Titlebook: Uncertainty in Biology; A Computational Mode Liesbet Geris,David Gomez-Cabrero Book 2016 The Editor(s) (if applicable) and The Author(s), u

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Optimization in Biology Parameter Estimation and the Associated Optimization Problemmputational and algorithmic developments have provided novel tools to improve this estimation step. One of these improvements concerns the optimization step, where the parameter space is explored to find interesting regions. In this chapter we review the parameter estimation problem, with a special

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Interval Methodsne entire sets of parameters, and thus to exhaust the global search within a finite number of steps. In addition, we show that the method can be accelerated by set-valued constraint propagation, which allows great improvement of its efficiency. To illustrate the applicability of our method we apply

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Sloppiness and the Geometry of Parameter Spaceploration can be challenging, because parameter space often has high dimension and complex structure. Recent work, however, has revealed universal structure in parameter space of models for nonlinear systems. In particular, models are often ., with strong parameter correlations and an exponential ra

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Sensitivity Analysis by Design of Experimentss no limit to the number of times a computer experiment can be run, but they are often time-consuming. Moreover, the number of parameters in a computer model is often very large and the range of variation for each of these parameters is often quite extensive. The DOE provides the statistical tools n

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Waves in Spatially-Disordered Neural Fields: A Case Study in Uncertainty Quantificationd homogeneous domains, resulting in equations that are translationally invariant. However, there is an obvious need to better understand the dynamics of such neural field models on heterogeneous domains. One way to include heterogeneity is through the introduction of randomly-chosen “frozen” spatial

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Book 2016he 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 biom