1. Biological modelling and validation with FLAME Mike Holcombe University of Sheffield

2. How we are making new biological discoveries using systems biology Through intensive collaborations with experimental biologists Emphasis on very detailed and ‘correct’ agent definitions – the biologists may need to do new experiments Taking account of geometry and location and physical forces – this is vital Recognising the diversity of natural systems – not all cells are the same Validating model predictions through new experiments

3. 3 A complex system

4. 4 Pharoah’s ants We are studying trail formation in Pharoah’s ants (M.pharaonis). Observations have identified “trail-laying behaviours” This is used to indicate to others where sources of food is. The seven trail pheromones in Pharaoh’s ants are synthesised by the Dufour’s gland/poison apparatus. The volatile component is very short-lived but the other components are very persistent A model based on rules derived from extensive observation in the lab was built.

6. 6 Trail geometry Branching networks of pheromone trails

7. 7 New discovery -Trail Geometry The bifurcation angle is very regular - about 60°. This tells the ants which way to go The pheromone has no directional information so how does it work? A simple rule such as: If fed then: take the easy route; if there are 2 easy routes turn round will get them home. Jackson et al Nature, vol. 432: 2004. Robinson et al Nature Vol 438, 2005 Jackson et al ANIMAL BEHAVIOUR 71: 2006

8. 8 Ants foraging randomly and with long lived pheromone trail (Bicak)

9. Simulation of a Pharoah’s Ant colony using a supercomputer

10. 10 A simple chemical reaction A simple reaction: Two chemicals -A (blue) plus B (yellow) combine to make C (green) Pogson

11. 11 A more complex molecular example: part of the human immune system Innate immune system - deals with basic infections and inflamation Adaptive immune system - learns from exposure to diseases - bacteria, virus, etc. – Basis of vaccination Very complex systems - still not fully understood

12. 12 Model basics Each NF-  B, I  B and I  B-kinase (IKK) molecule is an individual agent, As are the importing and exporting nuclear receptors and the interleukin-1 (IL-1) toll like receptors. The agents are all contained within a spherical cell consisting of a cytoplasm and concentric nucleus

14. 14 New discovery There had been some evidence that the ratio of I  Bα to NF  B was 3 times what was ‘needed’ Where was all this excess I  Bα? The model predicted that if it was sequestered with the actin filaments this would explain where it was We can track every molecule at all times and thus model the full pathway in detail Recent experiments have produced very significant data that confirms this Pogson et al PLOSOne 3(6): (2008) Immunoprecipitation of I  B  and secondary actin immunoprecipitation

15. 15 Epithelial tissue - skin and urotheliome Mac Neil

16. 16 Mac Neil

17. 17 Different types of cells Stem cells Transit amplifying cells Differentiating cells Fibroblasts Keratinocytes Corneocytes Basic cell cycle

18. 18 T. Sun, P. McMinn, J. Southgate, DWalker Wound healing Why do some wounds heal and others do not? Each cell is an individual and yet some will start to divide and close up the wound. What is organising this? How can we find out what goes wrong when it doesn’t work?

19. 19 Skin healing – stem cells are blue - 3d model McMinn et al Sun et al Fibroblasts and keratinocytes self-organising

20. 20 Functions of TGF-β1 During Epidermal Wound Healing Sun et al Healed virtual epidermis - the stratified cells with relatively high expression level of TGF-β1 were labelled with yellow(A), In the integrated model different colours were used to represent keratinocyte stem cells (blue), TA cells (light green), committed cells (dark green), corneocytes (brown), provisional matrix (dark red), secondary matrix (Green), BM tile agent (light purple). Some of the cells with relative low expression level TGF-β1 were also illustrated In virtuo investigation of the influence of TGF-β1 on epidermal wound healing at subcellular level. The virtual wound with normal proliferation and migration rates were simulated for (A) 0, (B) 200, (C) 400 and (D) 800 iterations. The cells with high TGF-β1 expression levels were labelled with yellow.

21. 21 So what is FLAME? It is based on representing each agent as a general computational model - the X-machine (Eilenberg 1974) The agents communicate using messages and message boards Agents are specified using XMML Filters and message board libraries ensure concurrent efficiency Complete models are automatically generated in C from the specifications

22. 22 Hierarchical modelling FLAME framework Molecular agent-based model Cellular agent-based model call return Internal solver External solver

23. 23 COPASI (COmplex PAthway Simulator) can be called as a function within the agents FLAME framework Salem Adra

24. 24 FLAME Block Diagram X parser files Xparser.exe Model.xml Functions.c 1-N Xml files 1-N Xml files Main.exe 0.xml make Libmboard Your files Xparser files

25. 25 Output analysis FLAME produces a vast amount of data We will need to use data mining and information extraction technology to fully exploit this DAIKON – Dynamic invariant detector http://pag.csail.mit.edu/daikon/ http://pag.csail.mit.edu/daikon/ http://pag.csail.mit.edu/daikon/ This uses machine learning techniques to identify properties that hold in thousands of simulation runs.

26. 26 keratinocyte0:::OBJECT z == motility x <= 500.0 x >= 0.0 y != 0 y <= 467.957706 y >= 27.36479 z == 0.0 force_x <= 0.288605 force_x >= -0.30796 force_y <= 0.312008 force_y >= -0.311693 force_z != 0 force_z = -0.399635 num_xy_bonds = 0 num_z_bonds <= 8 num_z_bonds >= 0 num_stem_bonds <= 10 num_stem_bonds >= 0 cycle != 0 cycle = 1 diff_noise_factor != 0 distance_travelled <= 840.52939 distance_travelled >= 0.0 x >= z x >= force_x x != force_y x > force_z x != diff_noise_factor x >= motility x != dir x != distance_travelled (distance_travelled == 0) ==> (force_x == 0) (num_xy_bonds == 0) ==> (num_z_bonds == 0) (num_xy_bonds == 0) ==> (num_stem_bonds == 0) num_xy_bonds >= num_stem_bonds N. Walkinshaw, P. McMinn

27. 27 Conclusions Agent–based modelling provides a different insight into many types of complex systems It can help uncover what may be going on ‘internally’ It is complementary to traditional modelling approaches The structured way these models are built aids understanding Models can easily be extended by combining several agent-based models together and by introducing new types of agents FLAME - Flexible Large-scale Agent-based Modelling Environment http://www.flame.ac.uk

28. 28 Acknowledgements Rod Smallwood Sheila Mac Neil Salem Adri Des Ryan Francis Ratnieks Eva Qwarnstrom Dawn Walker Simon Coakley Duncan Jackson Elva Robinson Mark Pogson Mariam Kiran Rob Poole Jeff Green Petros Kefalas Mesude Bicak Mark Birkett Phil McMinn Susheel Varma Sun Tao Chris Thompson, John Karn, Stephen Wood (IWP) Neil Walkinshaw Phil McMinn Jenny Southgate (York) Chris Greenough (RAL) David Worth (RAL) Shawn Chin (RAL) Hubert Dravid Michael Neugart Silvano Cincotti Afsaneh Maleki-Dizaj And many more IBM