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Uncertainty in Biology

E-BookPDF1 - PDF WatermarkE-Book
478 Seiten
Englisch
Springer International Publishingerschienen am26.10.20151st ed. 2016
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.mehr
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E-BookPDF1 - PDF WatermarkE-Book
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Produkt

KlappentextComputational 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.
Details
Weitere ISBN/GTIN9783319212968
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2015
Erscheinungsdatum26.10.2015
Auflage1st ed. 2016
ReiheStudies in Mechanobiology, Tissue Engineering and Biomaterials
Reihen-Nr.17
Seiten478 Seiten
SpracheEnglisch
IllustrationenIX, 478 p.
Artikel-Nr.1969258
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
An Introduction to Uncertainty in the Development of Computational Models of Biological Processes.- Reverse Engineering under Uncertainty.- Probabilistic Computational Causal Discovery for Systems Biology.- Macroscopic Simulation of Individual-Based Stochastic Models for Biological Processes.- The Experimental Side of Parameter Estimation.- Statistical Data Analysis and Modeling.- Optimization in Biology: Parameter Estimation and the Associated Optimization Problem.- Interval Methods.- Model Extension and Model Selection.- Bayesian Model Selection Methods and their Application to Biological ODE Systems.- Sloppiness and the Geometry of Parameter Space.- Modeling and Model Simplification to Facilitate Biological Insights and Predictions.- Sensitivity Analysis by Design of Experiments.- Waves in Spatially-Disordered Neural Fields: a Case Study in Uncertainty Quantification.- X In-silico Models of Trabecular Bone: a Sensitivity Analysis Perspective.- Neuroswarm: a Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons.- Prediction Uncertainty Estimation Despite Unidentifiability: an Overview of Recent Developments.- Computational Modeling Under Uncertainty: Challenges and Opportunities.mehr

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