Can we Verify an Elephant?. David Harel The Weizmann Institute of Science. 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.
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The Weizmann Institute of Science
In recent years he became very interested in biological modeling, and actively participated in some of the projects
As are certain parts of mathematics
Just to get us in the mood….
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
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
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…
Be comprehensiveThat is, do the whole thing...
An entire organ or organism
An entire population?But what isthe whole thing? (horizontal delineation)
Probably also genomic/proteomic
Maybe biochemistry & even physics (particles, quantum mechanics, string theory…)??On (or up to) what level of detail? (vertical delineation)
Crucial point:Comprehensive modeling entails capturingeverything that is known about the system, and doing everything else any which way…
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…)
(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
Be realisticThat is, make it look good…
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 )
Entry to thymus
The model reveals emergent properties(with Efroni and Cohen, ‘07)
Here we use 3D animation and are interested in organ formation.Project II (pancreas)(with Y. Setty, Y. Dor and I. Cohen; 2007)
Cell count results:
Wild “playing” yielded insights into the role of blood vessel density into organ development
Experimental confirmation in progress!
(with N. Kam, M. Stern, J. Hubbard, J. Fisher, H. Kugler, A. Pnueli; 2001−7)
Meet the Grand Challenge by modeling the C. elegans nematode
Or some comparable creature
Then combine all to yield a smoothly zoomable & executable model
Central CS problem to be solved:
(hierarchy, abstraction and levels)
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)
Aha! The $64m question…
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?
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
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
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