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
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
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 …
What to model? Be comprehensive That is, do the whole thing...
But what is the whole thing? (horizontal delineation) An entire cell An entire organ or organism An entire population?
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…)??
Crucial point: Comprehensive modeling entails capturing everything that is known about the system, and doing everything else any which way…
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 … )
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
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
How to model? Be realistic That is, make it look good…
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).
The model reveals emergent properties ( with Efroni and Cohen, ‘ 07 ) Competition change:
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.
Wild “ playing ” yielded insights into the role of blood vessel density into organ development Experimental confirmation in progress!
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.
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)
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.
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?
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 A Turing-like test for modeling (H’ 2005)
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