Earlier quoted context omitted.
This is a course on a fairly novel field of research, not a method to do anything remotely consumer-facing.
I disagree! It's a fantastic domain for startups! Nanopore is readily available. I have sequenced stuff in my kitchen, and I'm a computer scientist.
CS 522: Machine Learning Approaches to Decode the Human Genome
21–30 of 37 posts
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#22Anybody knows why we cant just write a cell simulator and start experimenting that way with DNA manipulation? I mean I have no idea what the first nucleotides are for, but when I have a simulator I can try changing them and see what happens? This may sound like a naive approach, but is there anything special hindering us from building such a simulator or is it just that scientists will not find it useful as it will t…
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#23Anybody knows why we cant just write a cell simulator and start experimenting that way with DNA manipulation? I mean I have no idea what the first nucleotides are for, but when I have a simulator I can try changing them and see what happens? This may sound like a naive approach, but is there anything special hindering us from building such a simulator or is it just that scientists will not find it useful as it will t…
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#24Anybody knows why we cant just write a cell simulator and start experimenting that way with DNA manipulation? I mean I have no idea what the first nucleotides are for, but when I have a simulator I can try changing them and see what happens? This may sound like a naive approach, but is there anything special hindering us from building such a simulator or is it just that scientists will not find it useful as it will t…
> 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…
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.
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#25Anybody knows why we cant just write a cell simulator and start experimenting that way with DNA manipulation? I mean I have no idea what the first nucleotides are for, but when I have a simulator I can try changing them and see what happens? This may sound like a naive approach, but is there anything special hindering us from building such a simulator or is it just that scientists will not find it useful as it will t…
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
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#26Anybody knows why we cant just write a cell simulator and start experimenting that way with DNA manipulation? I mean I have no idea what the first nucleotides are for, but when I have a simulator I can try changing them and see what happens? This may sound like a naive approach, but is there anything special hindering us from building such a simulator or is it just that scientists will not find it useful as it will t…
> 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…
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#27Earlier quoted context omitted.
This is a common idea among computer scientists looking at biology. They see "sequence of base pairs" and immediately think of a Turing machine's tape or a memory segment in a computer. It leads them astray. There's no reason to think that evolution has or would construct anything resembling a language or a language paradigm. Evolution doesn't introduce abstractions. Now, programmers, by our neurophysiology and train…
I have always found this example of using evolutionary processes in FPGAs (and how utterly bizarre the resulting circuitry) to be very useful in clarifying just how different the biological world is from the CS one. https://www.damninteresting.com/on-the-origin-of-circuits/ tl;dr - evolution takes advantage of the entire solution space without any respect the the abstraction layers we've created in our minds.
> after just over 4,000 generations, test system settled upon the best program. When Dr. Thompson played the 1kHz tone, the microchip unfailingly reacted by decreasing its power output to zero volts. When he played the 10kHz tone, the output jumped up to five volts.
> Dr. Thompson peered inside his perfect offspring to gain insight into its methods, but what he found inside was baffling. The plucky chip was utilizing only thirty-seven of its one hundred logic gates, and most of them were arranged in a curious collection of feedback loops. Five individual logic cells were functionally disconnected from the rest— with no pathways that would allow them to influence the output— yet when the researcher disabled any one of them the chip lost its ability to discriminate the tones. Furthermore, the final program did not work reliably when it was loaded onto other FPGAs of the same type.
> It seems that evolution had not merely selected the best code for the task, it had also advocated those programs which took advantage of the electromagnetic quirks of that specific microchip environment. The five separate logic cells were clearly crucial to the chip’s operation, but they were interacting with the main circuitry through some unorthodox method— most likely via the subtle magnetic fields that are created when electrons flow through circuitry, an effect known as magnetic flux.
This nicely illustrates a major advantage of evolutionary processes: they can use any resource in the environment, whether you know that resource exists or not.
The program's crippling overspecialization ("the final program did not work reliably when it was loaded onto other FPGAs of the same type") is also typical of evolutionary processes.
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#28Anybody knows why we cant just write a cell simulator and start experimenting that way with DNA manipulation? I mean I have no idea what the first nucleotides are for, but when I have a simulator I can try changing them and see what happens? This may sound like a naive approach, but is there anything special hindering us from building such a simulator or is it just that scientists will not find it useful as it will t…
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
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#29Anybody knows why we cant just write a cell simulator and start experimenting that way with DNA manipulation? I mean I have no idea what the first nucleotides are for, but when I have a simulator I can try changing them and see what happens? This may sound like a naive approach, but is there anything special hindering us from building such a simulator or is it just that scientists will not find it useful as it will t…
Is there anything better than DFT method for quantum chemistry? You can look it up and see how much effort it takes to simulate just a few molecules.
I'm pretty sure full cell level simulation would revolutionize biology and medicine.
Re: CS 522: Machine Learning Approaches to Decode the Human Genome
#30Earlier quoted context omitted.
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…
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 get into the nitty gritty details. Note that many people haven't been even exposed to the basics!