. Conceptualisation is inevitable especially if we study really complex system. The primary goal of the monograph is to investigate the usefulness of the Agent Modelling Language for conceptualisation of particular immune processes. The Agent Modelling Language (AML) extends the UML for conceptualisation of multi-agent systems. The natural immune system is perceived as the multi-agent system in the monograph. Selected types of AML-based diagrams represent properties and processes occurring in a secondary lymphoid organ - a lymph node where interactions between T-cells and dendritic cells are mainly taken into account.
Book Synopsis Conceptual Modelling in Computational Immunology by : Martina Husakova
Download or read book Conceptual Modelling in Computational Immunology written by Martina Husakova and published by Tomas Bruckner. This book was released on 2015-09-10 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: The natural immune system is an amazing complex system aiming at the homeostasis maintenance of a living organism. The non-linear, dynamic and complex nature of this system renders the behaviour far from predictable. This fact complicates exploration of immune processes and their understanding with traditional in vivo or in vitro strategies. Techniques of computer science are a promising alternative for the investigation of the natural immune system. Computational immunology investigates an inner life of the natural immune system with the assistance of various approaches of computer science (artificial or computational intelligence), mathematics, physics or statistics. It offers in silico strategies helping with the understanding of phenomena that are difficult to explore through traditional techniques. The monograph introduces the historical context of this research area and the first computer science-based applications. It concentrates mainly on conceptual modelling of various biological processes with the usage of particular conceptual languages and approaches (concept maps, entity-relationship diagrams, ontologies, topic maps, SBML, CellML, SBGN, statecharts and UML) differing in the degree of formality and use. Conceptual models are crucial, because they highlight the most important "players" of immune processes and relations between them. Conceptualisation is inevitable especially if we study really complex system. The primary goal of the monograph is to investigate the usefulness of the Agent Modelling Language for conceptualisation of particular immune processes. The Agent Modelling Language (AML) extends the UML for conceptualisation of multi-agent systems. The natural immune system is perceived as the multi-agent system in the monograph. Selected types of AML-based diagrams represent properties and processes occurring in a secondary lymphoid organ - a lymph node where interactions between T-cells and dendritic cells are mainly taken into account.
This two-volume set (LNAI 9329 and LNAI 9330) constitutes the refereed proceedings of the 7th International Conference on Collective Intelligence, ICCCI 2014, held in Madrid, Spain, in September 2015. The 110 full papers presented were carefully reviewed and selected from 186 submissions. They are organized in topical sections such as multi-agent systems; social networks and NLP; sentiment analysis; computational intelligence and games; ontologies and information extraction; formal methods and simulation; neural networks, SMT and MIS; collective intelligence in Web systems – Web systems analysis; computational swarm intelligence; cooperative strategies for decision making and optimization; advanced networking and security technologies; IT in biomedicine; collective computational intelligence in educational context; science intelligence and data analysis; computational intelligence in financial markets; ensemble learning; big data mining and searching.
Book Synopsis Computational Collective Intelligence by : Manuel Núñez
Download or read book Computational Collective Intelligence written by Manuel Núñez and published by Springer. This book was released on 2015-09-09 with total page 661 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (LNAI 9329 and LNAI 9330) constitutes the refereed proceedings of the 7th International Conference on Collective Intelligence, ICCCI 2014, held in Madrid, Spain, in September 2015. The 110 full papers presented were carefully reviewed and selected from 186 submissions. They are organized in topical sections such as multi-agent systems; social networks and NLP; sentiment analysis; computational intelligence and games; ontologies and information extraction; formal methods and simulation; neural networks, SMT and MIS; collective intelligence in Web systems – Web systems analysis; computational swarm intelligence; cooperative strategies for decision making and optimization; advanced networking and security technologies; IT in biomedicine; collective computational intelligence in educational context; science intelligence and data analysis; computational intelligence in financial markets; ensemble learning; big data mining and searching.
Computational biology, mathematical biology, biology and biomedicine are currently undergoing spectacular progresses due to a synergy between technological advances and inputs from physics, chemistry, mathematics, statistics and computer science. The goal of this book is to evidence this synergy by describing selected developments in the following fields: bioinformatics, biomedicine and neuroscience. This work is unique in two respects - first, by the variety and scales of systems studied and second, by its presentation: Each chapter provides the biological or medical context, follows up with mathematical or algorithmic developments triggered by a specific problem and concludes with one or two success stories, namely new insights gained thanks to these methodological developments. It also highlights some unsolved and outstanding theoretical questions, with a potentially high impact on these disciplines. Two communities will be particularly interested in this book. The first one is the vast community of applied mathematicians and computer scientists, whose interests should be captured by the added value generated by the application of advanced concepts and algorithms to challenging biological or medical problems. The second is the equally vast community of biologists. Whether scientists or engineers, they will find in this book a clear and self-contained account of concepts and techniques from mathematics and computer science, together with success stories on their favorite systems. The variety of systems described represents a panoply of complementary conceptual tools. On a practical level, the resources listed at the end of each chapter (databases, software) offer invaluable support for getting started on a specific topic in the fields of biomedicine, bioinformatics and neuroscience.
Book Synopsis Modeling in Computational Biology and Biomedicine by : Frédéric Cazals
Download or read book Modeling in Computational Biology and Biomedicine written by Frédéric Cazals and published by Springer Science & Business Media. This book was released on 2012-11-06 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational biology, mathematical biology, biology and biomedicine are currently undergoing spectacular progresses due to a synergy between technological advances and inputs from physics, chemistry, mathematics, statistics and computer science. The goal of this book is to evidence this synergy by describing selected developments in the following fields: bioinformatics, biomedicine and neuroscience. This work is unique in two respects - first, by the variety and scales of systems studied and second, by its presentation: Each chapter provides the biological or medical context, follows up with mathematical or algorithmic developments triggered by a specific problem and concludes with one or two success stories, namely new insights gained thanks to these methodological developments. It also highlights some unsolved and outstanding theoretical questions, with a potentially high impact on these disciplines. Two communities will be particularly interested in this book. The first one is the vast community of applied mathematicians and computer scientists, whose interests should be captured by the added value generated by the application of advanced concepts and algorithms to challenging biological or medical problems. The second is the equally vast community of biologists. Whether scientists or engineers, they will find in this book a clear and self-contained account of concepts and techniques from mathematics and computer science, together with success stories on their favorite systems. The variety of systems described represents a panoply of complementary conceptual tools. On a practical level, the resources listed at the end of each chapter (databases, software) offer invaluable support for getting started on a specific topic in the fields of biomedicine, bioinformatics and neuroscience.
Intelligent information and database systems are two closely related subfields of modern computer science which have been known for over thirty years. They focus on the integration of artificial intelligence and classic database technologies to create the class of next generation information systems. The book focuses on new trends in intelligent information and database systems and discusses topics addressed to the foundations and principles of data, information, and knowledge models, methodologies for intelligent information and database systems analysis, design, and implementation, their validation, maintenance and evolution. They cover a broad spectrum of research topics discussed both from the practical and theoretical points of view such as: intelligent information retrieval, natural language processing, semantic web, social networks, machine learning, knowledge discovery, data mining, uncertainty management and reasoning under uncertainty, intelligent optimization techniques in information systems, security in databases systems, and multimedia data analysis. Intelligent information systems and their applications in business, medicine and industry, database systems applications, and intelligent internet systems are also presented and discussed in the book. The book consists of 38 chapters based on original works presented during the 7th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2015) held on 23–25 March 2015 in Bali, Indonesia. The book is divided into six parts related to Advanced Machine Learning and Data Mining, Intelligent Computational Methods in Information Systems, Semantic Web, Social Networks and Recommendation Systems, Cloud Computing and Intelligent Internet Systems, Knowledge and Language Processing, and Intelligent Information and Database Systems: Applications.
Book Synopsis New Trends in Intelligent Information and Database Systems by : Dariusz Barbucha
Download or read book New Trends in Intelligent Information and Database Systems written by Dariusz Barbucha and published by Springer. This book was released on 2015-03-22 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent information and database systems are two closely related subfields of modern computer science which have been known for over thirty years. They focus on the integration of artificial intelligence and classic database technologies to create the class of next generation information systems. The book focuses on new trends in intelligent information and database systems and discusses topics addressed to the foundations and principles of data, information, and knowledge models, methodologies for intelligent information and database systems analysis, design, and implementation, their validation, maintenance and evolution. They cover a broad spectrum of research topics discussed both from the practical and theoretical points of view such as: intelligent information retrieval, natural language processing, semantic web, social networks, machine learning, knowledge discovery, data mining, uncertainty management and reasoning under uncertainty, intelligent optimization techniques in information systems, security in databases systems, and multimedia data analysis. Intelligent information systems and their applications in business, medicine and industry, database systems applications, and intelligent internet systems are also presented and discussed in the book. The book consists of 38 chapters based on original works presented during the 7th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2015) held on 23–25 March 2015 in Bali, Indonesia. The book is divided into six parts related to Advanced Machine Learning and Data Mining, Intelligent Computational Methods in Information Systems, Semantic Web, Social Networks and Recommendation Systems, Cloud Computing and Intelligent Internet Systems, Knowledge and Language Processing, and Intelligent Information and Database Systems: Applications.
Computational Immunology: Models and Tools encompasses the methodological framework and application of cutting-edge tools and techniques to study immunological processes at a systems level, along with the concept of multi-scale modeling. The book's emphasis is on selected cases studies and application of the most updated technologies in computational modeling, discussing topics such as computational modeling and its usage in immunological research, bioinformatics infrastructure, ODE based modeling, agent based modeling, and high performance computing, data analytics, and multiscale modeling. There are also modeling exercises using recent tools and models which lead the readers to a thorough comprehension and applicability. The book is a valuable resource for immunologists, computational biologists, bioinformaticians, biotechnologists, and computer scientists, as well as all those who wish to broaden their knowledge in systems modeling. Offers case studies with different levels of complexity Provides a detailed view on cutting-edge tools for modeling that are useful to experimentalists with limited computational skills Explores the usage of simulation for hypothesis generation, helping the reader to understand the most valuable points on experimental setting
Book Synopsis Computational Immunology by : Josep Bassaganya-Riera
Download or read book Computational Immunology written by Josep Bassaganya-Riera and published by Academic Press. This book was released on 2015-10-21 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Immunology: Models and Tools encompasses the methodological framework and application of cutting-edge tools and techniques to study immunological processes at a systems level, along with the concept of multi-scale modeling. The book's emphasis is on selected cases studies and application of the most updated technologies in computational modeling, discussing topics such as computational modeling and its usage in immunological research, bioinformatics infrastructure, ODE based modeling, agent based modeling, and high performance computing, data analytics, and multiscale modeling. There are also modeling exercises using recent tools and models which lead the readers to a thorough comprehension and applicability. The book is a valuable resource for immunologists, computational biologists, bioinformaticians, biotechnologists, and computer scientists, as well as all those who wish to broaden their knowledge in systems modeling. Offers case studies with different levels of complexity Provides a detailed view on cutting-edge tools for modeling that are useful to experimentalists with limited computational skills Explores the usage of simulation for hypothesis generation, helping the reader to understand the most valuable points on experimental setting
"Taken together, the body of information contained in this book provides readers with a bird’s-eye view of different aspects of exciting work at the convergence of disciplines that will ultimately lead to a future where we understand how immunity is regulated, and how we can harness this knowledge toward practical ends that reduce human suffering. I commend the editors for putting this volume together." –Arup K. Chakraborty, Robert T. Haslam Professor of Chemical Engineering, and Professor of Physics, Chemistry, and Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA New experimental techniques in immunology have produced large and complex data sets that require quantitative modeling for analysis. This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.
Book Synopsis Systems Immunology by : Jayajit Das
Download or read book Systems Immunology written by Jayajit Das and published by CRC Press. This book was released on 2018-09-03 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Taken together, the body of information contained in this book provides readers with a bird’s-eye view of different aspects of exciting work at the convergence of disciplines that will ultimately lead to a future where we understand how immunity is regulated, and how we can harness this knowledge toward practical ends that reduce human suffering. I commend the editors for putting this volume together." –Arup K. Chakraborty, Robert T. Haslam Professor of Chemical Engineering, and Professor of Physics, Chemistry, and Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA New experimental techniques in immunology have produced large and complex data sets that require quantitative modeling for analysis. This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.
This book constitutes the proceedings of the 6th Brazilian Symposium on Bioinformatics, BSB 2011, held in Brasília, Brazil, in August 2011. The 8 full papers and 4 extended abstracts presented were carefully peer-reviewed and selected for inclusion in this book. The BSB topics of interest cover many areas of bioinformatics that range from theoretical aspects of problems in bioinformatics to applications in molecular biology, biochemistry, genetics, and associated subjects.
Book Synopsis Advances in Bioinformatics and Computational Biology by : Osmar Norberto de Souza
Download or read book Advances in Bioinformatics and Computational Biology written by Osmar Norberto de Souza and published by Springer Science & Business Media. This book was released on 2011-07-21 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 6th Brazilian Symposium on Bioinformatics, BSB 2011, held in Brasília, Brazil, in August 2011. The 8 full papers and 4 extended abstracts presented were carefully peer-reviewed and selected for inclusion in this book. The BSB topics of interest cover many areas of bioinformatics that range from theoretical aspects of problems in bioinformatics to applications in molecular biology, biochemistry, genetics, and associated subjects.
A practice-oriented survey of techniques for computational modeling and simulation suitable for a broad range of biological problems. There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational biologists to handle new and unanticipated problems. This text is intended to fill that gap by teaching students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis. It collects in one place a selection of broadly useful models, algorithms, and theoretical analysis tools normally found scattered among many other disciplines. It thereby gives the aspiring student a bag of tricks that will serve him or her well in modeling problems drawn from numerous subfields of biology. These techniques are taught from the perspective of what the practitioner needs to know to use them effectively, supplemented with references for further reading on more advanced use of each method covered. The text, which grew out of a class taught at Carnegie Mellon University, covers models for optimization, simulation and sampling, and parameter tuning. These topics provide a general framework for learning how to formulate mathematical models of biological systems, what techniques are available to work with these models, and how to fit the models to particular systems. Their application is illustrated by many examples drawn from a variety of biological disciplines and several extended case studies that show how the methods described have been applied to real problems in biology.
Book Synopsis Biological Modeling and Simulation by : Russell Schwartz
Download or read book Biological Modeling and Simulation written by Russell Schwartz and published by MIT Press. This book was released on 2008-07-25 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practice-oriented survey of techniques for computational modeling and simulation suitable for a broad range of biological problems. There are many excellent computational biology resources now available for learning about methods that have been developed to address specific biological systems, but comparatively little attention has been paid to training aspiring computational biologists to handle new and unanticipated problems. This text is intended to fill that gap by teaching students how to reason about developing formal mathematical models of biological systems that are amenable to computational analysis. It collects in one place a selection of broadly useful models, algorithms, and theoretical analysis tools normally found scattered among many other disciplines. It thereby gives the aspiring student a bag of tricks that will serve him or her well in modeling problems drawn from numerous subfields of biology. These techniques are taught from the perspective of what the practitioner needs to know to use them effectively, supplemented with references for further reading on more advanced use of each method covered. The text, which grew out of a class taught at Carnegie Mellon University, covers models for optimization, simulation and sampling, and parameter tuning. These topics provide a general framework for learning how to formulate mathematical models of biological systems, what techniques are available to work with these models, and how to fit the models to particular systems. Their application is illustrated by many examples drawn from a variety of biological disciplines and several extended case studies that show how the methods described have been applied to real problems in biology.
This book introduces semantic representations of multiscale, multidomain physiological systems that link to qualitative reasoning and to quantitative analysis of biophysical processes in health and disease. Two major public health problems, diabetes and hypertension, serve as use-cases to illustrate the depth and rigor of such representations for logical inference and quantitative analysis. Central to this approach is the Ontology of Physics for Biology (OPB) that formally represents the foundations of classical physics and engineering system dynamics that are the basis for our understanding of biomedical entities, processes, and functional relationships. Furthermore, we introduce OPB-based software for annotating and abstracting available biosimulation models for reuse, recombination, and for archiving of physics-based biomedical knowledge. We have formalized and leveraged physics-based biological knowledge as a working view of physiology and biophysics from three distinct perspectives: (1) biologists and biomedical investigators, (2) biophysicists and bioengineers, and (3) biomedical ontologists and informaticists. We present a logical and intuitive semantics of classical physics as a tool for mediating and translating biophysical knowledge among biomedical domains. Daniel L. Cook, MD, PhD John H. Gennari, PhD Maxwell L. Neal, PhD
Book Synopsis The Ontology of Physics for Biology by : Daniel L. Cook
Download or read book The Ontology of Physics for Biology written by Daniel L. Cook and published by CRC Press. This book was released on 2023-12-15 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces semantic representations of multiscale, multidomain physiological systems that link to qualitative reasoning and to quantitative analysis of biophysical processes in health and disease. Two major public health problems, diabetes and hypertension, serve as use-cases to illustrate the depth and rigor of such representations for logical inference and quantitative analysis. Central to this approach is the Ontology of Physics for Biology (OPB) that formally represents the foundations of classical physics and engineering system dynamics that are the basis for our understanding of biomedical entities, processes, and functional relationships. Furthermore, we introduce OPB-based software for annotating and abstracting available biosimulation models for reuse, recombination, and for archiving of physics-based biomedical knowledge. We have formalized and leveraged physics-based biological knowledge as a working view of physiology and biophysics from three distinct perspectives: (1) biologists and biomedical investigators, (2) biophysicists and bioengineers, and (3) biomedical ontologists and informaticists. We present a logical and intuitive semantics of classical physics as a tool for mediating and translating biophysical knowledge among biomedical domains. Daniel L. Cook, MD, PhD John H. Gennari, PhD Maxwell L. Neal, PhD
