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I don’t mean to dissuade you, because simulating biology is a truly fascinating task. That said, what you’re describing would be a monumental task, not exactly a free time side project. Consider protein folding. This occurs millions of times per second in the thirty trillion cells in the human body. We only just now built a machine learning model that can predict some of the conformations of individual proteins. The crazy thing is that protein folding is the easiest problem in biology. Nice clean training data, static high resolution targets. Cell biology is not that. Cells are noisy and constantly changing, and its insanely difficult to even measure what they're doing. Like every time we try to determine how many different types of cells there are, we come up with a different, larger number than before. The reason for this is that all our measurements of the number of proteins, RNA, and other chemicals in cells are bad, like looking through a distorted broken lens. Complicating all this is that biology is is insanely coupled across both spatial and temporal scales. Consider the KRAS protein. Its one of the most commonly mutated genes in cancer. The most common mutations in KRAS amount to about a dozen atoms being out of place in the protein, due to a single base change in DNA. This nanoscale change in a single cell propagates up to tumors that interfere with bulk physiology and result in death. Or, more specifically for the brain, consider huntington’s disease. Theres a section of DNA thats repeated in a gene called huntingtin. If you have less than 36 copies of the repeat, the you're fine. More than 36 results in a brain disease that typically takes 30-50 years to even manifest. So, all that to say that simulating this is insanely hard. Like, you’re trying to write a simulation for a system where we dont know what it’s made of or how its individual parts work. Also, we just empirically know that changing the arrangement of a few atoms can result in massive changes that lay dormant for decades before they suddenly appear. |