Biology Will Be the Next Great Computing Platform

Biology Will Be the Next Great Computing Platform
wired.com

My suggestion for what to do after the CS degree, a road I took myself during the last few years, is to go to edX (or Khan Academy for any missing basics) and at the very least take MITs "Introduction to Biology", which actually is an introduction to genetics (when you learn biology you first have to understand cells). Also, neuroscience, for which there are many courses (longest one on Coursera, "Medical Neuroscience", with an excellent teacher), from the basics (https://www.mcb80x.org/) to computational neuroscience (https://www.edx.org/course/computational-neuroscience-neuron...).

I found understanding the basics of biology a lot more informative than learning new twists about some functional programming concept. It introduces you to a massively(!) parallel world where statistics rules, and errors/outliers are actually essential to functioning biological systems. You may find that if you remove all errors, for example that a protein is made that is not supposed to be made because it's not supposed to be needed, actually is essential, because only by making it does the cell notice that a different (better) fuel option has become available (that is from a concrete example, see the linked course :-)). So definitely add plenty of statistics courses until you learn to think "big". Each time you think about a problem think about that thing happening a trillion times instead of individual cases, until this becomes a habit. This helped me a lot, because most people focus their mind on single examples, for example when making suggestions how to improve society (hint: suggestions that sure work for anyone, but if everyone tried it would quickly unravel). Okay, in the last two sentences I'm going out on a limb (claiming that learning biology and statistical thinking helped), but definitely try some biology, statistics and neuroscience guys. It's all free and most of it high quality. And the statistics knowledge is just as good for machine learning so you need that anyway.

Also recommended: "Principles of Biochemistry" (https://www.edx.org/course/principles-biochemistry-harvardx-...), although I would say you should not try to do all the exercises because it will be way too much work (and highly demotivating, looking at how the discussion forum shows how the nr. of students very quickly grows thinner from course section to section) to try to learn all that stuff by heart. Also: "Cell Biology: Mitochondria"( https://www.edx.org/course/cell-biology-mitochondria-harvard...) to understand where the power comes from.

Also check out individual universities, some have a lot of free courses (sometimes using the edX platform software, which is open source, e.g. Stanford) hat they don't put on 3rd party platforms like edX.

No it is not. Analog computers were used before good digital computers could be produced, for the same roles and straightforward problems.

Life solves problem at scales that you cannot even touch with "precision" and in completely different ways. The problem is thinking that those systems are supposed to be used instead of computers. I reality, they are to tackle much different problems. Also see my longer comment here. Whenever you have a problem that you can nicely solve with a silicon-based computer you don't need a biological system.

Also don't forget that biological systems are still settings the targets for things like AI or robotics, from which our silicon-based systems still are very far away from reaching. They do that with added features of self-replication and self-healing.

The things described in the article are not biology used for computation, but principles from CS applied to biology which has some validity. In one case I achieved strides in protein design by reducing the engineering cycle from months to days. Imagine hitting "compile" and having to wait a 72 hours to know your result. That's biology, even with a fast organism like yeast or e coli.