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I'm curious about your thoughts (if you have any) on the boolean modeling formalism. Basically, you represent bio-molecules as having two states: active and ina

I'm curious about your thoughts (if you have any) on the boolean modeling formalism. Basically, you represent bio-molecules as having two states: active and inactive. Their states change according to boolean logic update rules that are determined by the state of other molecules in the system. You end up with a very simple dynamical system. Theoretical biologists have been working with Boolean models for >15 years [1]. Boolean circuits also have some pretty deep connections to theoretical computer science [2]. The goal of this very simple formalism is to get the structure of a system, while retaining much of the quantitative behavior. How productive this is depends on your perspective, I guess. Also, that's a pretty uncharitable view of systems biology. It seems clear to me that understanding even moderately complex phenotypes practically requires models of biology that include many molecules, with significant feedback loops. Further we see emergent biological behavior across multiple scales of time and space, from milli-second long protein-protein interactions to multi-year developmental processes. Systems biology is basically just studying biology while taking all of that into account. That seems worth studying to me, especially given the ineffectiveness of our current therapies in managing most diseases. So, I'm interested in what parts of systems biology you would describe as "smoke and mirrors". [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388622/pdf/nihms-1011828.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388622/pdf/nih... [2] https://www.quantamagazine.org/mathematician-solves-computer-science-conjecture-in-two-pages-20190725/ https://www.quantamagazine.org/mathematician-solves-computer...

twic 6y ago
It's definitely not a charitable view of systems biology, i admit. I furiously agree that understanding living things requires including many molecules, feedback loops, multiple scales, all that stuff (and people always forget to include physical forces!). What i'm so far unconvinced of is that formal modelling of that is more useful than just thinking about it in the usual way. The acid test of a formal model, for me, is the ability to make insights or reliable predictions that are useful, and that couldn't be done without the formal model. Do models actually do that? That fly wing paper is nice - i remember reading that von Dassow et al paper from 2000 when i was an undergraduate! It's a really satisfying read (for me, a skim read right now, i confess). But what does it tell us about the fly's wing that we didn't know already? Don't mistake knowledge of the model for knowledge of the thing. The bit about steady states doesn't really seem physiologically relevant to anything. At some point i should add a disclaimer that i got out of cell biology over a decade ago, and was only ever a bench scientist. These are strictly the opinions of an ill-informed amateur, What this reminds me of more than anything is category theory. Category theory is a set of formal tools for modelling all sorts of things that are interesting to programmers and computer scientists (and beyond!), and when you make those models, they have an enormous intuitive appeal - you look at the model and think yes, that is the essence of what this thing is! The models kick you right in the brain in the same way that those boolean network models do. You can push arrows around and make the models recapitulate the behaviour of the real thing. But that's as far as it goes - the models describe, but in practice, they don't explain or predict in any useful way. But perhaps your point about productivity depending on perspective is the key. We haven't traditionally had models like these in biology. So perhaps it's just that we don't recognize them as knowledge, because they don't look like the kind of knowledge we had before? I don't know. I'm even more out of practice at metaphysics than cell biology.

twic 6y ago

It's definitely not a charitable view of systems biology, i admit. I furiously agree that understanding living things requires including many molecules, feedback loops, multiple scales, all that stuff (and people always forget to include physical forces!). What i'm so far unconvinced of is that formal modelling of that is more useful than just thinking about it in the usual way. The acid test of a formal model, for me, is the ability to make insights or reliable predictions that are useful, and that couldn't be done without the formal model. Do models actually do that? That fly wing paper is nice - i remember reading that von Dassow et al paper from 2000 when i was an undergraduate! It's a really satisfying read (for me, a skim read right now, i confess). But what does it tell us about the fly's wing that we didn't know already? Don't mistake knowledge of the model for knowledge of the thing. The bit about steady states doesn't really seem physiologically relevant to anything. At some point i should add a disclaimer that i got out of cell biology over a decade ago, and was only ever a bench scientist. These are strictly the opinions of an ill-informed amateur, What this reminds me of more than anything is category theory. Category theory is a set of formal tools for modelling all sorts of things that are interesting to programmers and computer scientists (and beyond!), and when you make those models, they have an enormous intuitive appeal - you look at the model and think yes, that is the essence of what this thing is! The models kick you right in the brain in the same way that those boolean network models do. You can push arrows around and make the models recapitulate the behaviour of the real thing. But that's as far as it goes - the models describe, but in practice, they don't explain or predict in any useful way. But perhaps your point about productivity depending on perspective is the key. We haven't traditionally had models like these in biology. So perhaps it's just that we don't recognize them as knowledge, because they don't look like the kind of knowledge we had before? I don't know. I'm even more out of practice at metaphysics than cell biology.