Overview of Computational Biology

woyouwenti writes "Nature is doing an overview on computational biology. Fascinating stuff if you haven't seen before. I especially like the overview by David Searls including my favorite new science category - "Protein Linguistics"..."

As ironic as it is

[TripleA]

The one invention that has brought man as far away from nature as possible, is now helping us understanding it better.

Re:As ironic as it is

[guybarr]

The one invention that has brought man as far away from nature as possible, is now helping us understanding it better.

math and computation is an integral part of nature.

Fundemental particles are manifestations of group theory.
Information is an integral part of statistical physics
and has measureable physical properties.

computations and math are not disjoint or far from nature, they are part
of it's very essence. It's just that it took man some time to
figure that out.

This is the Future of Biology

[Salis]

Finally, the amount of quantitative experimental data and the rate of its acquisition can support a a full strength thrust into the accurate simulation of cell processes, from first principles of reaction kinetics and structural dynamics.

If you like biology, but also enjoy mathematics, I suggest you explore this area of research. To gain understanding of the fundamentals, the best route through an undergraduate degree would be either chemical, mechanical, or electrical/computer engineering. Chemical, if you're interested in the metabolic networks inside cells, protein folding, protein interactions, or cell process regulation. Mechanical, if you're interested in mostly structural dynamics and the use of artificial or grown materials to be used inside a biological setting (biomaterials). Electrical/Computer, if you're more interested in the data mining/bioinformatics aspect of the research, where analyzing huge sets of data and applying revealing search algorithms is your piece of cake.

Notice how I left out biology, biochemistry, genetics, and microbiology, the three former maintstays of the biological sciences. If you want to get involved in simulations or anything remotely quantitative, engineering is the way to go. You will never learn the necessary mathematics as a straight biological science major. You have been warned!

Btw, I'm a chemical engineering graduate student who is currently heading in this very direction of research.

First phase: Stochastic simulations of cell processes!

Last phase: Complete simulation of structural dynamics, transcription/translation regulation, dna structure dynamics, signal transduction, cell-cell interactions, extra-cellular matrix formation and dynamics, tissue and organ functionality, developemental dynamics, and complete simulation of protein folding (on the fly, hehe).

Time in between phases: 40? years. :)

Hsalis

Any intelligent questions, feel free to email!

The new statistics?

[EnlightenmentFan]

About 40 years ago, one of the things a university's statistics department "did" was help analyze statistical data for other scientists whose experiments yielded some. Now, thanks to easy computer programs, researchers (or their grad students) just type their data into the little blanks and --bingo!--statistical analysis! Probably pretty good statistical analysis too, in about 9 out of 10 papers, and wouldn't it be nice if we knew which was #10.

I believe a lot of statistics departments got downsized once computer programs could do the stuff they were doing.

I foresee the same process with computational biology. That is, biologists trained in nuances of living systems don't have a clue how to plug data into computers for simulation or whatever. There is a natural opportunity for computational specialists to get involved analyzing some interesting stuff. The question is, how long does the stuff stay interesting?

For example, modeling fluid dynamics inside a cell is fascinating the first time somebody does it. After a few iterations, it's cut and dried to plug in appropriate values for viscosity, boundary conditions, etc. Cells won't change much in the next 40 years.

I think ecological models will stay interesting the longest, if only because this beautiful planet of ours creates so much variety in those initial values and boundary conditions.

Re:The new statistics?

[EschewObfuscation]

I have to disagree with you. The approach taken by computational biologists, at least the ones leaning more towards simulation than data crunching (i.e., mining genome databases) is closer to theoretical biology than computer science in some ways. Computers are to biology what mathematics is to physics (to qoute Harold Morowitz) - a tool for analysis and for manipulation of theoretical models.