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Uncertainty in Biology A Computational Modeling Approach

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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.

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Cham., 2016

Collation

IX, 478

Language

English

ISBN/ISSN

978-3-319-21296-8

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Biomedical Engineering and Bioengineering
Computational and Systems Biology
Computational Science and Engineering

Specific Detail Info

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Statement of Responsibility

Liesbet Geris, David Gomez-Cabrero

Other Information

Cataloger

Yudi

Source

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Validator

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