1. Can we Verify an Elephant? David Harel The Weizmann Institute of Science

2. Surprisingly many parts of this were influenced by Amir Pnueli In recent years he became very interested in biological modeling, and actively participated in some of the projects

3. Here are some static computerized elephants

4. Computer science is really the science of the dynamic As are certain parts of mathematics

5. So here are some dynamic computerized elephants

6. And now for a really dynamic one

7. Just to get us in the mood ….

8. Why do we do computerized modeling? What and how should we model? What makes models “ valid ”, “ complete ”, and how do we verify this? Such questions become especially acute when we try to model Nature

9. Biological artifacts are really reactive systems (Harel & Pnueli, 1986) on all levels: the molecular and the cellular, and all the way up to organs and full organisms Biology as Reactivity

10. Biological systems can be modeled and analyzed as reactive systems, using languages/tools developed for constructing computerized systems A thesis follows: Put simply: Let ’ s reverse-engineer an elephant rather than engineer an F-15 …

11. What to model? Be comprehensive That is, do the whole thing...

12. But what is the whole thing? (horizontal delineation) An entire cell An entire organ or organism An entire population?

13. On (or up to) what level of detail? (vertical delineation) Inter-cellular Intra-cellular (inter-molecular) Probably also genomic/proteomic Maybe biochemistry & even physics (particles, quantum mechanics, string theory…)??

14. Crucial point: Comprehensive modeling entails capturing everything that is known about the system, and doing everything else any which way…

15. To construct a “ full ”, true-to-all-known- facts, 4-dimensional model of a multi- cellular organism WOP: Whole Organism Project A Grand Challenge for Comprehensive Modeling (H, 2003) Which animal would be a good choice? Later (but it ’ s not an elephant … )

16. Another crucial point (otherwise we’re wasting our time) : The model should make researchers excited, enabling them to observe, analyze and understand the organism ― development and behavior ― in ways not otherwise possible; e.g., to predict

17. Help uncover gaps, correct errors, form theories and explanations Suggest new experiments, and help predict unobserved phenomena Help discover emergent properties Verify biological theories against laboratory observations Pave the way for in silico experimentation, and possibly synthesis, drug construction, … Additional potential gains are enormous

18. How to model? Be realistic That is, make it look good…

19. Project I (thymus) (with S. Efroni and I. Cohen, ‘ 03 ) T-cell (thymocyte) behavior in the thymus. Many cells, complex internal behavior, interaction and geometric movement. Enormous amount of biological knowledge assimilated and modeled (~ 400 papers).

20. The front end

21. Statechart outline for a single T-cell Migration Interaction Receptors Cell phase Receptors decisions Entry to thymus

22. Straight run Interaction, etc.

23. The model reveals emergent properties ( with Efroni and Cohen, ‘ 07 ) Competition change:

24. Project II (pancreas) (with Y. Setty, Y. Dor and I. Cohen; 2007) Embryonic development of the pancreas (very different characteristics). Here we use 3D animation and are interested in organ formation.

26. Cell count results: Normal growth:

28. Wild “ playing ” yielded insights into the role of blood vessel density into organ development Experimental confirmation in progress!

29. Project III (C. elegans) (with N. Kam, M. Stern, J. Hubbard, J. Fisher, H. Kugler, A. Pnueli; 2001−7) Vulval precursor cell (VPC) fate determination in the C. elegans nematode Few cells, lateral and inductive signaling with subtle timing; many mutation-driven variants.

30. C. elegans:

31. Development Behavior

32. Meet the Grand Challenge by modeling the C. elegans nematode Proposal: Or some comparable creature

33. P6.p P7.p ayIs4;e1282;lin-15(n309) P5.p P4.p anchor cell

34. P5 ablated wildtype vulva fate

35. Carry out multi-level modeling, with different abstraction levels modeled with different languages and methods Then combine all to yield a smoothly zoomable & executable model Central CS problem to be solved: Vertical linkage (hierarchy, abstraction and levels)

36. A modest step forward: Biocharts (with H. Kugler and A. Larjo, 2009) A compound, fully executable 2-tier language for modeling biology Upper level captured using Statecharts Lower level captures networks, pathways, etc.; e.g., with semantics-rich biological diagrams.

37. When are we done? Aha! The $64m question…

38. But, … comprehensive modeling is about understanding a whole thing You really and truly understand a thing when you can build an interactive simulation that does exactly what the original thing does on its own. Q: How do you tell when you ’ ve managed to achieve that?

39. A: We want prediction-making taken to the utmost limit; the key to this is to fool an expert. Hence, for comprehensive modeling, I propose a Turing-like test, but with a Popperian twist

40. We are done when a team of biologists, “ well versed ” in the relevant field, won ’ t be able to tell the difference between the model and the real thing A Turing-like test for modeling (H’ 2005)

41. This is not a test for the weak- hearted, or for the impatient … And it’s probably not realizable at all … But as the ultimate mechanism for prediction-confirming, it can serve as a lofty, end-of-the-day, goal for the WOP Grand Challenge

42. Thank you for listening