3 ms·

There are currently 251131639 sequenced proteins in UniProt[^1], so, that's a very lower bound on the number of things a modern biologist has to amuse themselv

by dsign 3y ago

There are currently 251131639 sequenced proteins in UniProt[^1], so, that's a very lower bound on the number of things a modern biologist has to amuse themselves with. Many still consider biology as the study of each individual biological organism, system, or protein. But since there are so many of those, I argue that biology must become a science of methods of understanding, and not a science of bare understanding. It's the difference between a company that produces mining machinery and a company that sends miners with pick and shovel underground. And that transformation is going to require for biologists to become system scientists and engineers, steeped to the brim in math, biochemistry and computer sciences. [^1]: https://www.ebi.ac.uk/uniprot/TrEMBLstats https://www.ebi.ac.uk/uniprot/TrEMBLstats

deleted 3y ago
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RandomLensman 3y ago
There is an element to this, but at the same time things tend to get incredibly messy at the level of whole complex organisms. (Btw., that also holds for mining/geology: you can do a lot of complex measurements from the surface, drill some holes and still be wrong about what you encounter underground).
DrScientist 3y ago
Why is there any science at all above pure maths? Why isn't physics, chemistry, geology etc etc just maths? That's because choosing the right level of abstraction is really important for making practical progress. For example penicillin was discovered and used to save millions of lives without any rigorous mathematical understanding of how the drug interacts with it's target. I'm not saying maths isn't incredibly useful and increasingly important in the study of biology, I'm just saying that approaches that don't need maths ( beyond simple counting et al ) are also very important as well - biology is so complex, it's too easy to get bogged down in the detail. Also I do wonder sometimes whether mathematicians don't actually understand some of the maths they work on - they can follow the mathematical logic but can't "see it". ie then find their way through the logic maze by following a logical thread in the darkness - better than stumbling around randomly - but it doesn't mean you understand the maze - and because they don't understand it beyond the 'following the logical thread' they can't communicate it to others. Perhaps the latter is unfair - I'm not a mathematician - I'd be interested to hear other views on that.
- dsign 3y ago
  My point was that there is only so much we can understand. Let me give you a concrete example, one which I have chosen to be easy to understand--the irony!-- : You are a biologist and are given the task of reverting skin senescence in a billionaire client of yours. I'm choosing this example because senescence is a very individual process, with different biological pumps[^1] stopping at different points in time and for different reasons. You can choose to understand how the processes worked together to produce the present system state and skin condition. But that's not your task, your task is to revert it. Understanding seems like a logical first step, but along the way, you (always) discover that these processes involve tens of thousands of interactions between an order of magnitude more of metabolites working at different stages and compartments, and that you can't keep a general intuition of them in your mind[^5], other than the very basic "sh*t breaks". But that's okay. You can always put all of it in a database. Then you only need to remember where the database is, and the dozens of different simulations that are interacting with that database. You will also need to understand the organization of the database, and what the simulations are doing, but there are way less of those and they follow human-made ontologies, sometimes they even come with documentation. If you play with those toys correctly, you will come with an individual intervention for your billionaire that you know will be sound, even if you don't have a comprehensive chain of reasoning of why expressing your customer's variation of 3D9S[^2] 7.5% less will help make his skin better[^3]. In any case, this is an area where there is some vigorous debate[^4] right now. This is somewhat similar to how we don't understand the precise effect of a weight amount trillions in a LLM, but we can still architect, build and profit from the LLM. [^1] That's a name I use for clusters of connected pathways, but the distinction is arbitrary and in this case the clusters were created by a graph clustering algorithm. [^2] https://www.rcsb.org/structure/3D9S https://www.rcsb.org/structure/3D9S [^3] If you are thinking that I should have made this example about cancer: the most frequent cause of cancer is cellular senescence. I couldn't muster the cynicism of making an example about the symptom instead of the cause. But most of my colleagues in search of public funding will. Go figure. [^4] https://direct.mit.edu/posc/article-abstract/31/5/594/115643/Dealing-with-Molecular-Complexity-Atomistic?redirectedFrom=fulltext https://direct.mit.edu/posc/article-abstract/31/5/594/115643... [^5] Or, worse, you risk holding to the wrong intuition or understanding. Because we tend to misunderstand complex things much more easily than simple things, you know.

by dsign 3y ago

deleted 3y ago

[deleted]

RandomLensman 3y ago

There is an element to this, but at the same time things tend to get incredibly messy at the level of whole complex organisms. (Btw., that also holds for mining/geology: you can do a lot of complex measurements from the surface, drill some holes and still be wrong about what you encounter underground).

DrScientist 3y ago

Why is there any science at all above pure maths? Why isn't physics, chemistry, geology etc etc just maths? That's because choosing the right level of abstraction is really important for making practical progress. For example penicillin was discovered and used to save millions of lives without any rigorous mathematical understanding of how the drug interacts with it's target. I'm not saying maths isn't incredibly useful and increasingly important in the study of biology, I'm just saying that approaches that don't need maths ( beyond simple counting et al ) are also very important as well - biology is so complex, it's too easy to get bogged down in the detail. Also I do wonder sometimes whether mathematicians don't actually understand some of the maths they work on - they can follow the mathematical logic but can't "see it". ie then find their way through the logic maze by following a logical thread in the darkness - better than stumbling around randomly - but it doesn't mean you understand the maze - and because they don't understand it beyond the 'following the logical thread' they can't communicate it to others. Perhaps the latter is unfair - I'm not a mathematician - I'd be interested to hear other views on that.

dsign 3y ago
My point was that there is only so much we can understand. Let me give you a concrete example, one which I have chosen to be easy to understand--the irony!-- : You are a biologist and are given the task of reverting skin senescence in a billionaire client of yours. I'm choosing this example because senescence is a very individual process, with different biological pumps[^1] stopping at different points in time and for different reasons. You can choose to understand how the processes worked together to produce the present system state and skin condition. But that's not your task, your task is to revert it. Understanding seems like a logical first step, but along the way, you (always) discover that these processes involve tens of thousands of interactions between an order of magnitude more of metabolites working at different stages and compartments, and that you can't keep a general intuition of them in your mind[^5], other than the very basic "sh*t breaks". But that's okay. You can always put all of it in a database. Then you only need to remember where the database is, and the dozens of different simulations that are interacting with that database. You will also need to understand the organization of the database, and what the simulations are doing, but there are way less of those and they follow human-made ontologies, sometimes they even come with documentation. If you play with those toys correctly, you will come with an individual intervention for your billionaire that you know will be sound, even if you don't have a comprehensive chain of reasoning of why expressing your customer's variation of 3D9S[^2] 7.5% less will help make his skin better[^3]. In any case, this is an area where there is some vigorous debate[^4] right now. This is somewhat similar to how we don't understand the precise effect of a weight amount trillions in a LLM, but we can still architect, build and profit from the LLM. [^1] That's a name I use for clusters of connected pathways, but the distinction is arbitrary and in this case the clusters were created by a graph clustering algorithm. [^2] https://www.rcsb.org/structure/3D9S https://www.rcsb.org/structure/3D9S [^3] If you are thinking that I should have made this example about cancer: the most frequent cause of cancer is cellular senescence. I couldn't muster the cynicism of making an example about the symptom instead of the cause. But most of my colleagues in search of public funding will. Go figure. [^4] https://direct.mit.edu/posc/article-abstract/31/5/594/115643/Dealing-with-Molecular-Complexity-Atomistic?redirectedFrom=fulltext https://direct.mit.edu/posc/article-abstract/31/5/594/115643... [^5] Or, worse, you risk holding to the wrong intuition or understanding. Because we tend to misunderstand complex things much more easily than simple things, you know.