Onconomicon

2012-01-20T10:23:00.002-05:00

So researchGate is kind of an interesting idea. As is this this New York Times article on the future of science publishing. I happen to agree that science publishing is due for a major makeover, but I don't think its replacement is out there yet. PLoS One has a lot going for it, as do other open access journals, but they don't bring us closer to the thing I really want to see: real time science publication.

I'm not totally sure that it's a desirable end, since a lot of what gets done in paper writing (careful description of methods, bringing together disparate data into one story, filling in logical holes with supporting experiments) only gets done because of the hurdle of publication. But imagine how many small, interesting, positive results never get published because they don't fit in with such a complete narrative required by a paper. Or, for that matter, the number of negative results.

What if, at the end of the day, everyone just blogged their science. You'd put a blurb about your methods, a blurb about your results, maybe a picture of your gel, sequencing results, graph, whatever, and let it run free on the internet. I can think of 2 or 3 figures that I plan to put into a forthcoming paper that I wish I didn't have to wait to share.

What are the down sides? Well a blog is less permanent than a publication, which gets archived and saved in about a hundred different places. It also is hard to put on your CV. Moreover, do people really want to go to someone's blog to learn what their lab is doing, or would they much prefer a scientific paper, which is sort of a digest version? Finally, does it actually advance science if everyone can know what everyone else is doing at all times? Would people continue to have the drive to publish first to avoid being scooped? Would there be even less reward for replicating past results, since you could in theory check to make sure you were the only one working in your area?

The expanding ecosystem is not without problems, but it's interesting to see what is created in the years ahead.

Well intentioned mediocrity?

2012-01-08T18:17:00.001-05:00

Opposing the bill that started the National Cancer Institute, James Watson, the loose cannon co-discoverer of DNAs structure said:… we must reject the notion that we will be lucky. … Instead we will be witnessing a massive expansion of well-intentioned mediocrity.(credit: The Emperor of all Maladies)Can we say, truthfully, that he was wrong? Or has the cure for cancer been a long road that will soon bear its full fruit?

Flip the Conference

2011-11-18T10:09:00.001-05:00

People who are a fan of the khan academy might know about the concept of "flipping the classroom". The simple idea of this technique is having students watch lecture videos as homework and do problems in the classroom, rather than the reverse.

I wonder whether this same method could be applied to conferences, which often result in long, dull talks followed by interesting questions and discussions. One could imagine recorded talks made available to conference attendees a few days in advance followed by a conference entirely devoted to activities designed to promote interaction between researchers, such as poster sessions, discussion panels, small workshops and even debates. Why spend a day watching a big screen in front of the lecture hall in some convention center when we can just as easily do that in our bathrobe at home?

TCGA Datasplosion

2011-11-17T14:51:00.001-05:00

I'm at the TCGA meeting in Washington DC, and am being blown away by the enormous amount of data and complicated analysis of that data. It's phenomenal how much sequencing, morphometric and related data have been accrued in a wide variety of tumor types. The analyses are pretty interesting, but it's also surprising how much previously known biology comes out again and again. I can't tell if people are reporting results that we already know because it's safer, or because there's not much new in the data. Either way, it's clear that for these results to be useful we will need much more clinically relevant annotation and many more tumors. In a future where all patients have sequenced tumors, perhaps we will see much more clinically applicable conclusions.

A Tripartite Ethic (way off topic)

2011-11-05T09:17:00.001-04:00

It's my personal belief that we probably have never read the work of the most brilliant philosophers. That is, no one has read their work, because they never wasted their time writing it down. The most knowledgeable philosophers were probably smart enough to pursue happiness on their own, outside of the public light, and never felt the need to have their ideas validated by others. The most wise philosophers don't need blogs.

Which all just comes as a preface to say that I write the following post with some hesitation, and that by the very act of committing it to words probably proves that it is wrong. With that preface, here are my thoughts:

It strikes me that we have a very confused conceptions of ethics in this country. I don't know who the culprit is, but I just want to state the problem. The fundamental confusion lies in unifying or scrambling the elements of what are most properly thought of as three different fields of ethics into a single mishmash. This is almost impossible to explain without first describing what the three separated fields are, as I see them.

The Tripartite Ethic:

1) What you should do

2) What you should motivate others to do

3) What you should enforce others to do

Since that simple description is probably not crystal clear, I'll clarify. Field number one of ethics is what you should do. It consists of systems of rules or guides for your own behavior and a method for answering questions in your own life. It's what I think most people think of when they think of "ethics" writ large. This can be as mundane as "should I go to the store" to as broad reaching as "should I dedicate my life to the poor and helpless?" This is the broadest group.

The second sphere is really a subset of the first, in that it is still a set of rules that govern your own actions. This group of ethics, though, govern the way you react to others actions. That is it answers the question: "How should I respond in my actions to the actions of others?" In a manner of thinking, it is a set of rules that you apply to others' behavior to determine how you should react. For example, "Should I lend money to someone I know to be dishonest?" or "Should I associate with someone who dedicates her life to the poor?" or even "Should I invest my earnings in a cigarette company?" This is a set of ethics applied to others, but viewed through the lens of your reactions.

The third is probably the most frequently confused of the ethics. It answers the question: "When should I resort to applying force or threats of force to others or sanction the use of force in my name?" Few people, day to day, find many situations in which they should apply force, barring of course soldiers and police. Frequently when we believe it is proper to apply force we delegate that duty to police and soldiers rather than ourselves. However in the end it is force, applied by handcuffs, jail cells, bats and guns, that constrain the the thief, tax evader, or political dissident.

My point is not to propose an ethic, but point out that each of these is distinct, and deserves its own attention. For example, you might believe that all that are capable should give their lives to the poor (Sphere #1). You might, moreover, believe that when you observe others that are giving their life to the poor, you should respond positively to them and help them in turn (Sphere #2). However you probably don't believe that you should call the police or physically restrain those in the street who aren't helping the poor (Sphere #3). Then again, you might! However it certainly doesn't go without saying.

The problems seem to lie most frequently in blurring Sphere #1 and Sphere #2. Our philosophers, priests and politicians are full of ideas about what "we" should do. This amounts to their sphere #2, what they think that others should be encouraged to do. We have a name for the fact that sphere #1 and sphere #2 are not usually equivalent, it's called hypocrisy.

For all of the moralizing about how terrible hypocrisy is (perhaps it is also in the interest of sphere #2 to bemoan that others aren't practicing what they preach), I think it remains a universal element of human nature. The fact that our interests conflict with others' is just an unavoidable fact of a world with finite resources. We aren't naturally inclined to do unto others whatever they would have us do unto them. There are limits to our kindness. There will always be dissonance between what we'd like to do and what others would like us to do. In the end, the enticements and confinements created by others do shape our choices, as they must. But it is these enticements and confinements, not others' desires alone, that shape our actions.

To go way out on a limb (am I out on one already?), I think that the confusion has most likely been driven by the tendency to follow the person in the pulpit or square who has been invested with authority. We consume their Ethic #2 and try it on for our Ethic #1. They would have us give unto others, or maybe unto the pulpit, so we give unto others, or maybe unto the pulpit. Perhaps we shouldn't be quite so surprised when this borrowed morality becomes hard to follow, and even personally arduous. It is an ethic not our own.

Micro-Science-Blogging

2011-10-28T17:35:00.002-04:00

Here's a crazy idea, what if scientists posted results of their studies for the year, month, week, or hell, even their exciting result of the day, to YouTube? What if we blogged our science?

Let's say, for sake of argument, that I have an interesting result, not yet ready for publication in a journal but certainly worthy of the attention and maybe comments from other researchers. On the one hand, I could work on that idea further until it's ready for a "real" publication. That would probably involve some missteps, maybe some time lost (open the door for scooping), but will also make for a more complete product when it is actually released.

On the other hand, I could open myself up for comments at a very early stage. Not only would the online "timestamp" validate my status as first to the result immediately, it would also give others a chance to comment, criticize, and potentially even initiate their own projects. When the project reaches a later stage I will have already incorporated "reviewer" comments into my manuscript, which will remain just as valuable as a formal report of the work.

Perhaps such a system could work on a by-lab basis. Labs could become, in a weird way, their own publishing houses. Weekly updates on results could feed into the RSS feed of other labs. Just a thought

2011-10-22T08:11:00.003-04:00

If anyone is wondering what regulation capture by industry interests looks like, this is it:

A Life in Energy and (Therefore) Politics (wsj)

The energy industry is mired with so many competing regulatory and industry interests that calling it an "energy market" is a complete misnomer. Note the intriguing comments made later on in the article about some "dirty: coal plants being used as bargaining chips by companies against state and local politicians who, having granted monopolies, are now beholden to those companies.

It's a real fine mess.

The Rational Optimist and Ricardo

2011-10-12T21:19:00.000-04:00

I'm about halfway through The Rational Optimist and have to say that it's a bolstering case for the phenomenal power of markets and trade in human society. It's not exactly erudite, but it's a well versed look at the way trade and specialization have massively improved the lot of the average human in the past few million years and enabled us to grow to a population of several billion. Some of the points I like the most revolve around Ricardo's Law of Comparative Advantage, which the book describes as "the only result of social science that is both surprising and true". In honor of the recent free trade agreement between the US and several countries, I want to take a moment on this little law.

Briefly, the law states that if two individuals, nations, companies, whatever, have a different relative ability to produce a pair of different items, then one or both of the actors will be better off if they trade. So, for example, even if France is better at making both wine and cheese than England (probably true), both England and France will still benefit from trade if the relative cost of producing wine vs. cheese in the two countries are different.

What if France makes wine much more efficiently than cheese, and England does as well? It turns out, it doesn't matter. If the tradeoffs of wine vs. cheese are just different, that's all it takes for both countries to benefit.

What's marvelous about The Rational Optimist is that it couches these modern conceptions of economics in the ancient environment, before they were even realized. Let's say I make spears incredibly well, and I also run down and spear bison incredibly well (probably not true). I take a day to make a high quality spear, and a day to run down a bison. You, sadly, are terrible at both, taking 2 days to make a high quality spear, and 4 days to run down a bison.

Now we could both spend time in our own little spheres, making spears and running around all day. At the end of 6 days, you would have one spear, which you will have used bringing down a bison. I will have made 3 spears and brought down 3 bison.

After those 6 days, you have a brilliant idea. A brilliant idea that will keep you from running around all day and instead put my hands at work for you. Let's say that the next 2 days look a lot like the 6 days before. I make a spear, and go skewer a bison with it. You're just be polishing off that first spear when I bring the bison home.

But now you throw your incredibly brilliant plan into effect. "Ugg!" you say, (we're cave man, that's the my moniker) "I'll give you that spear for just 1/3 of your next bison!".

I think it through. 1/3 of my next bison? But I can have all the bison if I make the spear myself. But if I make my next hunting spear myself, I have to sit around all day making it, and won't be able to hunt until tomorrow. If I take this spear, I can hunt now. Let's see... 1 bison in 2 days, or 2/3rds of a bison in 1 day. I might not be able to figure out the fractions, but I know that I'm getting more. I take your spear, head out for the hunt, and that night I come back with my prize. You get 1/3 of a Bison, and never had to run around on the hot plains.

I won't belabor the point, but if you work out the math, over 6 days with the occasional spear trading hands, you still get your one bison by making 3 spears instead of 1 spear and one hunting trip. I am doing a lot more running, but I get 3 hunting trips without having to stop to make spears, and can make one hunting trip with my own spear and still have a day to spare. Calculated out, I get the same 3 bison, and a free vacation day!

Consider that this example is the most meager form of Ricardo's comparative advantage. Imagine that after a year of this arrangement, you get absurdly good at spear making. You've been doing it every day of the week after all, and you've had a chance to try some things out. Now, after figuring out how to use a rock to sharpen the tips and finding the best source of branches in the woods, you're turning out 2 or 3 a day! You're still only trading one for 1/3 of a buffalo, but now you can stock up on spears the first 2 days of the week and spend the rest of the time figuring out how to corral a buffalo calf and save it for later.

Clearly I've just sketched the barest bones of the argument here, and I highly recommend the book for a better treatment. The point, though, is that specialization and trade are massively powerful ways to improve our own lives and those of our trading partners. Imagine, in your own life, how difficult it would be to independently create all the wonderful artifacts of human productivity you enjoy. What you get out of voluntary trade makes possible the life you lead today.

Minimum Publishable Unit

2011-10-10T16:00:00.000-04:00

What the hell, I'll post again this year.

When is the right time to publish? Is it when you have an interesting result? Is it when your result is "complete", zipped up, lock-tight, slam-dunk? Is it when the result seems more likely true than not? Is it when you want to recruit others to the cause? Is it when you think it's good enough to impress people, and impress them in the amount that you need them to be impressed for your career to advance?

Sadly, I think the answer is often that last on my list. Publications are the the currency of science, and the only hard paper that demonstrates productivity on the public stage. Science is a big establishment, and our individual spheres are often tightly circumscribed, though they may seem vast from the inside. If I am applying for a job, and I have a Nature paper or a Cell paper (not happening any time soon, but let's just imagine for a moment), then I can put that on the desk and point to it. Say my competitor has a paper in PLoS Genetics. Still good, still the same paper, but different brand. I'd carry more weight with the Nature paper.

That brand seeking behavior is nothing new, and it's not actually the problem in itself. The problem is when the brand takes on a life of its own. If everyone buys Nike shoes because they are flashy and Nike, and not because the Nike brand speaks to quality, then where can you expect the quality to go? Similarly if Nature papers are sought because they are flashy, and hard to get, where does Nature go from there? Will Nature continue to speak to the quality of the underlying science?

My fundamental problem with the big name paper is that the results are often big and sweeping. They are rarely circumscribed, but elegant and well thought out. Imagine a demonstration that a single class of proteins perform some specific catalytic or signaling function. This in itself is worthy of being shared with the scientific community, as an informative work. But to get the Nature paper you need to show the protein is relevant in disease X, and that if you inhibit the catalytic activity you cure warts, cancer, and heart disease.

Of course inhibition of the protein won't turn out to actually cure warts, cancer and heart disease. Somewhere along the line a wrinkle will have been ignored. Some control, that in hindsight will be obvious, won't have been done. This is inevitable in the sort of expansive manuscripts that top journals demand.

I argue we should be more focused on rapid dissemination of our research, and broad feedback from the community early on in validating a result. I might run a smart, genome-wide screen, but you might have a better idea of how to interpret it. Someone else might recognize an important statistical error. Most importantly, someone else can *replicate* the experimental results early, before I truck on down the road with faulty assumptions.

The problem is not that the "minimum publishable unit" is too small, it's that it's too big, and that we focus on the papers and the journals and not on the results themselves. In the end, a Nature paper that's wrong is worth less than a small time journal article that's right. The Nature paper that's wrong may actually have negative value, having led labs down the wrong road. Next time you hear someone say that they have a Cell paper under their belt, ask next whether the result was replicated. Ask whether the result has, in fact, been important. Remember to value not the scientific articles, but the science itself.

Irritated in Chapel Hill

2011-10-08T12:34:00.001-04:00

The scientific establishment is Democratic.

News to anyone? Probably not. After all, Democrats by and large promote increases in NIH and NSF funding, tout the importance of the scientific consensus, and so on. But when one is, oh, say, not a Democrat, and trying to do science within the field as it currently exists, there are bound to be frustrating moments.

Take, for example, two seminars in a year with national Democratic representatives. Let me add that these seminars are ambiguously mandatory, with a sign in sheet at many, though not all events, and that they are catered by the program. Imagine a room full of decidedly left leaning individuals with their representative on stage. These reps, so far as I can tell, have nothing to talk about but their political leanings and aspirations. And they'll have all the time in the world to tell you how important they think it is to support, Education and (National) Health Care. They'll also mention how they plan, at the Federal level, to fund Science, Education and (National) Health Care. Nevermind where the money is coming from...

Now I'm not saying no seminar has had a Independent, Republican, or otherwise non-democratically leaning individual who has weighed in, where asked, on political issues. It's just that I have yet to see such an individual given an open forum with a mandate to talk about politics. And perhaps I am being unfair, since I can't imagine any such speakers wanting to wade into the issues of a limited Federal role in that crowd. The Feds are our lifeblood, as it currently stands, and I am one of the few who bites the hand that feeds him. Hell, I have even applied for Federal grants, and benefit from State money given to our Cancer Center, so who am I to complain?

But of course that is exactly the ratchet of Federal authority, ever gaining constituents through patronage, that erodes limited government. I have to live within the system as it exists, even as I argue for its change, as I do here. I just wish that this group of ostensibly deeply thinking individuals were less overwhelmingly game to sign on with whoever holds the purse strings. And I wish they didn't strongly (though perhaps unintentionally) broadcast their inclinations from the top down.

I hate being a naysayer, and I hate biting back at a program that has given me a great deal. But in case people are suffering under the illusion that the MD/PhD program at UNC has built a scientific consensus that you should vote for David Price and Kay Hagen , I just want to make it clear that it has not.

The Hazard of High Throughput

2010-10-31T10:13:00.000-04:00

We live in a high throughput age. Science is no exception. Microarrays, high throughput sequencing and spectrophotometers generate data on a scale and scope that would have taken years, decades, or centuries with the the old generation of technology. We are generating data on a scale that could not be conceived of a decade ago.

Great power... let's see what comes next....

The challenge, of course, is with the analysis. The data now generated is so massive that it cannot be simultaneously visualized or processed in its raw form. Nor can t-tests get you where you want to go with statistical analysis, so many are the comparisons being made.

Enter the world of the Bonferoni correction and the Benjamini-Hochberg false discovery rate. These statistical methods allow us to sift through such enormous data sets to focus on results that are significantly different from random expectation.

The hazard comes with these methods' complexity and somewhat obscure statistical assumptions. Many scientists are very well versed in the hypotheses of their discipline, but less so on the mathematics. There are so many ways to go wrong in applying these methods in a cookie cutter way that it boggles the mind. Along the lines of "Correlation implies causation" there are other such gems as: "Difference in significance does not imply significant difference".

This last mistake was featured prominently in stage 1 of a statistical analysis in a manuscript from a good lab that just passed my boss' desk. This lab has produced prominent publications in the past, and I was surprised to see this in their analysis.

What most surprised me was that the statistical method, buried deep within the methods section at the end of the manuscript, did not arouse the ire of my boss or the other lab member who read the paper. It was viewed as a good enough an answer to a tough problem. That, plus the prominence of the last author, led to a minor note somewhere in the review.

Reviews don't have dissenting opinions, but let me put one here. Statistical methods are important. So important that, in a paper that uses a high throughput method at its start, they often form the backbone for every follow up experiment. They should not be relegated to a footnote in the back and they should, wherever possible, be declared before the data are even generated, to avoid the nefarious problems of overfitting.

Papers that use statistic need statistically minded reviewers. If we aren't careful, we'll be fooled by randomness.

Formulary of Ten

2010-10-17T13:14:00.001-04:00

Here's a quick question: if you had to trim the formulary of a hospital down to just 10 adult meds, what would they be? I'm sure a careful and informed individual could make a comprehensive choice based on "Quality of Life Year" information and some epidemiology of the area. Here's what I am thinking:

1) Morphine
2) Aspirin
3) One or two antibiotics (Guess who doesn't remember micro enough to know which ones? I am guessing Cipro/levo and Vancomycin)
5) Insulin
6) Beta-blocker
7) A statin
8) Doxorubicin (What my boss called: the one chemo you'd bring to a desert island)
9) Warfarin
10) Lidocain

Thoughts? It might be a little over-covered for heart disease and under-covered for diabetes. Also, is it even worth it to have a chemo agent, from a utilitarian perspective? I know that's heretical from someone who is interested in cancer research, but how much life are you really adding with one therapy alone?

Also: does Morphine/lidocain deserve a place? Pain management is important, and I didn't want to leave it out. But when there are other diseases being left uncovered, should this be a top priority?

Anyone care to offer an opinion? It's an interesting way of thinking about priorities in U.S. medicine, and pondering how much we take for granted. What medicines are those that we can't make due without?

Peer Review

2010-10-14T01:32:00.002-04:00

If you've spent some time in graduate school you might have learned a thing or two about the open secret about peer review: it's not entirely done by peers and it's not always that thorough of a review.

Like it or not, grad students often find these reviews on their desk. I've heard of PIs leaving the job to grad students alone, though this does not occur in my lab. It's understandable, though, that this might happen. PIs have huge demands on their time. As invaluable as their opinions are, there is no way some PIs can field every manuscript. Apparently they have their own concerns about peer review (some of which apparently involves colorful dinos)

But what of the reviews performed by graduate students? There are two ways of looking at the duty of peer review.

Looked at one way, a peer review is a learning opportunity. Often the material details development on the edge of some field that contacts at least tangentially upon the student's research. It's a chance to see another lab's raw, early draft manuscript, and learn what merits publication in high level journals and what does not. It is a learning experience, and a chance to contribute to the body of science as a whole.

But let's just step out of the shiny world of gumdrops and candy canes for a moment. Peer review can be an inane chore. While students provide some value added to the journal and the author of the manuscript, it's harder to see where the review process benefits their progression in the ladder. Their role in the review is, effectively, anonymous, and comes with no honor, distinctions, gold stars, pats on the head, brownie points or first author publications.

Stated simply, there is no clear match between incentives and the quality of the peer review. Mistakes are often buried somewhere deep in the unending, jargon-filled paragraphs of the methods section. Whether a grad student takes the effort to check these methods, line by line, comes down to a question of how many hours (if any) of sleep they might prefer to have that evening.

Where's the incentive to dig in? Some grad students seem to possess a deep personal drive to throw other scientists under the bus, but it's probably a minority at most institutions. We can't rely on pure sadism to drive the scientific engine. There must be a way to reward careful and well considered reviews, particularly where they find obscure errors and tenuous methods.

I don't claim to know what the reward should be or how it could be structured. I've considered the possibility that confidential peer review is a mistake, and that publications should instead be edited by the journal seeking to publish. If Nature wants the value added of an expert opinion in the field, let them pay for it! Certainly they demand payment for their subscriptions, so why should their product be provided for free?

At risk of shifting to a seemingly radical alternative, perhaps open access and open comment system is the way to go. Take all comers that pass a basic editorial spot check, and allow insightful, observational comments to come from the community. Those comments can then be tied to the reputation of those who make them. Great insights can be noticed, and unnecessary bickering (I'm looking at you, reviewer #3), can be ignored. Online systems for grading and sorting comments based on reputation systems exist in many forms. Perhaps its time to turn them loose on science.

Concurrently, give labs the hard task of determining their own publication threshold. Perhaps more self review will go on in-house if authors know that they can publish whatever they please, and that they'd better get it right the first time. Perhaps some systems could allow papers to have a 'versioning' system, wherein they could be updated (to a point) to reflect public comment.

I don't claim that pay-to-review or open-comment systems solve the problems inherent in the current publication regime, but I think they deserve consideration. Let us at least recognize that good work deserves good incentives, and that the adding motivation to peer-review can only improve our scientific rigor.

Grad Year 1, Tiny Learnings

2010-10-14T00:22:00.002-04:00

Tiny learnings from grad year 1, stated briefly, for my own purposes. Trite? Maybe. Suck it up.

Mentorship is everywhere, always take the opportunity to meet people doing different things in different labs. The guy down the hall may solve the problem that's been killing you all month. That actually happened to me today, and it was great.

Look out for #1, a little bit. Cognizant of the goal being science, remember that not everyone has your interests at heart. Example: you are very cheap labor for your PI and he/she isn't itching to see you leave. This cuts both ways, remember that you might not be respecting someone else's need to look out for #1. Example: Your PI has duties besides being on-call for you 24/7.
Related Fact: When your PI says it's easy, it's probably hard. If he says it's doable, it'll probably consume all of your attention for as long as you want to work on it. Remember that you are missing years of experience on the person you're talking to.

Write up your research goals early and often, and set some timelines. I almost never make my own deadlines, and almost always find new other goals that I forgot. But you can get a long way by taking a step back and seeing the big picture in which you are swimming, rather than that one method that hasn't been working for a month. Stupid library preps...

All that is gold does not glitter, and all that glitters is not gold. Corollary: Not all new technology is what it claims to be. Stronger claim: It never is. Stay frosty.

When you are on a roll, rock it. There is no more scant resource then genuine excitement. Push it.

Tons of papers are wrong. Downright wrong. Sometimes scandalously wrong. It's embarrassing, and some of it represents systemic problems with peer review and that whole mess. You can moan about it for a long time (I did). Not sure if that's worth the bellyache, but I'll keep you posted.

Don't make second-hand goals that stake your success on the assessment of the Nature/Science/NEJM intelligentsia, or the ivy pillars of the academe. Those gals and guys have their own little world, and it should come as no small surprise that the people in that club house aren't always all that fun to play with anyway.

Say something, or suck it up. I love to whine. It's a problem that I have. I'm still waiting for an example of when it has helped me. If you have problems with something that's going on in the lab, nip it in the bud and have a conversation. You might learn something, you might stop the problem, or you might at least feel better for having it off your chest. Otherwise suck it up.

The goal is knowledge. The goal is not a first author publication. Find the pleasure in solving the puzzles and exploring the science; it is its own joy. The other rewards may find their way to you somehow or another. Remember what you are building, and why you are building it. Remember your ideal.

Words that I am getting sick of

2010-08-21T11:51:00.001-04:00

This is a silly thing to write about, but the are some phrases I am getting tired of. Very tired.

X abrogates the activity of Y signaling - Seriously? Abrogates? Again? Biologists bring this one out at every opportunity, probably because they think it makes them look scholarly.
X is essential to our understanding of Y - It's usually not. It might help, but it's not essential.
Almost infinite - This one goes without saying. It's a lot, I get it. No need to go overboard
On the ground - You don't see this in science, but it's everywhere in the news. Mix it up, people.

That's all for now. Next time I see "abrogates" a paper is getting thrown across the room.

The Enzyme is NOT the Protein

2010-05-18T16:52:00.000-04:00

In my previous post I emphasized that the map is not the territory. I took String Theory off the shelf, dusted it off a little, and then mercilessly beat it up for no good reason. That was all well and good, but I want to address a point about biology that I think we sometimes loose site of.

The enzyme is not the protein. More precisely, the enzymatic activity is not the protein. Really said most completely, the function is not the protein.

Really, I am attacking a map. It's a map many have seen. It looks something like this. Or even like this.

As biologists, we often want to identify a protein's function. We're sort of obsessed with it, I guess. If a protein is necessary to survive, presumably it does something in the cell that allows life to happen. Maybe it helps break down bad stuff, or build up good stuff. Maybe it replicates DNA. Maybe it guards against invaders somehow. Perhaps it's a messenger of some kind, 'transducing' signals through the cytoplasmic ether.

We're helped along by the fact that a lot of genes really do seem to have a particular task to which they are assigned. Hexokinase has a very specific enzymatic reaction that it appears to be dedicated to catalyzing. It lives for the service of catalysis. Our very understanding of genes is driven largely by the observation of mendelian inheritance of genes that break these rigidly defined and clearly necessary functions. Animals with defects in these sorts of genes often suffer most obviously from some metabolic dysfunction, and we assign the gene to the metabolic disfunction.

Now that we have moved past simple metabolism to much more murky phenotypes, we seem to still be tied to the idea of proteins as acting to fulfill a certain function. It's as though they are machines designed to act as some cog that a watchmaker planned to use. Some examples: p53 protects against tumors. It's a tumor suppressor. Hif is a hypoxia sensor. VEGF is an angiogenesis factor.

Why can't these proteins have hobbies? Let's remember that evolution pressures a cell to survive, not to be elegant. In as much as survival is elegant, I guess that gets the cell there. But in the end the thing we're talking about is the most complicated gamish of protein you could think of, and it will do just about anything to get by. Whose to say that VEGF, in its off hours, doesn't swing by the glycolytic cycle for a little regulatory interlude. Perhaps Hif, during lunch hours, cruises by the spliceosome for a little slice and dice. Even the 'housekeeping gene' and paragon enzyme GAPDH appears to spend lazy sundays in the nucleus, a horrifying prediction for the one protein one function minded.

Let us remember that's what really happens in the cell is a very very complicated mess of reactions. Once a day in some cell in the human body I'd guess that every possible protein interaction pairing can and does occur. There's no reason that the cell hasn't evolved to use some of those strange pairings to give it a little more juice towards the end of its endless quest for self replication.

Which is all just to say that a protein doesn't need an easy to pin down function to be very important for the cell. Nor does a transcript with a well defined function necessarily not have other very important roles.

Here is the question we should be most concerned with. In all the cartographic glory of drawing out these maps, have we missed something essential? I've argued above that we've probably missed the fact that some proteins act in different places, and again missed that some places might occasionally be occupied by different proteins. I would argue that this is more than a frivolous attack, it explains why our experiments are so difficult to replicate and real advances are only rarely driven by deduction alone. We've only just scratched the surface of the combinatorial possibilities.

The Map is NOT the Territory

2010-05-17T22:05:00.000-04:00

I know very little about Alfred Korzybski, and even less about general semantics for which he is famous. I do, however, know his most famous quote: "The map is not the territory". So it is with complete academic ignorance that I co-opt the term for use in the world of biology. You've been warned.

Here's the idea, as I conceive it. You have a map. You have a model. You have a theory. Your theory is awesome, beautiful, exciting and entrancing. Let's say it's string-theory. Let's say it's relativity. Let's say it's evolution. Whatever.

This thing you have, this idea, it's a map. It's a guide to something. It's a flat piece of paper that represents and distills something about reality. It is not its own reality. It is not its own truth. It's serenity is not the same as the actual cold hard truth of the thing that it describes.

In the case of a map, like the kind you hang on your wall, this is obvious. No one is lining up troops to defend the borders on the map in the atlas on your coffee table. They are lining up to defend the real borders of the real states on the real rivers of the real earth.

But in the ivory tower there is a proneness to confusion. String theory, perhaps the best example I can think of, is lauded for its elegance and seeming brilliance. Few people could imagine an explanation for the complexity of quantum physics and relativity in a set of dimensions coiled down so small that they cannot be perceived at our scale of life. It is an impressive theory.

Where is the territory to go with it? String theory has yet to make a single testable prediction. Its details are so arcane that those who study it seem to inevitably be lost in its folded dimensions, content to treat the theory a platonic ideal to which the universe we live in might aspire to reach.

The map exists in service of those who live in the territory. Our theories exist in service of our abilities to make predictions and interact with our world. We draw Australia into our map because it helps us to make predictions about the consequences of certain actions (e.g. hey, I wonder what will happen if I sail South from Indonesia).

In Biology we are plagued by pseudo-predictive models. We spend a lot of time flailing around trying to come up with "mechanism" to explain our observations. We often find that we can come up with two or three. Sometimes we bother to test the predictions that our hypothetical mechanism would imply. Too much of the time the data show muddled and confusing support. We often pick and choose the experiments we want to perform that seem to bolster our point, and dig in for the academic fight over the arcana we've brought into the world.

In the end, the mechanism doesn't mater. The elegance of our theory doesn't matter. We can fight over the lines on the map until Armageddon, but what matters is whether we've done something positive in the real world. This should be completely obvious, but I am shocked at how quickly I am loosing touch with that simple fact. Keep your wits about you, and tuck Korzybski's saying in the back of your mind.

Evolution and Financial Markets

2010-04-18T14:21:00.000-04:00

I am way out of my league writing this post, but life is short and its fun to get ideas out there.

I was listening to one of those iTunesU podcasts about behavioral economics and I was moved to throw the following hypothesis out into the intrawebular ether. It's an explanatory hypothesis, and I can't figure out any useful predictions that it might make, but when has that ever stopped anyone before?

The idea is this: The boom and bust cycle of the markets is driven by an ingrained human tendency to barrel forward full bore when we and those around us have resources, and hoard resources when we and those around us have little. The key here is the social nature of the tendency, we don't just pay attention to our own resources, but also of the perception of the total resources available in the environment.

Furthermore, this tendency might be the result of evolutionary pressure. When the environment is perceived as resource rich, the most evolutionarily favorable strategy is to voraciously consume those resources rapidly. This has two benefits. First, it makes those resources available to an individual for making offspring. Second, it removes those resources from the pool, keeping them away from equally hungry competitors.

Biology has shown time and time again that this approach has consequences. In almost every species studied that I know of, permitting organisms to eat as much as they want shortens their lifespans and decreases their overall health. These animals are willing to make a sacrifice to be able to consume as much as possible and have the best chance of leaving the population with abundant offspring.

However this technique only works so long as the resources are present. When hard times come, animals convert to a different strategy, sometimes a radically different strategy. In C. elegans, for example, a relative starvation causes the worm to choose a completely different life cycle. The worm forms a 'Dauer' instead of an adult. This smaller form lives many fold longer, and waits until better times to spring into its adult form. When resources are scarce, survival beats out consumption.

And of course, humans are no exception. In good times, when resources are abundant, we are prone to consume those resources. Moreover, we are prone to turn into the voracious creature of our evolutionary history, making sacrifices for our future financial health to ensure we are not left behind in the rush. When time are hard, however, we throw a bit of a switch. We act much more cautiously, recognizing that surviving through to the next boom is our top priority. We are much more cautious with our investments, and put in the due diligence that was starkly absent in the good times.

I know I haven't really identified anything new here, but it was an idea that had never occurred to me. What does all this mean? My one conclusion would be that any large financial system, inasmuch as it tends to aggregate human perception, will be prone to the boom and bust. The deep psychological root of the human tendency (this is something that goes all the way down to worms, after all) means that it will happen again, and it there is probably little that we can do to stop it.

The Open Revolution

2010-04-18T10:39:00.000-04:00

Read this: http://www.nytimes.com/2010/04/18/education/edlife/18open-t.html

The question raised most loudly, I think, is how educational institutions and the educational process should change in a world where educational materials are much much more freely available. Like everything else in the internet age, there are two fundamental things missing. The first of these is human contact. You know, the kind you get from actually sitting in the room with a group of fellow students working on the same thing. The second is a filter. How on earth do you find the good and reputable information in the vast sea of the internet. Who compiles a menu of course offerings that pull together the richest and most savory elements. After all, you could try reading wikipedia from a to z, but I don't know that would really be an education.

Finding a way to incorporate contact and filters into university education are challenges, but they are challenges that learners can solve independently. There's always meetup.com for finding a learning group, and online aggregate ratings can steer towards the good courses (if imperfectly). But there is one problem that fundamentally cannot be solved by a single person working to elevate their education on an individual level.

This problem is certification. Once you complete a course at a traditional university it gives you credit towards something called a degree. This degree is backed by the institution, and comes with a reputation that acts as a signal to people in the marketplace. As odd and unsavory as it may sound, the degree 'brands' you, and gives you the benefits (and sometimes detriments) that brand confers. There is nothing I can think of that allows an independent learner to acquire such a certifying brand. Institutions that acknowledge and certify learned skills are desperately needed if the open courseware revolution is going to take another leap forward.

The Six Degrees of Functional Genomics

2010-03-19T18:23:00.001-04:00

I enjoyed a whirlwind functional genomics talk by Steve Kay today about the circadian rhythm in a number of model organisms. Perhaps most interesting was his outline of the functional genomic approach. They were, paraphrased, as follows.

1) Identify the elements of the circadian rhythm machinery
2) Model and generate quantitative hypotheses about this machinery
3) Synthetically reproduce the machinery

He went on to describe how we are working on wrapping up step 1 and just beginning to dip our toes into step 2.

I should start by saying that I think the research described was truly impressive and brought to bear a large number of high throughput techniques to answer a question in a way that went beyond the usual model of figuring out what "your favorite gene" has to do with process X. He's turned things on their head and asked what does gene Y have to do with "my favorite process", and answered the question en masse.

However after he delved into the previous work on circadian rhythm I was left a little worried. In a process which, like the cell cycle machinery, transcription and translational control is so key, how can you avoid the fact that a vast swath of the cell's general control mechanisms for these fundamental processes will in some way also affect the circadian rhythm? At some point, any element of the cell that isn't completely inert is going to affect any process you can choose, albeit in perhaps a small way.

Which brings me to the Six Degrees of Functional Genomics. No part of the cell exists in isolation. No process in the cell can, really, be excised from the context of the larger cell. What we're talking about, after all, a tiny little bag of water packed chock full of different proteins. True, there are compartments, but these compartments have a nasty way of communicating with each other. In the end, every protein in the cell is functionally related to every other protein of the cell, given enough degrees of separation. Quantitative models are likely to look more like weather models than a nice damped oscillator. In the end, it's all very dependent on initial conditions and most predictions will be probabilistic in nature.

To try to develop quantitative models of cellular behavior given current knowledge may be like trying to model all of human social interaction by the Facebook "friends" network. It's heavily biased for certain kinds of interactions, like those between college dorm mates. It's true, these relationships are very important, and they tell you a lot about how the cell behaves day to day. But there's almost certainly a set of interactions that we don't know we don't know, like the interactions with parents and family (not everyone wants their parents seeing all those facebook photos).

To this extent it probably is important to try to develop the networks we currently have. We do still want to find key elements of our processes of interest. Hopefully we don't get so carried away looking for the next gene that we forget the complexity of cellular function try to build ever bigger piles of genes in our category of interest.

I'm aware that I am setting up a bit of a straw man, here, and I don't pretend that Dr. Kay is unaware of these kinds of concerns. But I think when the broader scientific community looks to functional genomics and computational biology for answers, they need to be aware of the fundamental limitations. Frankly I am not sure if the broader scientific community looks to functional genomics and computational genomics for much of anything, but that has its own problems.

Tool Time

2010-02-05T07:53:00.003-05:00

To start my note off on a tangent, I want to recommend the "getting to work early" paradigm. Arriving to work before 7am finds very few souls clogging the arteries of the building, and very few distractions to sidetrack this grad student's taxed neural network. It is a time for settling in and thinking about the big picture. It is also a time for using all those adverbs that your PI has stricken from your science writing. Even the word "abrogate" gets boring if repeated endlessly like a sitcom laugh track.

So let me expound, veritably explicate, upon the following question: What are the bioinformatic tools that I wish I had for my research? The answer comes like a dam burst. There is simply too much material to stay above water.

Multiple Reference Short Read Mapping: By this I mean a tool that corrals the multiplicity of reference human genomes and variant annotations and links them together for read mapping. This might sound silly, since most reference mappers can handle a SNP here or there. But there are a number of "everything that can go wrong will" sorts of scenarios, where a common SNP variant or two can lead to horrifically erroneous mapping. This propagates into very confusing results down the line. Such results have to be carefully untangled by hand before they reveal their fundamentally invalid core. With the tools we now have available in the human genome, multiple reference mapping is becoming a must have app. So if you're out there WashU, Broad, Sanger, BGI, hear my prayer.
Base Quality Retouching: Like the photographs that grace the covers of the latest supermarket magazines, the output that spools off of an Illumina GA needs a little retouching. Sometimes it needs a lot of retouching. There are the issues of PCR duplicates, nucleotide chemistry and failed cycles. These are technical problems that may or may not go away any time soon. In the meantime, we need better base quality numbers. Is that set of 8 reads calling an A instead of G a SNP? Hard to say if you can't believe your base call qualities. MarkDuplicates (picard) and the GATK coming out of the Broad might have this problem mostly solved, but they remain to be packaged into a neat little bundle and handed out like candy to the rest of us.
The Mapping Quality Problem: To anyone who has played with the high throughput sequencing technology should know about this problem. What does it mean that a read maps to a given location? Suppose it maps to one location perfectly, but 25 with one mismatch. Suppose instead that it had mapped to one location with one mismatch and only two with two mismatches. Which gets the better mapping quality? How are these situations even comparable? I have my own thoughts on a Bayesian way of handling this situation. Maybe just saying the word Bayesian is enough to conjure my solution, and maybe it's too naive to be useful in implementation. Regardless, we need an answer sooner rather than later, lest interesting loci perish for want of a good sequencing read to feed them.
The SNP caller to end all SNP callers: This really does not deserve an explanation. (1) We want SNPs. (2) We want confidence scores for those SNPs that is remotely close to correct. The first part is done, we can call SNPs, but I'll be damned if I believe the kinds of confidence scores we assign to them. Getting this problem solved really requires getting the three problems above solved first.
Structural Variants for the Rest of Us: The gsMapper has a nice little tool for calling structural variants. Of course, 454 reads are quite amenable to this kind of work due to their length. Paired end Illumina reads should be perfectly functional too, though. I have yet to see an easy to use structural variant caller whose results I can sink my teeth into. I've seen a number of ad hoc tools, and some very high level tools which are nearly impossible to use. To get this problem solved rightly we probably need the mapping quality problem solved first.
De PseudoNovo RefSembly: No I am not just trying to smash words together to sound smart. I bring this tool up because, in my ideal world, the tools are bountiful, the data overfloweth, and every grad student is above average. In this imagined world we also have this little gem of a tool for particular problems. Sometimes you have a reference. Sometimes you have multiple references. Sometimes you have some reads that map to the reference, and some reads that you think might represent some new genetic material. You'd like to map to the genome, but you'd also like to put together those delicious additional morsels into something that approximates a meal. For this you want Ref-Sembly, a tool that uses the a priori information from the genome you are working from but also openly allows and embraces the possibility of additional sequence. Such a tool should make a best guess at what such underlying sequence is and provide information about how that sequence might connect to the reference you've dutifully provided. Currently, I think people map reads to the genome and just cram the unmapped refuse into a de novo assembler. I'm not going to say that this is wrong, but, in my heart of hearts, I don't feel that it is fully right. Assembly off of a reference needs to be more nuanced than a garbage compactor.
A Visualization Suite that Doesn't Crash My Computer When I Try to Look at Tens of Thousands of Reads: Does my request defy the bounds of computer science? Is my measly 8 gigs of RAM insufficient for your hungry java app? All I know is this: there is currently no replacement for putting eyes on data. I can see an indel coming from a mile away if I can visualize my reads. IGV is my current tool of choice, but it craps out (for me) when the coverage gets deep. Unfortunately this is where I need the tool the most. Maybe the answer is that I should get some more sticks of RAM, but I have to imagine that the coverage is only going to get deeper, and the problem will continue to mount.
A Visualization Suite that Produces Poster-Ready Images: UCSC genome browser comes close. Very very close. But the difficulty in customizing the visualization and the the granularity of the images (with their horrific font) makes this a step down from my ideal. If only there was a "whimsical" button to enhance the graphic appeal of the data it already displays, then I think we would be there. If I am looking across 100 kb of sequence I need my exons to have a little more flair then a vertical line one pixel thick. My guess is that my hypothetical reader is now laughing that I didn't notice the "Visualize, with Feeling" button, tagged with the infamous 'blink' html, that sits dead center on the home page at genome.ucsc.edu. Maybe that person will email me.

That's it for now. I think I have exorcised the adverb demon that haunts my scientific writing. I return to the keyboard and the pipette knowing that my salvation is temporary, and that the thirst for flowery exposition shall rise again.

Presenting to Peers

2010-01-24T17:48:00.001-05:00

So this is just a random comment, but I realized recently how infrequently a grad student gets the opportunity to present to his/her peers. Well actually I didn't realize it, a friend of mine in the program realized in and brought it to all of our attention. He offered to start off a set of presentations that we, as students, would give to each other.

This may sound a little ridiculous at first. You're thinking: "What on earth is he talking about? Poster sessions, journal clubs, lab meetings, maybe a departmental presentation or two aren't enough?" Well, yea, I guess those are quite considerable. Our peers form most of the audience for those presentations. But in those circumstances there is almost always an authority figure present. There's your PI or even other PIs and members of your thesis committee. There's a program director or maybe even a judge who is looking for the presentation that wins a prize.

And that is great. Don't get me wrong; we need that. But where is the opportunity for us to grow into independent scholars? Where is the opportunity for us to shape a presentation style that is designed to speak to an audience that understands the material at our level, and on our own terms? I've noticed that the talks that are most captivating at conferences are those that are delivered with a sense of familiarity and comfort with both the subject mater and the audience. They're often delivered with humor and the occasional hint of wry, self-deprecating humility. If we want to train not just scientists, but communicators, we should give them the chance to train their art in an uninhibited setting. I'm concerned that always reporting our results in front of people we need to impress may further entrench systems of jargon and insular academic perspective.

I'm not trying to badmouth lab meeting or committee meetings. Those are opportunities to expose our line of research to outside challenge, and sometimes even outside attack. We need that. We need to learn to think like scientists. That means constantly revisiting our own assumptions and our own familiarity with our discipline. But maybe every once in a while the big guys could step out of the room and us baby scientists could talk about what we do to each other. It might save a little adrenalin for another day, and it might foster lines of cooperation between students that could last through our careers. And who knows, maybe we wouldn't have to sit through so many presentations that sound like someone reading through their alphabet soup.

Too many articles, too little time

2010-01-15T14:31:00.000-05:00

How on earth do you drudge through the literature? I guess a good number of people read the abstracts and skim for interesting figures. A few others hone their area of interest into such a tiny corner that they can actually be completely up to date (in, say, VHL and HIF1alpha interactions in hypoxic conditions in HeLa cells under 10% FBS concentration, or whatever).

But where is the fun in that? As someone who got interested in science because it was just, so damn cool, I don't want to give up that connection to the broader picture. I'm not particularly interested in participating in the construction of my very own, brand new pigeon hole. I can see the wooden outlines now: using sequencing technology for subtyping of malignant melanoma in a clinical setting using gene list X. I will know everything about how to use sequencing technology Y to look at melanoma genes X in patient group Z. I mean I will be able to take the kids to school with my most scholarly, mind numbingly in depth knowledge of XYZ. But I will have forgotten the wider goal, the bigger picture. I will have specialized in the war of 1812 only to find that World War-Eleventy-Two is raging on without me.

Solutions? Google Reader sure seems like a good shot at trying to stay mildly up to date. Even there I get ruggedly behind. What I really need is a second brain that can sift through all the sludge to find those nuggets of thrilling wisdom. Anyone know where I can get one of those?

Paradigm 4

2009-12-23T10:24:00.001-05:00

If there's one thing I hate, it's the monotony of generating data. I'm working on running sequencing on 48 samples right now, and let me tell you that is not a bag of laughs. I am all too aware that what I am doing could probably be done faster and better by a halfway decently designed machine, and consequently I just sit there daydreaming of a world in which I have that machine. This leads to pipetting errors which leads to more frustration which, as you can imagine, leads to more daydreaming. It's a cycle of violence on the microliter scale.

Which is all just an intro to explaining why I loved this article in the New York Times on the data deluge. Sequencing machines (among other things) are generating so much data that it's actually the analysis that becomes the limiting step. Eventually there will be so much data output that there will be little need for pipetters and a great need for analyzers. That's right, the limiting reagent is actually human brain hours.

Mind you not just any human brain hours will do. To understand this data we need people well versed in computer science. Computers, after all, are the only things capable of reading off the billions of bases involved in anything approaching reasonable time. A human reading of just a single human genome at one base per second (no breaks, no sleeping) would take over 95 years to complete.

Our limiting reagent brain needs to be versed in statistics, to allow for the fact that any comparisons made on the genomic scale and possibly between large populations. Signal is well hidden by noise, and the noise isn't even necessarily as random as we would like it to be. After all, this is a living, breathing genome we're talking about, not a string of A's, C's, G's and T's as we often imagine it.

Which brings us to the third necessity. The analytical brain that we need also, ideally, should have a strong understanding of molecular biology and the biology of any disease in question. Cells are complicated. Ridiculously complicated. But we do know a pretty enormous amount about how they operate. A thorough understanding of this prior knowledge helps us ask more pointed questions of the data in hand.

Have I overdetermined the system yet? Probably. In the end, deciphering this data is going to take a lot of collaboration. I've seen a lot of attempts at all-in-one prepackaged analysis engines for sequencing data. None of them, so far, looks very impressive. Moreover understanding the output of such packages is its own special challenge, since their inner workings is often closed source or poorly documented. Thus it's often hard to trust or interpret the results that you don't generate yourself.

So will this data flood answer the big questions of our age? Are we going to find the cure for cancer? Perhaps at least the cure for a cancer? If we do it will be not only because of our ability to design and execute good experiments, but also our creativity in sifting the results.

The Panopticon, Part II: Control

2009-12-11T15:29:00.000-05:00

Having just opened a machine of revolutionary scientific power, you convene your graduate students to discuss the possibilities.

Yes I know this is ridiculous, what PI respects and trusts his grad students enough to share this kind of information? He'd just go straight for his fellow PIs, right? Just roll with it, people.

Sitting down with your students you first carefully explain to them the circumstances of finding the machine and the mysterious booklet with its unbelievable claims (see Part I). Your students sit around and listen attentively and with increasing eagerness lean forward in their chairs. They've seen the strange device stowed away in the corner of the lab, and they know this is not one of your endless hypothetical. You end your explanation quickly and ask:

"So, what should we do with the machine? We have 25 uses and we'd better use them well!"

Abe: "Well clearly we should start analyzing people with Our Favorite Disease! We can look at 25 of them, which should give us a good sense of what's going on in OFD."

Ben: "What's going on in OFD? Do we even know what we're looking for?"

Abe: "Well first of all we can see if parasite X is present in OFD. I mean that theory has been kicking around for a long time now."

Charles: "But what if there are no parasites, or only a few parasites? Do you want to waste all of these experiments just looking for parasites?"

Abe: "Well that's the great thing about it! I mean if you really can look at everything, then we can answer a lot of questions at once. Like what about the theory that OFD patients have greater p123 signaling? We'll be able to count the p123 and answer that one right off the bat, too."

Ben: "Hold on bucko, what are you saying? If we just look at 25 OFD patients we won't have any idea whether p123 is high or low. We'll just know what it is on average in those patients, we have no basis for comparison."

Charles: "Yea Abe, slow down. We need to think through this. What are the proper controls?"

Abe: "Proper controls? Look we only have 25 runs of this thing, we need to be focusing on interesting samples, not normal everyday people. What if we don't look at enough sick patients and miss something important?"

Charles: "We can't do this without controls. There's just no way. You have to be able to compare the patients to some estimate of what's normal, and there's no other way to know what's normal without using some runs of the Panopticon. I mean, we could use previous estimates of p123 or parasite prevalence in the general population, but there's no way that we can really believe that those are accurate."

Ben: "Yea, I remember hearing that p123 may have three or more isoforms that have been undetected in our blotting assay. The Panopticon is powerful enough to see those."

Delta: "It will see those... (Mysteriously) but what else will it see?"

Ben: "What do you mean, Delta?"

Delta: "What else will it see, under the surface? It's true we may see p123 isoforms we expect, but what about those we don't expect?"

Ben: "(Condescending) Well we'll look at those, too. Now Abe do you see why we need to run some normal patients through the machine, too?"

Abe: "Yea I guess, but I think we should do as few as possible."

Ben: "Well yea, I mean you only need to run a few controls."

Charles: "Do you guys just completely not get it? We're not running the kind of control where we know what to expect. This isn't like running a PCR with water instead of DNA template. We don't just need to know what's normal, we need to know the variability of normal. Sure, we might put two people in and they might not have parasites, but what if the third one would have? If we see parasites in half of our patients we still won't know if that's normal for the general population?"

Abe: (Sighs) "Well what do you recommend? We can't waste all of these runs."

Charles: "Split it down the middle. Half the runs, or I guess 13 if it makes you happier, could be patients, 12 could be normal people."

Abe: "I'd say it was a shame, but I guess we can still answer so many different questions."

Delta: "The more questions you ask, the more will slip through your fingers."

Ben: "Well I know that doesn't make any sense. The more questions the merrier."

Delta: "Seeing everything is like seeing nothing. It is a true Panopticon. The original Panopticon was a tower in the middle of a prison. Each cell faced the center, and the guards could see all cells from their vantage point. So will you be within the Panopticon. You see all, but in this sight you become imprisoned. Just as you can view each cell, you will find you can see none of them."

Abe: (Laughing) "So speaks the oracle! Did that make any sense?"

Ben: "Not that I can tell"

Charles: "No"

Delta: "Go ahead then."

Abe: "I don't know what Delta is talking about, but let's just run 20 people, 10 healthy, 10 sick. We'll have 5 left over just in case something goes wrong."

You decide to allow your graduate students to proceed. They agree to try the machine on ten patients and ten healthy people, analyze the data. You are pleased that they've come to the right conclusion, and put together a case-control design. You worry, though, about Delta's ominous prophecy for this experiment. Perhaps you will find out what she means when the data comes in...

Continued later.

On purposeful learning

2009-12-08T13:47:00.003-05:00

I was cleaning out my Onenote files the other day and came across a poem by WB Yeats I liked and saved:

"What Then?"

His chosen comrades thought at school
He must grow a famous man;
He thought the same and lived by rule,
All his twenties crammed with toil;
`What then?' sang Plato's ghost. `What then?'

Everything he wrote was read,
After certain years he won
Sufficient money for his need,
Friends that have been friends indeed;
`What then?' sang Plato's ghost. `What then?'

All his happier dreams came true -
A small old house, wife, daughter, son,
Grounds where plum and cabbage grew,
Poets and Wits about him drew;
`What then?' sang Plato's ghost. `What then?'

`The work is done,' grown old he thought,
`According to my boyish plan;
Let the fools rage, I swerved in naught,
Something to perfection brought';
But louder sang that ghost, `What then?'

I mean to post a science-based post soon but this has been a continuation of conversations we've had in the past semester. What is the end goal of accruing knowledge for all of you? How will we use it? Or will we end up spinning our wheels with minutiae discoveries only for the sake of accruing grants we require for personal survival?

A Risky Statement on Gender

2009-12-08T10:20:00.000-05:00

Before I get myself into too much trouble, I want to mention that N=2.

So I am sitting in my bioinformatics class during presentations and I am noticing an interesting phenomenon that I can't help but post about. Each student is supposed to present, but students can form teams.

Here's the result: The two girls in the group are part of a two person team. There are no two person teams with two males (of five). In the teams with a male and a female, the female begins the presentation and the male jumps in about 2 minutes into the 15 min presentation and (loudly) completes the remainder of the presentation. The female sits patiently for the balance of the presentation, but the male never hands the torch back.

My tentative conclusion, the barriers to women in science are often subtle, and are not only institutional.

A Comment on Bioinformatics

2009-12-01T09:56:00.000-05:00

So this is my summary of bioinformatics approaches to sequence alignment, phylogeny, and structural prediction:

Create a set of assumptions so that you can use dynamic programming
Use dynamic programming
Forget how ridiculous your assumptions were

There you go. I just saved you hours of coursework.

The Hoard addendum

2009-11-23T12:23:00.001-05:00

So I think my post about the Hoard was somewhat rambling and failed to make any clear point. My concern is this: science has outgrown its incentive structure. Good science needs to be done on a scale that involves many hardworking individuals, not all of which will be writing the manuscript and scribbling their name at the front of the author list.

Take, as an example, a massive study to sequence genes in cancer X. This is going to involve intellectual contribution from dozens of people, if not hundreds. Doctors will be involved in enrolling patients and providing detailed clinical annotations of their progress. Surgeons will carefully select samples. Pathologists will carefully grade those samples and possibly select which can truly be classified as cancer X and sent to the lab. Lab workers will perfect protocols, churn them through a pipeline they design. Bioinformaticians and statisticians will then undergo a rigorous analysis of resulting data, perhaps even writing new programs and methods for their processing.

The result, surely, is going to be an enormous multiauthor publication or series of publications. None of this could happen without a few organizing minds at the top, and they, rightfully, claim a great deal of the credit. But what of the multitude of other researchers also involved? They are sandwiched somewhere between et. and al., without the due credit many of them deserve. Much of their work is crammed, often in tiny font, into the often discarded 'methods' section.

To come back around, I am concerned for my place as a growing scientist in the sea of collaborative studies. I truly enjoy the thrill of pushing back scientific boundaries, and specifically I think that modern sequencing offers a powerful tool to do just that. But as a new graduate student who will not be leading the studies, I am concerned that these projects lead me nowhere towards a first author publication. I'm therefore treading water with respect to fulfilling my requirements for graduation. I want very much to be a good scientist and even more to work on important discoveries, but I worry that some ways of doing that come at the expense of my own career.

The Panopticon, Part I

2009-11-21T11:28:00.000-05:00

First off, I just want to mention that the word panopticon is one of my favorites. This post is going to be about something that might more properly be called "Heisenberg's Device", but I am going with The Panopticon. The traditional use of panopticon is more dire than the one I describe, although I hope to draw a line between the two in later posts.

I'm going to lay out this story over a couple of days, since I don't want to spend too much time on it each day. A little suspense never hurt anyone. No device like the one I describe exists, or really could exist, but I think it's an interesting point from which to inquire about our scientific methods.

Please forgive my use of the second person. I like it because it makes the whole thing feel like the lead up to a question, which is exactly what this story is intended to be.

Here we go:

You are a biologist tasked with the study of disease X. You've been studying the disease for decades, and you understand a great deal about the workings of this illness, from the molecular biology to the physiology to the pathology and epidemiology. Your efforts have been fruitful. Bit by bit you've knocked back the boundaries of ignorance and revealed certain properties of disease X that have garnered you recognition in your field, though done less to actually cure it.

You come in to work in your lab one day and notice a large package sitting outside in the hall. It has your name on it, but no other identifying information. Curious, you begin to rip into this strange gift. When you tear all the packaging away you find you have a device that looks not unlike a refrigerator coated in a series of snaking tubes, valves and wires. Like a refrigerator it has a door in the front, which can be sealed with three enormous locks. Behind it has an industrial scale plug, a usb port with cord and, tucked away in a small leather pouch, a booklet.

Of course you grab the booklet, looking for some information, any information, about what this strange shipment is. Have your graduate students gotten carried away with themselves? Have the minus 80 freezer designers gone all 'steampunk'?

The front of the booklet has only the following words in bold letters

PANOPTICON

Twenty-five uses

Tantilized, you open the manual and read further. It reads:

Greetings! You are one of a select group of scientists that has been chosen to receive our latest revolutionary technology. We are aware of your tireless pursuit of knowledge and your prior successes, and we are assured that you will know how best to use our device to maximal benefit. The machine we have provided (the Panopticon, hereafter), is just another tool in your pursuit of the cure. We provide 25 uses, all this machine is capable of, at no cost to you or obligation of future purchase.

This device has been developed in secret by our labs and is able to perform perhaps the most rigorous scientific assay conceivable. With just simple setup procedure, this machine is capable of detecting and reporting the precise position and movements (within Heisenburg's limitations, of course) of every atom and molecule contained within the chamber. This data can be downloaded to a computer where it can be stored for analysis and detailed examination. The machine can track this information for five seconds before it becomes inactivated due to the risk of power overload. Never fear, though, the technology is perfectly safe and can even assay living subjects at no harm to or effect on the assayee (see: FDA certification information in Appendix F).

The details of operation and the proper interpretation of the data output formats are provided in this manual, but we have removed key elements of the Panopticon's operation which we consider trade secrets. We hope that you will make use of your 25 assays fruitfully. Good luck!

You sit down at one of your labs benches, confused. Every position of every atom and molecule in the chamber? For five seconds? No microscope or imaging technology even comes close! It's like transmission electron microscopy in real time, on a massive massive scale. And in living subjects! You take a moment to ponder the implications

So that's it for today. I'll explore the reactions to the machine in later posts.

The Hoard

2009-11-20T12:30:00.000-05:00

Science is built on the daring and unconventional ideas of a few going against the hive mentality of the pack. Things that seem like strange artifacts in the data emerge as cryptic signposts pointing to bold new ideas. The bizarre non-Newtonian behavior of light led to relativity, and the equally bizarre downregulation of supposedly overexpressed genes in petunias eventually became RNAi.

But in the era of big science, how do we pick what anomalies are worth following up on? As science becomes more and more dependent upon technology to progress, any line of inquiry becomes expensive both in dollars and man hours. Within this paradigm, is independent investigation possible?

I would argue that in many ways it is not, but our current model still does little to promote the large scale collaboration necessary for modern research. There is still a big emphasis (in biology at least) on first author publications rather than roles in large collaborative studies. Collaborative openness, while often touted as a cornerstone of an institution, often does not make careers. We like to put a name on a given discovery, or maybe two names. We award Nobel Prizes to a handful of researchers, not a collaborative team.

That's not to say that we're not trying. Take The Cancer Genome Atlas (TCGA). This massive effort by six major research centers will work to sequence tumors and matched normal tissue from the same individual across dozens, then hundreds, then thousands of individuals. Eventually it will trace the contours of the cancer genome at extremely high resolution by using very powerful (but expensive) sequencing technology on a massive scale. This is the sort of google maps of the cancer genome. This atlas will be available to everyone, publicly, at no cost to the users.

It will be interesting to see if this new paradigm works. As great as these new, massive, collaborative databases are I think we have one risk, which is a sort of inverse tragedy of the commons. If the data is public and everyone has access to it, no one will have a vested interest in following up on that data. Publishers just aren't as impressed with computational follow up as they are with studies that generate new data. Furthermore we seem to have very little interest, in our modern scientific society, in 'negative' results. Much of what these databases will do, I think, is wash away the apparent significance of correlative studies done with our scientific eye trained on just a few pathways. If we look at two genes and see that they go up and down together we call them a signaling pathway. But if we look at 2000 genes and see them go up and down together, do we still come to the same conclusion?

It's a rocky road we embark upon.

On the dilution of science and loss of value

2009-11-17T10:10:00.002-05:00

The other day, Will and I were discussing the vast number of scientific papers already published and what little room there will be for our own contributions. In our despair, we forgot a crucial part of life, that quality will always outlast quantity; in a strict capitalistic sense, while there might be imitators, the ideas of good products will always be bought and sold. Take for instance the iPhone app store. At its conception, the few apps that existed served very specific purposes and developers were able to charge a fair but profitable price for their services. Once the app store began to get diluted with apps such as "Goal2Action" and "Looptastic Gold", the power of the market once again shifted to consumers to find and support quality apps. So in regards to science, how far can I take my favorite metaphor? Who are our consumers?

The scientific community plays a large role in judging the quality of our work, but in a deeper philosophical sense, Time plays the final judge in determining quality. Because as scientists, what we're really striving for is to discover truth. Pontius Pilate once asked "What is truth?" and the answer is maintained by methods that are still unreachable to us. Therefore the discovery of truth will always remain the final barometer for our work. Scientific discoveries such has Mendel's postulations on genetics or Galileo's astronomical work (haha) has stood the test of time. Even Darwin's evolutionary thought has accrued evidence to defend itself. And while our contributions might not be as large, if they reflect truth, they should withstand both the scrutiny of our peers and also the interrogation of Time.

And that is what we should be aiming for, to understand truth, as opposed to trying to publish for the sake of our career. Ambition is not necessarily evil, but it should not be the forefront of our motivations. Too often in science we see work being done because it is required for a grant, as opposed to a more "pure" motivation of just wanting to know for the advancement of knowledge. Such work de-values the body of science because the act of merely spinning your wheels gains you no distance. And the community itself is to blame for adopting an environment that is a microcosm of the capitalistic world at large, a model that struggles to succeed because the end goal is always selfish achievement.

So while it can be discouraging to see the volumes and volumes of scientific literature, I find peace that not all of it is noteworthy, not all of it is significant, and not all of it is true. And I remain hopeful that as I continue to seek truth, I will find it.

The Blog to Nowhere

2009-11-13T18:29:00.000-05:00

So I never really introduced this blog. Since I am not really sure who is reading it (anyone?) I want to take an opportunity to say a few things, if only as kind of a mission statement for my own reference.

First off, this blog is about mulling through the big ideas in science. I've noticed that graduate school often follows a long decline from the big picture to a sort of niche myopia, where you are aware only of the things going on in your subfield. The technical details of the day to day work of research overwhelm those high minded considerations that made science interesting in the first place. There are so many exciting things happening in biological research, and cancer research in particular, that I think deserve a wider conversation.

Secondly, I want to write about the culture of science and science education. In some ways science is nothing more than a culture. It's a way of thinking about ideas, communicating those ideas and evaluating their utility. The way we do science is inherently linked to the institutions we've built up to pursue it. Better technology and better science is as much about designing the right culture of inquiry as designing the right experiment.

Thirdly, I hope to improve, if slightly, my communication of scientific ideas. I hope to make each post clear, contained and concise. For some reason the scientific writing style has become an impenetrable thicket of technical language. So many of the journal articles I have read are accessible only to the most up-to-date members of the field. They can be unreachable even to those using the same model organism, but studying different areas. To my mind this is a deep weakness. An idea is only as good as its communicability, and while it's great to be the first to discover something you've done very little if it doesn't reach the person who can use it to maximal impact. Wherever possible I hope to practice avoiding the technical language and giving the complete background.

Lastly I want to have a record of my naive youthful hopes and dreams when I'm a grizzled senior grad student so that maybe I can keep the flame alive when the going gets rough.

Biology and Black Holes

2009-11-11T09:09:00.000-05:00

I attended a talk Monday in which the speaker proposed that biology was on the cusp of a new era. This era, he claims, will see biology increasingly resembling the discipline of astronomy. We will more and more be training our genomic telescopes on small parts of the genome, building models, and testing that model against other parts of the genome.

While I don't disagree with him that this is where the field is going, I think we need to do everything we can to avoid the possible consequences. The astronomical model seems, to me, to be a black hole of correlative, descriptive analysis with no useful predictions or clear connection to human application. We risk getting lost in a biological string theory, which makes no substantive claims but which drains brainpower and dollars from more applicable efforts.

What do I mean by this? Take CHiP-seq studies, for example. These are incredibly powerful experiments that document behavior of transcription factors throughout the genome, and they reveal new interactions and possible regulatory pathways. But they often do so in a deluge, and teasing apart the specific and biologically relevant (read: predictive and useful) associations from the less specific, less relevant results can be an entire career's worth of work. In short, we have more data than we know what to do with.

I don't want to sound totally negative about biological astronomy. If we train our telescopes specifically towards disease states we may be able to sort out relationships between genomic, epigentic, trancriptional or other states and prognosis or even treatment responses. Massive biological data coupled with excellent clinical annotation could go a long way towards personalized medicine. But in the complex regulatory network of the cell it seems unlikely that any simple interpretations for these data sets will emerge anytime soon. For now, we should expect biological astrology; We can make some predictions about the future but be damned if we know anything about the mechanism.

Dear Graduate Student Professors

2009-11-05T11:55:00.001-05:00

I am currently sitting in genetics class reflecting on the quality of graduate education in science. Let's just say that the class is taught with a 'challenging' style. I'm going to list a few key requirements of good graduate school teaching that are often disregarded.

Enunciate. This is not that hard, people.
Get to know your students' names. Grad classes tend to be small, and any one of us could be a future colleague. At least make an effort.
Look at your slides before class and understand the flow of your presentation. By this I mean understand the logical progression of the ideas. Make sure that everything you need to understand a slide is presented before your arrive at that slide.
If you're explaining an experimental result or protocol, take your time. These tend to make a lot of sense after you understand them, but are impenetrable the first time you look at them. That's usually because there are a lot of tools that go into the experiment with which students may be unfamiliar. How can you understand a genome wide association study if you don't know what a SNP is?
When you plan to ask questions, make sure that the answer is actually available given the information you've presented. If students can't guess the answer then it's a bad question or you've failed to lay out the setup to the question. If a student answers your question incorrectly explain clearly but without condescension why the answer is wrong.
Do not get stuck on one slide. Your students, as interested as they may be, will start to tune out.
Balance and manage questions in class. This is an art. Don't get bogged down, but don't race through material and leave everyone in the dust. Actively ask your students about your pacing, perhaps on an individual basis to avoid peer pressure.
Vary your cadence. Show the importance of a particular part of your presentation with vocal emphasis.
Don't be afraid of the blackboard. It's a great way to draw out and clarify a point.
Try to make sure that if you're teaching without a textbook that students who get lost have a written resource to which they can refer that has complete information (this doesn't have to be powerpoint slides, but that's one good place).

Some of the above are so damn simple that I can't understand why anyone wouldn't be able to manage them. Others are not all easy to live up to, but your students will massively appreciate your efforts if you try.

PS: To add a few more...

Never ever ever condescend to your students. It's immature and unprofessional.
Avoid the adversarial model of professor vs. student.
Did I mention enunciate?

Sitting in class

2009-11-05T11:27:00.003-05:00

Hello dear readers. This is my first post.

As I'm sitting in class, I'm wondering about effective techniques for students to learn. Most of these effective techniques are not included in this class. To be fair, graduate school classes are no worse than medical school classes. Just replace one bad lecture with another. However, at least medical school will eventually allow us to take a hands on approach to the material with third and fourth year rotations whereas graduate school will never allow me to apply my skeletal knowledge of transposons in the lab.

Karmic Koalas Crash Eclipse

2009-11-04T08:34:00.000-05:00

So I took the plunge and updated my system to 9.10 (karmic koala) from 9.4 (jaunty jackalope). So far I'm happy with the small aesthetic changes and what seems to be a mild speed increase, but I was miffed to find out that the new release broke my version of Eclipse. I used the workaround found here, which worked well. Ubuntu is great and all, but its moments like this that I wonder if it'll ever make it to the mainstream.

Reference Genome(s?)

2009-10-31T16:50:00.001-04:00

Of the many interesting subjects at the genome informatics meeting at Cold Spring Harbor Labs, one of the more interesting surrounded the release of the latest human genome reference. I'll call it hg19.

The issue surrounding the release is as follows. Many people have been using the old reference, hg18, for years. Not only is it more familiar, but the coordinates have become a sort of cannonical framework on which we have draped massive amounts of auxilliary information. No doubt some programmers (myself included, unfortunately) have hard coded coordinates from the old genome into their utilities. Converting will be sort of like the switch away from 2 digit dates at y2k. It's a much smaller scale, but no less of a pain for those who have to do it.

What's more interesting is that the new genome, and increasingly future genomes, includes so called "alternate assemblies". These are regions of the chromosome for which different well sequenced individuals have chromosomal rearrangements and mutations large enough and different enough to require a completely different reference sequence. This, too, is likely to create a new mess of complications in dealing with the informatics side. Sure, your sequence matches to chromsome 17, but whose chromosome 17? Is it the "alternate" or the "reference" chr17? Both?

The plot will only thicken as the 1000 genomes project becomes more accessible to public use. Imagine not 2 or 3 alternates, but thousands! Moreover, what sorts of changes warrant an alternate assembly? And what frequency? How will sequences be mapped to these assemblies, in their ever expanding multiplicity? How will the assemblies be stored locally? It seems impractical to have one thousand 3 GB files floating around with individual genomes.

Of course an alternative is to keep the main reference and create a database of changes to that reference, such as dbSNP. The question there becomes how to manage large and unusual sorts of changes, such as large indels and chromosomal rearrangements. Moreover, won't we be apt to bias ourselves against the 'non-reference' alleles since all of our mapping algorithms will use the reference only?

The appropriate path to take, in my mind, is to abandon the present model of a reference genome as a flat file with a list of nucleotides. We need to think of it, increasingly, as a graph, with alternate paths through that graph representing individual human genomes. Such a model will allow us to map more easily to variant genotypes, and could compress the amount of hard disk space required store multiple genomes in our clusters.

We'd risk, of course, decreased accessibility to new users. A text file with a list of A's, T's, G's and C's is easy for people to understand and start to work with. That's why any conversion to a new standard would have to be spearheaded by a well funded group with the means to create a suite of conversion tools to make a seamless transition back to fasta files for those who are used to them. Such a group would also need to design new utilities for alignment, variant calling, visualization and downstream analysis (among other things). Moreover, they'd have to come up with a new coordinate system (scary) to map back and forth between old and new results.

This is no small task, but it needs to happen. It's time to stop treating all genomes as vaiants on Watson or Venter.

First Post! The War on Cancer: Silver Bullets or Atom Bombs

2009-10-29T14:11:00.001-04:00

Sitting here in the Genome Informatics meeting in Cold Spring Harbor Labs I am newly awakened to the vast broader community working on many of the scientific problems that I often feel I toil on in isolation. Moreover I'm amazed by the amount of collective knowledge and competence in dealing with the informatics problems associated with sequencing by synthesis technology specifically and genomic biology broadly.

So here we go! I'm going to take a stab and creating a public space for my thoughts and perhaps even a site for dialogue with other researchers. Which is to say: comment liberally! Correct my foolishness or debate ideas. I certainly don't expect much traffic, but leave a note if you stop in.

Now for the main course.

Silver Bullets or Atom Bombs:

In conversations with one of my previous PI earlier this week I got the chance to muse about the oncoming deluge of cancer genomics data. I think everyone in bioinformatics, and in the biology community in general, is thrilled with the enormous number of new tools now available through sequencing, but there remains the hovering question: what are we going to do with all of this new data? More to the point, what are we looking for?

Two possibilities are well known. Each stems from a sort of overarching category of cancer therapy. On the one hand you have the atom bombs. These are the old standbys of the arsenal, the drugs that blast a cell with toxic insult and hope for the best. These often target the genomic DNA, either through antimetabolites or through damaging alkylating agents. Presumably the cancerous cells, with their rapid replication, will suffer the most, but there are many victims of the therapy and the toxicity is intense.

On the other hand you have the (usually newer) silver bullets. Here I am thinking of imatinib or trastuzamab. These therapies are intended to be trained killers, entering the body and using a cancer cell's weak points to mount a targeted assault on proteins the cancer cell depends upon. Without these signals the tumors remit, often with less toxicity to the patient.

World Maps

There are two ways in which the genomic revolution could obviously help cancer treatment. The first is a sort of world map. Right now oncologists drop their atom bomb therapies based on tumor pathology, which uses information like the tumor's location and appearance under a microscope for categorization. Some categories of tumor respond very well to some therapies, others extremely poorly. But some tumor types seem to have very heterogeneous responses to the same therapy. Some patients experience full remissions while others see very little benefit. Furthermore patients differ in their ability to withstand an atom bomb therapy, with some suffering severe toxicity and dying from the treatment, rather than the disease.

For these, we need a more precise world map. If you're going to be dropping atom bombs, even as a last resort, you sure as hell would like to be dropping them in the right place. Finer scale categorization of tumors based on either their genotype or expression profiles (or both) could hone our categories and put clearer boundaries on the map. Perhaps carboplatin is a good drug for melanoma, but only if the cells have a specific cell cycle checkpoint defect. There might be no way to see such a defect just from histology.

Just as you need to have a map of where to bomb, you need a map of where NOT to bomb. You want to destroy Melanomatown but spare Bonemarrowburg. Some patients need to be spared the treatment due to toxicity susceptibilities of their normal, healthy cells. There might be genomic markers of such susceptibility in the genome.

This line of inquiry amounts to careful epidemiology and delineation of differences between tumors and individuals. No new therapies are necessary, just better characterizations of the patients and their disease.

The Golden Gun

Then there's the prospect of new targets. Perhaps now we can find new targets which, like BCR-ABL in CML, fall to a single, focused attack. To me this is the more tantalizing possibility. Perhaps we can find a whole host of tumors which have such pin point weaknesses and hit them hard. To quote Brian J. Druker et. al.

...[O]one of the major issues is to identify appropriate targets for drug development. Although the Abl kinase inhibitor has been useful for clinical concept validation, several features of CML may make the success of a kinase inhibitor as a single agent unique for this cancer. The Bcr-Abl tyrosine kinase, present in 95% of patients, is sufficient to cause the disease, and in early disease, it may represent the sole molecular abnormality. Few other malignant diseases can be ascribed to a single molecular defect in a protein kinase. (link)

Protein kinases make nice targets because of their susceptibility to small molocule inhibitors, but they are by no means the only good target possibly. With more detailed understanding of cancer genome architecture we may find the variants which, like CML, have a "kingpin" gene regulating the whole cancer phenotype. These may have been too subtle to see by histology, but shine out in either expression analysis or genome resequencing. Once identified, we can design a slew of new bullets to go into a sort of golden gun. Simple assays can be designed to determine whether a patient will be susceptible to one of these targeted therapies, and we can hit the tumors exactly where they are weak. By hitting the kingpin of the proliferation we could reduce the possibility of drug resistance and drug toxicity.

How?

This is just my conception of the field. Many people are already discussing these issues, and I don't pretend that there's a single original idea here. But it's fun to play with big ideas, and you have to start somewhere. I've sort of answered the large question of "what are we trying to do?" but I will end with a series of questions that to me have no obvious answer.

How do we focus our efforts in finding targets?
How do we pull relevant genomic/phenotypic/histologic etc. features out of an ever growing list?
How do we target kingpin changes in cancers for which there is no easy pharmacologic target?
How does cancer 'evolve' drug resistance and how do we prevent it?
How do we categorize patients and tumors with treatment regimens, given that the N decreases the more finely we partition the individuals?
How do we make genomic information available to clinicians?

I'll leave it at that. If you read this far thanks, and I hope I didn't go overboard.