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Problem is that the models of how parts connect in biology are mostly descriptive. However, try to use only words to describe how a circuit works that powers a
by j7ake 3y ago
Problem is that the models of how parts connect in biology are mostly descriptive.
However, try to use only words to describe how a circuit works that powers a radio, then use those words as a “model” to predict how a radio would work: you’ll end up with vague hand wavy text that poor quantitative and qualitative predictions.
One solution is to use the language of differential equations to explicitly describe in simple but precise language how parts are connected. Although not quantitative in their predictions, at least this “systems” approach makes qualitatively interesting predictions (eg limit cycles, fixed points, bistability).
- wildylion 3y agoAnd here I am, with this thought about how us humans, having an ingrained notion of science and discovery needing to be built on __analyzing__ things, are defied by such complex, interwoven systems that really can't be put into parts. And while I'm totally an armchair scientist here, what comes to mind immediately is that the resources needed to describe such a system analytically would grow exponentially with a very high value of that exponent. I wonder, what a horrendously complex system of differential equations would be needed to describe just one receptor, with its countless inputs and outputs -- and how brilliant in mathematics would a person reading it have to be. Disclaimer: this comes from a university dropout with no background in biology and only a little in mathematics -- and moreso, whose native tongue is not English.
- j7ake 3y agoI think one thing to keep in mind is that coming up with these differential equations is an art. Classically it's about balancing simplicity (for human understanding) and being able to recapitulate some of the dynamics of the system. Receptor dynamics is one subsystem that has been studied well theoretically. We know increasing number of binding sites can generate for example "cooperativity" and bistability dynamics. Here's a review that discusses this further: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146043 https://journals.plos.org/plosone/article?id=10.1371/journal...
- alexpetralia 3y ago> And while I'm totally an armchair scientist here, what comes to mind immediately is that the resources needed to describe such a system analytically would grow exponentially with a very high value of that exponent. Yes, this relates precisely to Stephen Wolfram's notions on "computational irreducibility." For humans, it is an impossible project. For machines, it is perhaps possible, but we humans would not comprehend it.