anusinha

Studying organisms like these would certainly teach us a lot about cellular maintenance and extremely slow patterns of gene regulation.

Relatedly, note that storing information in DNA is the longest lived form of information storage. Harddrives tend to deteriorate in ~10 years or so. Flash Memory has a similar life time. Books can last hundreds to thousands of years, but no more, and stones and rocks and fossils, perhaps a few hundreds of millions of years. But genetic information can be maintained for billions of years--it is a discrete information encoding, is easily copyable, and has noise tolerance and error correction. Not very useful (yet!) to store information and data, but very interesting to think about conceptually: humans have been reconstructing in metal and silicon what nature developed billions of years ago. Sort of cool to think about.

> I realize this sort of thing is a little too weird to be a commercial success

Nothing's ever too weird. Ten years ago, people were afraid to put their real name on the internet. Standards will shift, as long as customer/user privacy is maintained.

Note that there are many industries in which passive income is simply impossible. Yes, this is _Hacker_ News, but fields outside of software engineering do exist.

This is not _entirely_ relevant, but I would like to illustrate the complexity and cost of the problem. Many large pharmaceutical companies have completely pulled out of the market for developing drugs in one of the three main classes of diseases that affect most Americans: Neurodegenerative diseases (the other two classes being cancer and cardiovascular disease). This is because the cost of drug development is incredibly expensive.

First, suppose we have a drug candidate. Great. This would take many, many years of study of neurons/brain tissue cultures using in vitro techniques (petri dish, etc. out of the native environment). These are great and relatively cheap, but it's difficult to isolate good drug candidates. Eventually, after high throughput screening or whatever other research method is used, a candidate is found. This would (realistically) take many years and require many millions in funding.

Then, once you have this in vitro drug candidate, you have to start testing it in animal systems, starting with simple mice. Animal studies are expensive, because the organisms are essentially disposable. These sorts of studies for neural problems are even more expensive because the animals need to be observed carefully using various research techniques for very extended periods of time, on the order of months to years (since this is the timescale on which most neural disease work). This is obviously both expensive timewise and monetarily.

So our theoretical candidate works in simple animal and in vitro systems. Great! There a lot of drugs that get this far and then utterly fail on human subjects. Assuming that it passes FDA standards for human testing (which, at this point, seems fairly reasonable), human testing will start. The main purpose of Phase I trials is to determine toxicity of the drug in human systems, and to see if it has any significant effect. Let's say ~40 patients are being tested. These 40 (lucky? unlucky?) patients will take the drug, and then be investigated using a variety of techniques to judge the efficiency. One of the standard techniques in neural imaging is the MRI scan. The typical cost to the company carrying out the test is around $3000+ USD per scan carried out. Each patient will need to be scanned many times (let's say 10) over a period of time to determine whether or not the drug had any affect. Compared to other techniques where simpler blood tests or post-mortem tissue analyses can tell the story, this is very expensive. And this is just phase I. Phases II and III expand the number of patients into the several thousands. The cost of drug development for one potential compound for so long (the timeframe can be order of 15+ years) for a very, very small chance (due to the complexity of the field and the few successes so far) of a huge earning is simply unjustified for many companies. Earning back hundreds of millions of dollars of losses back must be done by passing the cost along to the customer.

Note that the patent lifetime on drugs is very short. Nominally, the compound is protected for 20 years. But this is applied for before any clinical trials start, so the effective lifetime is significantly shorter. For a neurodegenerative disease, the effective lifetime of the patent could very well be much sorter than the more "usual" effective lifetime of 7-12 years [1].

Also, as an extra. Organic synthesis is hard. I mean, really, really hard. You have to design a good synthetic pathway, then make sure it works, then scale it up from working in a fume hood, to working in large chemical reactors that can produce it on an industrial scale. I'd like to point people at the synthesis of Tamiflu, the antiviral used against various influenza strains [2]. These are long pathways and accordingly tend to have lower total yields. Drug synthesis is really hard, regardless of the cost of R&D of the drug.

TL;DR: Finding drug candidates is really difficult. FDA clinical trials is really difficult. Making the drugs themselves is really difficult.

[1]: http://en.wikipedia.org/wiki/Generic_drug [2]: http://en.wikipedia.org/wiki/Oseltamivir_total_synthesis

You have to do the latter. If you just switch without making any noise, then the company just sees you as percentage of users using the competitor's product. Not very meaningful.

You have to write a short letter saying why you once used <product> or refused to purchase <product> because of <reason>.

Will I be carrying a flash drive around in five years? I use my current one (that lives on my keychain) no more than once a week, and that's only to interface with computers that are offline.

How about a system where I hold my phone up to someone else's and speak a semantic description of which files to send and then wait a few seconds. Then, later, when I go home and am near my computer, my phone talks to my computer and automatically uploads the content to my computer/home server, and catalogs/saves it in the correct location.

All of these individual pieces already essentially exist, modulo some of the semantic NLP--nobody's put together the entire package.

I think it's important to note that although biological systems are still well out of our reach, chemical systems (using quantum chemical methods) is being done increasingly better, and we can learn a lot about various systems, ranging from graphene to organic semiconductors to metal-clusters in enzymes using computational methods. So one day, we'll slowly get there, and we shouldn't write it off. Though yes, the current state of the art is hopeless for cancer drug development--but the field is dynamically changing.

To be fair, Gaussian is orders of magnitude worse. See <http://www.bannedbygaussian.org/>.

In any conversation about Markdown, I think it's appropriate to mention Textile [1] as well. Textile is fairly similar, intended to fulfill the same niche of markup with slightly different syntax. Personally, I prefer textile because I think it's a bit more expressive, though I'd rather be writing LaTeX...

[1]: http://en.wikipedia.org/wiki/Textile_%28markup_language%29

Relatedly, there are powers in the pharmaceutical industry that would love to be able to carry out syntheses in space. The lack of an effective gravity force allows crystals to grow larger and this would be a useful property.

It seems as though ASICs are the method of choice for every high performance system. The examples that come to mind are supercomputer interconnects and the completely engineered from scratch Anton[1] machines that DE Shaw Research uses for very specific computational chemistry applications.

[1]: http://en.wikipedia.org/wiki/Anton_%28computer%29

Some thoughts about the coder culture and how it relates to women can be found on this really interesting blog post. http://www.infoworld.com/d/application-development/the-ugly-...

I can't say that I agree with all of it, but it's definitely worth reading to understand certain social and cultural dynamics.

This was a really great article, both using thermodynamics and inorganic chemistry. If you're interested in more theoretical applications of symmetry (or, in its proper mathematical formalism, group theory) to chemical systems, a good starting point is the Wikipedia page: http://en.wikipedia.org/wiki/Molecular_symmetry. Basically, just by using symmetry considerations, you can determine properties about the spectra, etc of various molecules. If you really want to learn about all the nitty-gritty, involving lots of matrix algebra, quantum mechancs, and the whole shebang, you might be interested in the book "Chemical Applications of Group Theory", by F. Albert Cotton.