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.