Uncertainty in Biology: A Computational Modeling Approach

✍ Scribed by Liesbet Geris, David Gomez-Cabrero (eds.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 471
• Series: Studies in Mechanobiology, Tissue Engineering and Biomaterials 17
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-ix
Front Matter....Pages 1-1
An Introduction to Uncertainty in the Development of Computational Models of Biological Processes....Pages 3-11
Front Matter....Pages 13-13
Reverse Engineering Under Uncertainty....Pages 15-32
Probabilistic Computational Causal Discovery for Systems Biology....Pages 33-73
Stochastic Modeling and Simulation Methods for Biological Processes: Overview....Pages 75-124
Front Matter....Pages 125-125
The Experimental Side of Parameter Estimation....Pages 127-154
Statistical Data Analysis and Modeling....Pages 155-175
Optimization in Biology Parameter Estimation and the Associated Optimization Problem....Pages 177-197
Interval Methods....Pages 199-211
Model Extension and Model Selection....Pages 213-241
Bayesian Model Selection Methods and Their Application to Biological ODE Systems....Pages 243-268
Front Matter....Pages 269-269
Sloppiness and the Geometry of Parameter Space....Pages 271-299
Modeling and Model Simplification to Facilitate Biological Insights and Predictions....Pages 301-325
Sensitivity Analysis by Design of Experiments....Pages 327-366
Waves in Spatially-Disordered Neural Fields: A Case Study in Uncertainty Quantification....Pages 367-391
In-Silico Models of Trabecular Bone: A Sensitivity Analysis Perspective....Pages 393-423
Front Matter....Pages 425-425
Neuroswarm: A Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons....Pages 427-447
Prediction Uncertainty Estimation Despite Unidentifiability: An Overview of Recent Developments....Pages 449-466
Computational Modeling Under Uncertainty: Challenges and Opportunities....Pages 467-476
Back Matter....Pages 477-478

✦ Subjects

Biomedical Engineering; Computational Science and Engineering; Computer Appl. in Life Sciences