Modelling+formalisms

Next: What is a model (finite state machines)

=2 Modeling formalisms and concepts=

In this section we lay the ground work: an overview of various kinds of modeling and their implications. We discuss what kind of concepts we can derive from models.

In this section:
 * 1) What is a model?
 * Finite state machines as a model prototype
 * 1) Short-cuts
 * Ordering of states (ODEs and MAPs)
 * Decomposition into sub-systems (CA, Boolean networks)
 * 1) Cellular automata
 * Introduction to cellular automata
 * Modulo prime
 * Game of life (Conway)
 * 1) CA as paradigm systems
 * Lymph node model
 * 1) Generic CA
 * Generic behaviour of CAs (Wolfram)
 * Predictions about CAs (Langton)
 * 1) Mean field approximation
 * 2) Boolean Networks
 * Properties of boolean networks: NK networks (Kauffmann)
 * Forcing structures
 * What do we expect from gene regulation networks and what is important?
 * How do we go from experimental data to regulatory networks?
 * Modeling the cell cycle: data, boolean networks and ODEs
 * 1) Multi-CA: emergence
 * Mesoscale patterns
 * Rule 54 (Crutchfield: emergent computation)
 * 1) Mutli-CA: predefined
 * Cellular-Potts model
 * Differential cell adhesion
 * Movement and chemotaxis in lymph nodes
 * 1) Event-based models
 * Gillespie algorithm
 * 1) Overview of spatial model formalisms
 * 2) Timing regimes
 * Particle conservation (Margolus diffusion)
 * 1) Rock of Gibraltar (so what are models?)

Next: What is a model (finite state machines)