Chapter 11
Parameter Identification with Adaptive Sparse Grid-based Optimization for Models of Cellular Processes

11.1		Introduction
11.1.1		Adaptive sparse grid interpolation
11.2		Experimental Design
11.3		Materials
11.4		Methods
11.5		Data Acquisition, Anticipated Results, and Interpretation
11.5.1		Sorted grid points
11.5.2		Unique points
11.5.3		Unstable points
11.5.4		Interpretation and Conclusions
11.6		Troubleshooting
11.6.1		Trouble-shooting special cases: small and large problems
11.6.2		Large problem: 10 or more parameters
11.7		Discussion and commentary
11.8		Application Notes
11.8.1		Comparison of adaptive sparse grid and GA based optimization
11.8.2		Adaptive sparse grid-based optimization
11.8.3		Genetic algorithm
11.9		Summary Points
11.10		Acknowledgements
11.11		References

Chapter 12
Reverse Engineering of Biological Networks

12.1		Introduction: Biological Networks and Reverse Engineering
12.1.1		Biological Networks
12.1.2		Network Representation
12.1.3		Motivation and Design Principles
12.1.4		Reverse Engineering
12.2		Materials: Time Series and Omics Data
12.2.1		Metabolomics
12.2.2		Proteomics and Protein Interaction Networks
12.2.3		Transcriptomics
12.3		Approaches for Inference of Biological Networks
12.3.1		Genome-Scale Metabolic Modeling
12.3.2		Boolean Networks
12.3.3		Network Topology from Correlation or Hierarchical Clustering
12.3.4		Bayesian Networks
12.3.5		Ordinary Differential
12.3.5.1		Identification of Small Scale Biochemical Networks
12.3.5.2		Power Law Modeling
12.3.5.3		Automated Reverse Engineering
12.3.5.4		Parameter Estimation
12.4		Network Biology – Exploring the Inferred Networks
12.4.1		Graph Theory
12.4.2		Motifs and Modules
12.4.3		Stoichiometric Analysis
12.4.4		Simulation of Dynamics, Sensitivity Analysis, Control Analysis
12.5		Discussion and Comparison of Approaches
12.6		Summary Points
		Acknowledgements
		References

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