Can we Verify an Elephant?

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. Unexplained demos 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. BiologyasReactivity 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

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 comprehensiveThat is, do the whole thing...

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

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

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

15. WOP: Whole Organism Project A Grand Challenge for Comprehensive Modeling (H, 2003) To construct a “full”, true-to-all-known-facts, 4-dimensional model of a multi-cellular organism 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. Additional potential gains are enormous • Help uncover gaps, correct errors, form theories and explanations • Suggest new experiments, and help predict unobserved phenomena • Help discover emergent properties • Verifybiological theories against laboratory observations • Pave the way for in silico experimentation, and possibly synthesis, drug construction,…

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

19. 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). Project I (thymus)(with S. Efroni and I. Cohen, ‘03 )

20. The front end

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

22. Straight run Interaction, etc. Pre-recorded demos

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

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

26. Pre-recorded demos Normal growth: Cell count results:

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

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

34. P5 ablated wildtype vulva fate Pre-recorded demos

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 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. A modest step forward: Biocharts(with H. Kugler and A. Larjo, 2009)

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. A Turing-like test for modeling (H’ 2005) 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

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