Uncertainty in biology : a computational modeling approach /
| Corporate Author: | |
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
[2016]
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| Series: | Studies in mechanobiology, tissue engineering, and biomaterials ;
v. 17. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- 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.