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Titlebook: Uncertainty in Biology; A Computational Mode Liesbet Geris,David Gomez-Cabrero Book 2016 The Editor(s) (if applicable) and The Author(s), u

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書目名稱Uncertainty in Biology
副標題A Computational Mode
編輯Liesbet Geris,David Gomez-Cabrero
視頻videohttp://file.papertrans.cn/942/941137/941137.mp4
概述Addresses several main issues of building and validating computational models of biomedical processes.Identifies key techniques to model biomedical processes under uncertainty.Presents the main outcom
叢書名稱Studies in Mechanobiology, Tissue Engineering and Biomaterials
圖書封面Titlebook: Uncertainty in Biology; A Computational Mode Liesbet Geris,David Gomez-Cabrero Book 2016 The Editor(s) (if applicable) and The Author(s), u
描述Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
出版日期Book 2016
關(guān)鍵詞Biomedical Processes; Model Fitting; Reverse Engineering; Sensitivity Analysis; approximate Bayesian inf
版次1
doihttps://doi.org/10.1007/978-3-319-21296-8
isbn_softcover978-3-319-34372-3
isbn_ebook978-3-319-21296-8Series ISSN 1868-2006 Series E-ISSN 1868-2014
issn_series 1868-2006
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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An Introduction to Uncertainty in the Development of Computational Models of Biological Processesses. The first step is model establishment under uncertainty. Once models have been established, data can further be used to select which of the proposed models best meets the predefined criteria. Subsequently, parameter values can be optimized for a specific model configuration. Sensitivity analyse
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The Experimental Side of Parameter Estimationive and dynamical models, this data needs to contain time-courses as well as several perturbation experiments. This article gives an overview of and an introduction into various experimental techniques frequently used during the process of developing dynamical models of biological signalling network
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