10 ms·

I understand the principle that models of reality don't need to be 100% accurate to be useful. But... the way MD, CFD or weather models approximate reality see

I understand the principle that models of reality don't need to be 100% accurate to be useful. But... the way MD, CFD or weather models approximate reality seems fundamentally different from how current virtual cell models do it. In MD, CFD or weather models, all of the relevant low-level mechanisms are known, that's why when we want to study a system's reaction to some external shock, we know we will get a roughly correct result with some well-defined error. But in a virtual cell many low-level interactions are not modelled and these interactions can have large effect. Often they are not modelled because they simply haven't been discovered yet. With an accurate cell model we would be able to discover all of those mechanisms, that's where most of the value is. Here's a flawed analogy: Current virtual cell models are like LLMs. They give us permutations of what we already know but they can't discover what we haven't discovered yet.

moralestapia 1y ago
I get your point that models are limited by their own assumptions and can only get you so far. Hmm ... a good way to see one of their benefits is that they help you iterate quickly. Typically, research is done through a design experiment -> measure -> design experiment ... cycle; a new paradigm could be simulate -> design experiment -> measure -> simulate ... the simulation helps you design a much better experiment. You could just try to reproduce what you already know, but by studying how perturbations here and there make or break one specific phenotype, you could infer how the whole system works, and then design an experiment to test a much more narrowly defined hypothesis. An example of that is what I described on a sibling comment wrt. the behavior of plant hormone transporters. Simulations are also helpful to fill in many of these gaps due to lack of information or missing measurements. One technique that has now become fundamental in biochemistry is flux balance analysis [1]. You start from the premise of conservation of matter and, say, if you put in 2 grams of sugar, after some time X, those 2 grams of sugar or their byproducts will still be somewhere in the cell. So, if you know the inputs to a system and perhaps some of the outputs as well, and you somehow know the metabolic network involved, you can perform a simulation that approximates the rate of conversion of all byproducts, tells you which pathways are active or not, and even which ones are missing as well. This particular technique is also interesting because it does not have any actual biological knowledge imbued into it, it's just and abstract representation of "states" in the system and rates of change between time through time (Chat says "Linear Programming applied to a constrained flow network"). The exact same algorithm works in ecology, economy (trade balances), electricity (circuits simulation), transport, etc... And yet, with a very simple setup, you can reconstruct even the most complex metabolic networks, see [2]; and from here, you can use this model to answer things like "which pathway consumes the most energy", "what is the most effective way to maximize the production of X", "where the bottlenecks", "how can I turn off the production of Y, with the least possible changes", etc... without having to perform the actual experiment in wetware. 1: https://en.wikipedia.org/wiki/Flux_balance_analysis https://en.wikipedia.org/wiki/Flux_balance_analysis 2: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-79 https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-...
- RivieraKid 1y ago
  It's interesting and definitely useful. I guess my main point is that there's a massive gap between how exciting and useful the current models are compared to a hypothetical MD simulation. The difference in economic value could be 5 or 6 orders of magnitude.

moralestapia 1y ago

I get your point that models are limited by their own assumptions and can only get you so far. Hmm ... a good way to see one of their benefits is that they help you iterate quickly. Typically, research is done through a design experiment -> measure -> design experiment ... cycle; a new paradigm could be simulate -> design experiment -> measure -> simulate ... the simulation helps you design a much better experiment. You could just try to reproduce what you already know, but by studying how perturbations here and there make or break one specific phenotype, you could infer how the whole system works, and then design an experiment to test a much more narrowly defined hypothesis. An example of that is what I described on a sibling comment wrt. the behavior of plant hormone transporters. Simulations are also helpful to fill in many of these gaps due to lack of information or missing measurements. One technique that has now become fundamental in biochemistry is flux balance analysis [1]. You start from the premise of conservation of matter and, say, if you put in 2 grams of sugar, after some time X, those 2 grams of sugar or their byproducts will still be somewhere in the cell. So, if you know the inputs to a system and perhaps some of the outputs as well, and you somehow know the metabolic network involved, you can perform a simulation that approximates the rate of conversion of all byproducts, tells you which pathways are active or not, and even which ones are missing as well. This particular technique is also interesting because it does not have any actual biological knowledge imbued into it, it's just and abstract representation of "states" in the system and rates of change between time through time (Chat says "Linear Programming applied to a constrained flow network"). The exact same algorithm works in ecology, economy (trade balances), electricity (circuits simulation), transport, etc... And yet, with a very simple setup, you can reconstruct even the most complex metabolic networks, see [2]; and from here, you can use this model to answer things like "which pathway consumes the most energy", "what is the most effective way to maximize the production of X", "where the bottlenecks", "how can I turn off the production of Y, with the least possible changes", etc... without having to perform the actual experiment in wetware. 1: https://en.wikipedia.org/wiki/Flux_balance_analysis https://en.wikipedia.org/wiki/Flux_balance_analysis 2: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-79 https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-...

RivieraKid 1y ago
It's interesting and definitely useful. I guess my main point is that there's a massive gap between how exciting and useful the current models are compared to a hypothetical MD simulation. The difference in economic value could be 5 or 6 orders of magnitude.