Saturday, July 5, 2008

The DNA Network

The DNA Network

After a long delay, the transcript resumes [The Daily Transcript]

Posted: 05 Jul 2008 04:48 PM CDT

Sorry about the incredibly long delay in posts. I've been away. Specifically Brittany, Paris, Bavaria, and Iceland to experience the white nights.

Now I'm back and I'm not travelling for awhile. I'll post pictures as soon as I can.

Read the comments on this post...

Creating artificial DNA [Mary Meets Dolly]

Posted: 05 Jul 2008 03:12 PM CDT


Researchers in Japan have created artificial DNA building blocks that act like the adenine, guanine, cytosine and thymine, we all know and love. From RSC.com:


Japanese researchers have made artificial DNA that acts like the
real thing, even forming right-handed duplexes with complementary
artificial strands. The team, led by Masahiko Inouye at the University
of Toyama, made the new strands from four artificial DNA bases, which
they attached to a sugar backbone using ethyne bonds.

The
researchers hope one day to use the artificial DNA to create a new
biological information storage system that functions in a test tube
rather than the cell. The artificial DNA would be 'restrictively read
out in order to make proteins and eventually to maintain living
organisms,' says Inouye. 'In this sense, we can advantageously use
artificial DNA for information storage instead of natural DNA because
of its stability against ubiquitous naturally occurring enzymes and its
structural diversity.'

Too cool!

Abiogenesis vs evolution. [T Ryan Gregory's column]

Posted: 05 Jul 2008 01:25 PM CDT

At The Panda's Thumb, Nick Matzke has a post about abiogenesis (the origin of life from non-life) and evolution.

Read More...

Genetic testing: the end of private health insurance? [Genetic Future]

Posted: 05 Jul 2008 08:34 AM CDT

From a recent editorial in Nature:
In the future, [privacy] concerns will become more sensitive as genetic testing becomes more predictively powerful. Yet, at the same time, as that era blossoms, it will bring the risk-pooling benefits of universal health-care to the fore. As long as people do not have to share genetic data with private insurers, as is the case in the United Kingdom until at least 2014, those who anticipate bad health will do well to buy insurance cover. The genetically lucky, meanwhile, might as well save money and rely on the state. This will squeeze private insurers, suggesting that the [UK National Health System's] golden period may be yet to come. [my emphasis]
It's not a new argument by any means, but I was a little surprised to hear it in Nature.

Essentially, the argument goes, allowing consumers to engage in genetic testing while simultaneously barring insurance companies from accessing the results will lead to an inequality in the distribution of information: consumers will be able to predict their risk of certain chronic (and expensive) diseases much better than their insurers. As the predictive power of genetic testing increases so will this disparity, until it maxes out at the point where all remaining disease risk is non-genetic in origin.

In countries with a reasonable public health system, such as the UK, Australia or most of Europe, private health insurance is a luxury rather than a necessity - so people with a low genetic risk of chronic illness won't bother joining*. This will then increase the average risk to insurance companies of each new customer, forcing them to raise their premiums, and thus driving the next group of low-disease risk individuals out of the system. Repeat this for a few cycles and the whole system would ultimately collapse, or at least contract, leading to a widespread exodus to the public health system (which the editor somewhat bizarrely describes as a "golden period").

So, will accurate genetic testing, combined with legislation protecting customers from disclosure of genetic information, lead to the downfall of the private health insurance industry? I have too little economic knowledge to even hazard a guess, but this seems to me like something I really should know more about - so I would welcome informed suggestions in the comments.


* Obviously for readers living in countries without effective public health systems (such as Somalia, Tajikistan, and apparently the USA - or so I was told when I suggested that GINA might not be all good) the equation will be different.


Subscribe to Genetic Future.

Man, that was a long blogging break [Mailund on the Internet]

Posted: 05 Jul 2008 02:56 AM CDT

It has been a while since I blogged about anything, and I’m not really writing anything interesting today either.  I’ve been busy with exams and projects and haven’t really had anything to write about.

Now the summer holiday is approaching, and I have a lot of projects I’d like to finish before it, so I don’t know how much time I’ll have for blogging the coming weeks either.

Science is dead! Long live Science! [Bayblab]

Posted: 04 Jul 2008 11:23 PM CDT

Or so claims Chris Anderson, editor-in-chief at WIRED Magazine. In a piece entitled The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, Anderson argues that hypothesis testing and scientific models are going extinct and and, in this age of ever-increasing computing power, massive amounts of data are everything. In his own words:
Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise. But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete.
The argument, it seems, is one of induction on steroids: with so many data points, creating a model isn't necessary because the data are predictive with computational pattern finding, statistical analyses. "With enough data, the numbers," he writes, "speak for themselves."

To illustrate this point, Anderson uses Google as an example. Google's algorithm doesn't care why one page is higher ranked than another, all that matters is that the math says it is. This, of course, is a red herring. Google's algorithm is the model and their continued dominance in search is the successful test. As a technology company, they probably don't care what makes a page relevant, as long as their model continues to reflect what people are looking for.

A more relevant example used in the article is Craig Venter. Lamenting that our knowledge of biology and biochemistry is becoming too complex to be able to model and predict, Anderson points to Venter's ocean and air sequencing projects as an example of science without hypotheses.
If the words "discover a new species" call to mind Darwin and drawings of finches, you may be stuck in the old way of doing science. Venter can tell you almost nothing about the species he found. He doesn't know what they look like, how they live, or much of anything else about their morphology. He doesn't even have their entire genome. All he has is a statistical blip — a unique sequence that, being unlike any other sequence in the database, must represent a new species.

This sequence may correlate with other sequences that resemble those of species we do know more about. In that case, Venter can make some guesses about the animals — that they convert sunlight into energy in a particular way, or that they descended from a common ancestor. But besides that, he has no better model of this species than Google has of your MySpace page. It's just data. By analyzing it with Google-quality computing resources, though, Venter has advanced biology more than anyone else of his generation.
There's no doubt that Venter has made some major contributions to biology (and while he's pretty badass, I don't know if anybody considers him the greatest scientist who ever lived, as some do Darwin), but is his work really done without the scientific method? Of course not. Whatever genomes he sequences will be aligned and annotated, gene functions will be hypothesized all based on current theory. The massive amounts of data can be used to test evolutionary hypotheses; they can be used to generate hypotheses.

Imagine in the future, with all this wealth of personal genomic information available, somebody did the kind of pattern finding and statistical analyses the WIRED piece suggests, and finds a novel mutation that is the cause of some disease. This tells us nothing about human biology or the etiolgy of the disease. This doesn't suggest intervention or treatment. Without the scientific method - hypothesis forming and testing - this is little more than trivia.

Science is a way of knowing; a way of exploring our world and learning about it. It's the way we test ideas, answer questions and advance technologies. Chris Anderson seems to see it as bookkeeping; simply cataloguing observations. He's certainly correct that the 'Petabyte Age' offers "huge amounts of data, along with the statistical tools to crunch these numbers" and this will undoubtedly be a powerful tool and invaluable resource. To say it marks the end of the scientific method is absurd. If anything, the vastness of data will provide new observations and new ideas, which is the beginning of the process, not the end. The rumours of its death have been greatly exaggerated.

UPDATE: Good Math, Bad Math writes about the WIRED piece and large scale data analysis.
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