It’s a panel on how NKS applies to medicine.
First: “Challenges to Conceptualizing Biological Systems: Wobble, redundancy and the unpredictable,” by Elaine Bearer, Brown U. We have conceptual problems incorporating wobble, redundancy and the unpredictable.
The evolutionary hypothesis says that biological systems raise through the process of selection which ignores details. It operates on the results, so there may be multiple ways to get to the same outcome. There’s a ton of evidence that supports the idea that it’s the outcome that counts, including wobble in DNA-protein interactions. Also, many functions are redundant. Wobble means that more than one triplet can spcify the same amino acide; the third nucleotide can vary. E.g., methionine is coded for by ATG but also by ATT and ATC. There’s no 1:1 relationship between the nucleotide code and the protein. Also, transcription factor-DNA interactions have no code. [No idea what that last sentence means.] There are many combinations that will work.
There’s also redundancy: more than one protein or copy of a gene can have the same outome. E.g., the ability to change cell positions is crucial and there are over 50 proteins that take its cytoskeleton structure apart. She shows an amazing video of a platelet taking apart its cytoskeleton and puting it together again like an earthworks mound around the center of the cell in order to form a clot. She’s discsovered which proteins enable this. She used Mathematica to simulate how the protein spreads. [This isn’t a CA thing but more of an example of the power of Mathematica.]
She points to differences in conceptualization that get in the way of a fruitful conversation among biologists and mathematicians. For example, for her randomness is easy: it’s death. Life is randomness harnessed into regularity and repeatability. [I think she’s getting at the difference between randomness and complexity that occasionally confused me in NKS.]
Ilan “Lanny” Kirsch, chief of the genetics branch of the Center for Cancer Researcfh at the National Cancer Institute in Bethesda. He’s going to talk more generally.
Cancer is a genetic disease caused by genetic instability. The genome of a cancer is the not the same as the genome of the normal cell from which it arose; the DNA in a tumor is different that of the cell that gave rise to it. The change in DNA that is cancer is caused by interitance, encironment and randomness. So how does NKS modeling work? We’ll look at three general examples.
1. Pathways and systems. Define the initial conditions annd the rules that descrfibe the pathway. [He doesn’t explain what pathways he’s talking about.]
2. Sequential steps in carcinogenesis. Usually it’s not a single gene that goes but the alteration of sequential genes that causes cancer. NKS can define the sequential rule/condition changes lead to the coutme of malignant transformation.
3. Undersanding and modeling instability. This one is more problematic. It moeans modeling instability itself, studying the basis of change. Wolfram’s example of mutation (p. 321? 391?) is a very good starting point. In the example, there is a mutation of a rule (i.e., if the left and right block is black, the middle block turns white instead of black, or whatever). The sort of randomness he sees in genes look very much like the randomness Wolfram shows and seem to be capable of being modeled by CA. Perhaps we’re seeing the collision of CA. Are mutations the result of the intersection of programs each with its own rule and initial conditions?
Wolfram: What sort of questions should pure NKS investigators concentrae on that would help biologists? In morphological studies, what are the appropriate rules? What are the primitives? Network based systems? Mobile automata? And what’s the appropriate level for trying to do modeling? What sort of questions would you like to be able to answer about these questions?
Kirsch: [Didn’t undestand a word. Too much medical jargon for me.]
Bearer: Computational biological models have been held up by the belief that you need to have everything in place. But NKS may help us figure out what the missing factors are. When we were modelling, the Mathematica guy said that there has to be an inhibitor at a particular position because otherwise the model says that the branching would be other than it is. [Impressive.]