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 Content
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.
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