Uncertainty in Biology

A Computational Modeling Approach

  • Liesbet Geris
  • David Gomez-Cabrero
Book

Part of the Studies in Mechanobiology, Tissue Engineering and Biomaterials book series (SMTEB, volume 17)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Introduction

    1. Front Matter
      Pages 1-1
  3. Modeling Establishment Under Uncertainty

    1. Front Matter
      Pages 13-13
    2. Paul Kirk, Daniel Silk, Michael P. H. Stumpf
      Pages 15-32
    3. Vincenzo Lagani, Sofia Triantafillou, Gordon Ball, Jesper Tegnér, Ioannis Tsamardinos
      Pages 33-73
  4. Model Selection and Parameter Fitting

    1. Front Matter
      Pages 125-125
    2. Monica Schliemann-Bullinger, Dirk Fey, Thierry Bastogne, Rolf Findeisen, Peter Scheurich, Eric Bullinger
      Pages 127-154
    3. Millie Shah, Zeinab Chitforoushzadeh, Kevin A. Janes
      Pages 155-175
    4. Gunnar Cedersund, Oscar Samuelsson, Gordon Ball, Jesper Tegnér, David Gomez-Cabrero
      Pages 177-197
    5. Warwick Tucker
      Pages 199-211
    6. Mikael Sunnåker, Joerg Stelling
      Pages 213-241
    7. Sabine Hug, Daniel Schmidl, Wei Bo Li, Matthias B. Greiter, Fabian J. Theis
      Pages 243-268
  5. Sensitivity Analysis and Model Adaptation

    1. Front Matter
      Pages 269-269
    2. Brian K. Mannakee, Aaron P. Ragsdale, Mark K. Transtrum, Ryan N. Gutenkunst
      Pages 271-299
    3. An Van Schepdael, Aurélie Carlier, Liesbet Geris
      Pages 327-366
    4. Marlène Mengoni, Sebastien Sikora, Vinciane d’Otreppe, Ruth Karen Wilcox, Alison Claire Jones
      Pages 393-423

About this book

Introduction

Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.  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.

Keywords

Biomedical Processes Model Fitting Reverse Engineering Sensitivity Analysis approximate Bayesian information criterion causal modelling constraint propagation simulated annealing statistical inference statistical predictive modelling tensor decomposition

Editors and affiliations

  • Liesbet Geris
    • 1
  • David Gomez-Cabrero
    • 2
  1. 1.Biomechanics Research UnitUniversity of LiègeLiège 1Belgium
  2. 2.Center for Molecular MedicineKarolinska University HospitalStockholmSweden

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-21296-8
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-21295-1
  • Online ISBN 978-3-319-21296-8
  • Series Print ISSN 1868-2006
  • Series Online ISSN 1868-2014
  • About this book
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