31–37 of 37 posts
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To build the cell simulator you would have to model all the biochemical pathways in a cell. This is a bit of a problem http://biochemical-pathways.com/#/map/1 http://biochemical-pathways.com/#/map/2
Thanks, cool links. I didn't even know you can use Leaflet for something else than just maps.
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> Anybody knows why we cant just write a cell simulator This is hard. To my knowledge we can't even write most basic components of a cell simulator yet. One of the obvious requirements would be a protein folding simulator. Nobody has been able to come up with a working one of those yet. > when I have a simulator I can try changing them and see what happens? If you write a simulator that does this you will be a billio…
Yep. I recently met a team working on this stuff at the Allen Institute for Cell Science. The problem space is huge and the field is totally in its infancy. If anyone is interested in this work, check their job site. Last I heard they were looking to hire a programmer to help write the sim. Cool team + science.
It was funny to notice that they seem to value a Bachelor's degree at a rate of higher than 1:1 with career experience:
> 4-5 years of experience in a software development team AND a bachelor’s degree in computer science or a related field | OR 15+ years of relevant working experience...
> 6-9 years of experience in a software development team AND a bachelor’s degree in computer science or a related field | OR 20+ years of relevant working experience
That is a pretty heavy premium to put on those years during undergrad. I certainly wasn't as good by undergrad + 5 years as at 15+ years in the field, but maybe they know better than me.
> Learning the DNA regulatory code of the genome This is so interesting. I cannot imagine what kind of language evolution chose to build on top of DNA. There must be some paradigm it maps to, and it's going to be incredibly interesting to see if a compiler/interpreter can be made for DNA, along with higher level languages that compile down to it.
If you’re interested, much of what you’re asking about is actually known: (some) DNA sequences map directly to protein sequences. Because DNA has a 4 letter alphabet (G, C, T, A), it takes three to map to one of the 2x different amino acids that make up proteins. Proteins, in turn, are the “machines” that work in cells. DNA also contains regulatory segments, errors, “dead” code left over (and carried through generati…
Not only that, but it's also (quite literally) radiation hardened: the genome has evolved to a state where most changes to the DNA do very little. Or, if the environment is changing frequently, it'll have evolved to a place in the genome space where it has a higher chance to obtain more beneficial mutations (for more info on this sort of stuff, look up articles by Hogeweg, Colizzi or Crombach).
As a computational biologist, I can tell you that evolution works in many ways, but most likely not in the way you'd expect it to.
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If you’re interested, much of what you’re asking about is actually known: (some) DNA sequences map directly to protein sequences. Because DNA has a 4 letter alphabet (G, C, T, A), it takes three to map to one of the 2x different amino acids that make up proteins. Proteins, in turn, are the “machines” that work in cells. DNA also contains regulatory segments, errors, “dead” code left over (and carried through generati…
I've stopped recommending Alberts. It's a great cartoon guide to a mythical average eukaryotic cell, but it abstracts much farther than the data can bear and leaves the reader without the intellectual tools to work with the material in it. And so you get computer scientists thinking about assembly language and compiling and physicists building little stochastic models of state transitions without knowing the biologic…
Earlier quoted context omitted.
I've stopped recommending Alberts. It's a great cartoon guide to a mythical average eukaryotic cell, but it abstracts much farther than the data can bear and leaves the reader without the intellectual tools to work with the material in it. And so you get computer scientists thinking about assembly language and compiling and physicists building little stochastic models of state transitions without knowing the biologic…
I disagree, it's a decent overview that introduces the major concepts and how they work. It's not intended to be at the cutting edge or to provide all caveats. By definition all textbooks in biochemistry, biology and the like are out of date by the time they go to press. The average MBOC provides is enough of a basic understanding of molecular biology to move on to more advanced work including papers that start to ge…
If of interest, other notes are available at cs522.stanford.edu and more will be available shortly!
Yeah, definitely interested ;) Any chance the course has room for the other side of the coin? Namely, how neuroevolution and genetic strategies inform deep reinforcement learning? Am also interested in learning about the state-of-the-art in cloud based packages. I noticed recently Google released a tool called DeepVariant for use on their genomics platform. https://github.com/google/deepvariant Creating a universal S…
Genetic algorithms and the like are pretty much all terrible. They're ways of approximating your gradient, and fall to the curse of dimensionality. The only reason Uber and open.ai published their papers on evolutionary strategies (something pretty different from what people think of as genetic algorithms) is that current policy gradient methods are really bad as well, allowing what is effectively random search to do well.
It's kinda like how Bayesian hyperparameter optimization is pretty terrible and 2x random search almost always beats it easily.