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Uncertainty in Biology

A Computational Modeling Approach

  • Book
  • © 2016

Overview

Editors:
  1. Liesbet Geris

    1. Biomechanics Research Unit, University of Liège, Liège 1, Belgium

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    You can also search for this editor in PubMed   Google Scholar

  2. David Gomez-Cabrero

    1. Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden

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    You can also search for this editor in PubMed   Google Scholar

  • 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 outcome of key research groups in the field
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

      • Liesbet Geris, David Gomez-Cabrero
      Pages 3-11

  3. Modeling Establishment Under Uncertainty

    1. Front Matter

      Pages 13-13
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    2. Reverse Engineering Under Uncertainty

      • Paul Kirk, Daniel Silk, Michael P. H. Stumpf
      Pages 15-32

    3. Probabilistic Computational Causal Discovery for Systems Biology

      • Vincenzo Lagani, Sofia Triantafillou, Gordon Ball, Jesper Tegnér, Ioannis Tsamardinos
      Pages 33-73

    4. Stochastic Modeling and Simulation Methods for Biological Processes: Overview

      • Annelies Lejon, Giovanni Samaey
      Pages 75-124

  4. Model Selection and Parameter Fitting

    1. Front Matter

      Pages 125-125
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    2. The Experimental Side of Parameter Estimation

      • Monica Schliemann-Bullinger, Dirk Fey, Thierry Bastogne, Rolf Findeisen, Peter Scheurich, Eric Bullinger
      Pages 127-154

    3. Statistical Data Analysis and Modeling

      • Millie Shah, Zeinab Chitforoushzadeh, Kevin A. Janes
      Pages 155-175

    4. Optimization in Biology Parameter Estimation and the Associated Optimization Problem

      • Gunnar Cedersund, Oscar Samuelsson, Gordon Ball, Jesper Tegnér, David Gomez-Cabrero
      Pages 177-197

    5. Interval Methods

      • Warwick Tucker
      Pages 199-211

    6. Model Extension and Model Selection

      • Mikael Sunnåker, Joerg Stelling
      Pages 213-241

    7. Bayesian Model Selection Methods and Their Application to Biological ODE Systems

      • 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
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    2. Sloppiness and the Geometry of Parameter Space

      • Brian K. Mannakee, Aaron P. Ragsdale, Mark K. Transtrum, Ryan N. Gutenkunst
      Pages 271-299

    3. Modeling and Model Simplification to Facilitate Biological Insights and Predictions

      • Olivia Eriksson, Jesper Tegnér
      Pages 301-325

    4. Sensitivity Analysis by Design of Experiments

      • An Van Schepdael, Aurélie Carlier, Liesbet Geris
      Pages 327-366

    5. Waves in Spatially-Disordered Neural Fields: A Case Study in Uncertainty Quantification

      • Carlo R. Laing
      Pages 367-391

    6. In-Silico Models of Trabecular Bone: A Sensitivity Analysis Perspective

      • Marlène Mengoni, Sebastien Sikora, Vinciane d’Otreppe, Ruth Karen Wilcox, Alison Claire Jones
      Pages 393-423
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Keywords

About this book

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.

Editors and Affiliations

  • Biomechanics Research Unit, University of Liège, Liège 1, Belgium

    Liesbet Geris

  • Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden

    David Gomez-Cabrero

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