Online Artificial Gene Design

massivefoot writes to tell us New Scientist is reporting that researchers at Johns Hopkins University School of Medicine have released a new software suite, GeneDesign, that helps to simplify the steps in designing artificial DNA. From the article: "These key steps include translating proteins and amino acids - the building blocks which make proteins - backwards into a DNA sequence. Or the software can manipulate simulated DNA "codons" which can code for an amino acid. DNA codons are made of sets of three nucleotides - the fundamental molecules which link together to form a DNA chain."

Comments

What is the story here?

[Anonymous Coward]

What exactly is the exciting news here? This type of software has been around for many, many years. Analyzing a gene sequence to determine restriction enzyme sites, or optimizing codon usage for efficient heterologous expression is absolutely routine, and is performed even in undergraduate level molecular biology courses. It's laughable that the ability of this software to "...manipulate simulated DNA 'codons' which can code for an amino acid" is being touted as an advance.

I can't even believe that New Scientist is reporting this, let alone Slashdot. There must be at least 100 other tools which perform the same functions, many of which are free (both as in beer and source code).

Re:Trivial...

[Daniel Dvorkin]

The underlying science is pretty trivial, yeah. (Or at least "well-understood.") But having this tool in one place, as a reasonably well-designed Web app, is neat.

On to the bigger question ... I think the real thing that bothers me is, why is the biology field so devoid of computer people?!

Stereotyping here -- it's a bit of a culture clash. Until fairly recently, biology (with exceptions for some subfields such as ecology) was, to put it bluntly, the science you went into if you wanted to do science but weren't very good at math. And I think it's fair to say that most "wet-lab" biologists still think more qualitatively than quantitatively. They're very, very good at describing things; they're not so hot at putting those descriptions into numeric or algorithmic terms. And, still stereotyping, CS people tend to be exactly the opposite: "if you can't code it, it doesn't exist," and they're uncomfortable with the inherent, um, gooiness of living systems.

Computers are always supposed to behave predictably. Living things never do. It's really that simple.

You also have the opposite problem, overenthusiasm, which is born out of the same kind of ignorance: biologists who think that they can throw a bunch of random microarray or PCR data at someone's analysis algorithm and get The Answer, and computer scientists and mathematicians who take Bio 101 and think they know enough biology to interpret the answers they get. In both cases, of course, both sides are severly underestimating the complexity of The Other Guy's chunk of the problem.

Don't get me wrong; I do think it's getting better. But even someone like me, who's had one foot in each camp for a number of years now, has to admit that we've got a long way to go before quantitative biology really exists as a unified field.