1. Return to Eden: How biologically relevant can on- lattice models really be?

2. Outline What sorts of on-lattice models are there? What do/can we model on-lattice? Pros. Cons. Two case studies – Position jump modelling of cell migration. – Models for tumour growth.

3. Types of on lattice model Cellular automaton. – Exclusion processes. – Game of life. Cellular Potts model. Lattice gas automaton. – Lattice-Boltzmann. Ising model. Position jump models (on lattice).

4. Cellular automaton Pattern formation. Neural networks. Population biology. Tumour growth. See Ermentrout, G.B. and Edelstein-Keshet, L., Journal of Theoretical Biology 1993

5. Cellular Potts models Immunology Tumour growth Metastasis Developmental biology Cellular Potts Model of single ovarian cancer cell migrating through the mesothelial lining of the peritoneum.

6. Position jump models Development Pattern formation Animal Movement Aggregation

7. Advantages Simple to formulate and adapt. Easy to explain to biologists. Can capture phenomenological details. Mathematically and computationally tractable. Makes multiscale description possible (i.e. can often derive PDEs).

8. Problems with on-lattice models Geometry - Cells aren’t squares! Hard to convince biologists. Changing lattices are difficult to deal with (i.e. how to implement cell birth/death). Inherent anisotropy. Artificial noise effects.

9. What’s best for… …Parallelisation of code? – Can both on-lattice and off-lattice individual- based models be parallelised equally well? …Boundary condition implementation? – Which type of model deals best with curved boundaries for example?

10. Case Study 1: Position jump modelling of cell migration: Movement T+T+ T-T- = A cell

11. Signal Sensing = A cell

12. Some definitions

13. Probability master equation

14. Equivalent to PDE

15. Local Signal Sensing Cell Density Profiles Individual model – Blue histograms. PDE – Red curve.

16. Growth = A cell

17. Exponential Domain Growth Domain Growth PDE – Red. Average stochastic- Green. Individual Stochastic – Black. Cell Density Profiles Individual model – Blue histograms. PDE – Red curve. Domain length – Green star.

18. Density Dependent Domain Growth Domain Growth PDE – Red. Average stochastic- Green. Individual Stochastic – Black. Cell Density Profiles Individual model – Blue histograms. PDE – Red curve. Domain length – Green star.

19. Incremental Domain Growth = A cell

20. Connecting to a PDE In order to connect the PDE with the cell density we had to enforce a Voronoi domain partition. Interval Centred Domain Partition Vornoi Domain Partition

21. Diffusion on the Voronoi domain partition Domain Growth PDE – Red. Average stochastic- Green. Individual Stochastic – Black. Cell Density Profiles Individual model – Blue histograms. PDE – Red curve. Domain length – Green star.

22. Higher Dimensions Local sensing on a 50X50 square lattice PDE solution surface Individual based model – Square grid histogram

23. Triangular Lattice PDE solution surface Individual based model – Traingle grid histogram Diffusion on a triangular lattice

24. Growth in two-dimensions? Circular or square domain to make PDEs tractable. Apical growth? How much can lattice sites push each other out of the way? Can we make on lattice models replicate real biological dynamics, at least qualitatively?

25. Case Study 2: The Eden model

26. The Eden model Produces roughly circular growth (especially for large clusters) Start of with an initial “cell” configuration or a single seed. Square cells are added one at a time to the edges of the cluster in one of three ways:

27. Eden A A cell is added to any of the sites which neighbour the surface equiprobably. # surface neighbouring sites = 12

28. Example Eden A cluster

29. Eden B A cell is added to any of the edges of the surface equiprobably. # surface edges = 14

30. Example Eden B cluster

31. Eden C A surface cell is chosen equiprobably and one of its edges chosen equiprobably to have a cell added to it. # surface cells = 8

32. Example Eden C cluster

33. Real Tumour Slices Images Courtesy of Kasia Bloch (Gray Institute for Radiation Oncology and Biology and the Centre for Mathematical Biology)

34. Important properties Growth rate Morphology Surface thickness Genus (Holiness)

35. Number of holes vs time Eden AEden BEden C All values are averaged over 50 repeats

36. Surface scaling

38. Universality Classes (UC) By finding these coefficients we can classify these models into universality classes. Some well-known universality classes are: Name  Z EW¼½2 KPZ1/3½3/2 MH3/83/24

39. Tumour universality class Brú et al*. found a universality class for tumours. They placed tumours in the MH universality class. *Brú, A.; Albertos, S.; Luis Subiza, J.; Garcia-Asenjo, J. & Brú, I. The universal dynamics of tumor growth Biophys. J., Elsevier, 2003, 85, 2948-2961

40. Eden universality In strip geometry Eden is in KPZ. But not so in radial clusters? Why not?

41. Anisotropy Axial anisotropy cause problems. Eden AEden BEden C The three Eden models average over 50 repeats

42. Anisotropy correction Even model C exhibits a 2% axial anisotropy. But Paiva & Ferreira* have found a way to correct for this. Once corrected and surface thickness determined in the proper way it was found the radial Eden clusters fall into the KPZ UC. *Paiva, L. & Ferreira Jr, S. Universality class of isotropic on-lattice Eden clusters Journal of Physics A: Mathematical and Theoretical, IOP Publishing, 2007,

43. Mitosis Off-lattice Eden model – Ho and Wang*. – Isotropic but no use to us as it’s off lattice. On lattice with limited pushing range – Drasdo**. – Limited range of pushing. – Anisotropic. *Ho, P. & Wang, C. Cluster growth by mitosis Math. Biosci., Elsevier, 1999. ** Drasdo, D. Coarse graining in simulated cell populations Advances in Complex Systems, Singapore: World Scientific, 2005.

44. Adapted mitosis model Division in 8 neighbouring directions. No limit as to how far we can push other cells. Isotropic? Tentative yes. Universality class? Too early to say.

45. Summary Lattice model examples. Pros and cons. Position jump case study. Cluster growth case study. Lattice models can be compared to real-world phenomena (e.g. universality classes, genus). But how realistic are they?

46. Discussion points Will on-lattice models continue to be of use in the future? Will on lattice models ever be as realistic as off-lattice models? Why use a lattice model when an off-lattice model works just as well (and vice versa)? Do lattice models have a role in communicating our modelling ideas to biologists?