Guide to Different Topics of the Book

Biological systems and processes

1. Metabolism (2, 3, 4, 11.1, 11.4, 12.1)
2. Gene regulatory network (7, 8.2, 9)
3. Gene expression regulation (2, 9)
4. Signaling systems (8.2, 12.2)
5. Cell cycle (12.3)
6. Development (7.3)
7. Aging (12.4)

Concepts for biological function

1. Qualitative behavior (3, 7.1)
2. Parameter sensitivity and robustness (4.2, 10.2)
3. Modularity and functional subsystems (6.4, 8.3)
4. Robustness against failure (10.2)
5. Information (10.3, 15.6)
6. Population heterogeneity (10.3)
7. Optimality (3.2, 11.1, 11.2)
8. Evolution (11.3)
9. Population dynamics and game theory (11.4)

Model types with different levels of abstraction

1. Statistical particle models (15.6)
2. Stochastic biochemical models (5.1.2, 7.2, 9.4, 15.4)
3. Kinetic models (4, 5.1.1, 11, 12, 16.7)
4. Constraint-based models (3.2)
5. Discrete models (7.1)
6. Spatial models (7.3)

Mathematical frameworks to describe cell states

1. Topological (network structures) (3.1, 8)
2. Structural stoichiometric models (3)
3. Dynamical systems (4, 12, 15.2)
4. Deterministic linear models (6.3)
5. Deterministic kinetic models (4, 9.1, 12)
6. Uncertain parameters (10.1)
7. Optimization and control theory (4.2, 11.1,11.2,15.5)

Experimental Techniques

1. Experimental techniques (14)

Modeling skills

1. Model building (2, 3, 4, 5.1, 7)
2. Model annotation (5.4)
3. Parameter estimation (6.1)
4. Model testing and selection (6.2)
5. Local sensitivity analysis/control theory (4.2, 10.1, 10.2)
6. Global sensitivity/uncertainty analysis (10.1)
7. Model reduction (6.3)
8. Model combination (5.4, 6.4)
9. Network theory (8)
10. Statistics (15.3, 15.7)
11. Optimization of model outputs and structure (11.1)
12. Optimal temporal control (11.2, 15.5)

Practical issues in modeling

1. Use of databases (5.3, 16)
2. Data formats (5.2, 5.4)
3. Data sources (5.3, 16)
4. Modeling software (5.1, 17)
5. Simulation techniques and tools (5.1)
6. Model visualization (5.1)
7. Data visualization (8.3)