1. Lectures on Cellular Automata Continued Modified and upgraded slides of Martijn Schut schut@cs.vu.nl Vrij Universiteit Amsterdam Lubomir Ivanov Department of Computer Science Iona College and anonymous from Internet

2. Morphogenetic modeling Dynamical Systems and Cellular Automata

3. matter heat matter heat matter energy matter energy entropy dissipator Organisms are dissipative processes in space and time

4. Dynamical system Time Space Energy Model animals as dynamical systems Tissues Groups

5. Which space? - which geometry? parameter space a b geometric space x y state space morphometric space Q=S/A S A ? time velocity 

6. Which space? - which geometry? ? parameter space genotype environment state space phylogeny/ontogeny morphometric space morphology geometric space ontogeny/phenotype GA evolution environment Dynamics of species in environments.

7. discrete time steps T=0,1,2,3,… discrete cells at X,Y,Z=0,1,2,3,... discretize cell states S=0,1,2,3,... Discretization has many aspects t t+1 t+2 We can mix discrete and continuous values in some models

8. Cellular Automaton t t+1 update rules neighbours In standard CA the values of cells are discretized

9. Reduce dimensions: D=1, i.e. array of cells Reduce # of cell states: binary, i.e. 0 or 1 Simplify interactions: nearest neighbours Simplify update rules: deterministic, static A simple Cellular Automaton

10. Effects of simplification CA modelMorphogenesis Spatial discretization~ level of detail (organism, cell,molecule) Temporaldiscretization~ level of detail (cellcycle, reactionrate) Reduction of dimension  profound effects (2D“GameofLife”) Binarizationcomputationalconvenience (01  A,10  T, 00  G,11  C) nearest-neighbour interactions ~ spatialrestrictions simple update rules; simultaneity, ubiquity ~ non-linearity  profound effects

11.   Wolfram's 1D binary CA cell array binary states 5 nearest neighbours „sum-of-states“ update rule: cell array binary states 5 nearest neighbours „sum-of-states“ update rule: Wolfram, S., Physica 10D (1984), 1-35 Observe importance of symmetry of logic function Number of “alive” neighbors

12.   Wolfram‘s 32 „Symmetry-based“ CA rules in 5-neighborhood S  { 0,1 }   { 1,2,3,4,5 } S  2 5 =32 different rules Number of “alive” neighbors

13. Code for rule 6 (00110) 0 S (i,T+1)  neighbours 1 1 0 2 2 1 3 3 1 4 4 0 5 5 Number of “alive” neighbors

14. Rule codes 00000 00001 00010 00011 00100 00101 11111 rule 0 rule 1 rule 2 rule 3 rule 4 rule 5 rule 32 Number of “alive” neighbors

15. Temporal evolution initial configuration (t=0) e.g. random 0/1 t=1 t=2 t=3 rule n

16. Temporal evolution t=0 t=10

17. Rule 6: 00110 t=100 t=0

18. Rule 10: 01010 t=100 t=0

19. CA: a metaphor for morphogenesis static binary pattern translated into state transition rule rule iteration in configuration space genotype translation, epigenetic rules morphogenesis of the phenotype 0 0 1 1 0 0 1 1 0 0 0   1   0   1   0  

20. CA: a model for morphogenesis update rule iterative application of the update rule CA state pattern state space genotype epigenetic interpretation of the genotype, morphogenesis phenotype morphospace

21. CA rule mutations 10010 01011 swap 01010 genotypephenotypedevelopment negate How genotype mutations can change phenotypes?

22. CA rule mutations 0101010010 0110010100101 010010001 01110 11 01010 1 negate swap rule

23. Predictability? pattern at t+  t pattern at t explicit simulation:  iterations of rule n ? hypothetical "simple algorithm" ? How to predict a next element in sequence? Tough!

24. Sum of natural numbers 1… N algorithm 1: 1+2+…+N algorithm2: N(N+1)/2 (Gaussian formula) Sum of natural numbers 1… N algorithm 1: 1+2+…+N algorithm2: N(N+1)/2 (Gaussian formula) Predictability Nth prime number algorithm 1: trial and error algorithm2: ? no general formula Nth prime number algorithm 1: trial and error algorithm2: ? no general formula easyVery tough!

25. CA: no effective pattern prediction pattern at t+  t pattern at t behaviour may be determined only by explicit simulation

26. CA: deterministic, unpredictable, irreversible Simple rules generate complex spatio- temporal behavior For non-trivial rules, the spatio-temporal behaviour is computable but not predictable The behavior of the system is irreversible Similarity of rules does not imply similarity of patterns Simple rules generate complex spatio- temporal behavior For non-trivial rules, the spatio-temporal behaviour is computable but not predictable The behavior of the system is irreversible Similarity of rules does not imply similarity of patterns

27. Aspects of CA morphogenesis complex relationship between „genotype“ and „phenotype effects of „genes“ are not localizable in specific phenes (pleiotropy) epistasisphenes cannot be traced back to specific single genes (epistasis) phenetic effects of "mutations" are not predictable

28. Meinhardt, H. (1995). The Algorithmic Beauty of Sea Shells. Berlin: Springer

29. Patterns and morphology Pattern: a spatially and/or temporally ordered distribution of a physical or chemical parameter Pattern formation Form (Size and Shape) Morphogenesis: The spatiotemporal processes by which an organism changes is size (growth) and shape (development)

30. Where is the phene? Typification? Comparative measures? –length –density –fractal dimension Spatio-temporal development? phenotype A phenotype B