Plausible Accuracy

When I started out as a graduate student, in the dark distant past, I chose to work on a protein that we didn’t yet have in our lab.  One of my first goals was to acquire the gene and clone it into our expression system.  After reading some literature, I found one group who had mentioned getting the gene from another, and so I thought I had my opportunity.  I emailed the group who is on record as having supplied it and asked for the gene.  They replied “sure, no problem”; I waited.  Months.  Occasionally I would send off another email, but I didn’t want to seem too pushy.  They were doing me a favor, right?  In the meantime I worked with the mouse version of the gene.

Finally, the envelope came with the gene.  They didn’t provide any real information, but I went ahead and designed my PCR primers and got to work cloning it.  For months.  For some reason, my cloning reactions just weren’t working really well.  I struggled with PCR, digestions, ligations; it seemed like every step of the way was bogged down for some reason.  Eventually I managed to wrangle the gene into a vector and get a good sequence read.  At this point it became abundantly clear why I had been having so much trouble - the end of the gene was missing.  It turns out the lab I had gotten it from had used an enzyme in their own cloning that clipped the sequence short.

In retrospect, I probably should have figured this out much sooner.  The clues were all there, but as a young graduate student I was sure that an established lab would send me the proper gene, and I was just doing something wrong.

It turns out that around this time, another graduate student mentioned that they had bought the gene for their protein for something like $80 (the fee for this particular repository has since been raised to $120), which is just about as close to free as you could hope for.  It turned out that the same place carried my gene.  Of course I bought it, and within weeks my cloning was successful.

When people like John Wilbanks talk about developing these repositories, this is the type of situation they are looking to improve.  The old system, of asking a “favor”, little to no verification, and no real motivation for expediency or quality control is really shockingly bad.  It’s amazing to me that it’s taken this long to sort of start generating significant interest in validated, standardized, open repositories.  The clones, cell lines, mice, etc that we generate in great quantities need a better method of sharing and distribution than some antiquated version of quid pro quo.

This weekend, Mrs. PA and I went out to dinner in town (where, coincidentally, I had one of the best beers I’ve ever tasted).  During the meal, we had a wide-ranging conversation on the difficulties of running a successful lab group.  The training you get as an undergraduate, graduate students, and post-doc does little to prepare you for many of the duties you undertake as a professor.  Teaching, grant-writing, and personnel management are areas that you spend a lot of time working in as a professor but likely have little to no exposure to prior to this position.  Indeed, the level of multitasking it takes in order to be effective as a Principal Investigator at a major research institution is rather astounding.  What tends to happen, in many cases, is that some facet of the position is left to its own devices.  Often this is the personnel management side of things.

We realized that there is already a position, prevalent in industry, which could help ease the burden on professors - the “Principal/Senior Scientist” job.  I did an internship at GiantPharm one summer, and worked in a small group.  There was a leader of the group, but his office was actually in another building on a hallway with other group leaders.  My interaction with him was sort of minimal.  I did, however, spend a lot of time talking to my immediate supervisor.  He was a long-time employee, Ph.D., and incredibly intelligent.  If I was stuck on a task or needed further direction, his office was always open.  Since he was doing research of his own, it was easy to chat with him informally about the work and hash out new ideas.  If I were a professor, I’d love to have someone like this in the lab.

In academia, there are sometimes “Research Scientists” working in a group.  In my experience these tend to be glorified (more or less permanent) post-docs.  They are focused on their own project, and could often not care less about mentoring graduate students or ensuring that the lab is running smoothly (as long as it doesn’t significantly impede their work they are ambivalent).  It’s worth noting that post-docs themselves frequently have a similar attitude.

I think that there is some room here.  Why not delegate some of the roles typically shouldered by a single P.I.?  For instance, the P.I. can focus on “the big picture” (where is the research going, what are our major findings, what is going on in the community), getting money, and their teaching duties.  In the meantime, you can bring in a scientist to be the “research lead”.  By this I mean the person who is in the lab working on a project, but who also oversees the day-to-day activities.  If a graduate student is having trouble getting their affinity column to work, they can go to the Scientist.  This person could be responsible for some of the management of the lab as well - if a student isn’t showing up to work, they can talk to them and/or elevate the situation to the P.I.

Now, I’m not a professor.  I’m sure there are some issues with this plan (or else why wouldn’t it be implemented).  Some that come to mind:

• Funding - you are going to have to pay this Scientist more than you pay a post-doc.  $50,000/yr?  Somewhere in that ballpark is my guess.  It’s roughly equivalent to one post-doc plus an additional graduate student.  I don’t think that this is too onerous.
• Appearance of laziness - Will other faculty members think that you are unable to “handle” being a professor if you have to hire someone else to share the workload?  I’m not really sure about the answer here, but I’d hope this could be minimized.
• What about the Scientist’s career? Won’t they get unhappy and leave?  In my perfect world, the candidate would be someone who has completed their Ph.D. but is uninterested in some of the aspects of joining a faculty.  Perhaps they just don’t like writing grants and want to work at the bench, but aren’t fond of industrial work either.  There are people out there like this, trust me.  They would be thrilled to have a position like the one I’m talking about here.

I’m sure there are other problems, and I hope you’ll bring them up in the comments.  If you are in a faculty-like position, I’d really like to hear your thoughts on this.  Do you see the utility of hiring such a researcher?  Why is it not done?

Over the end of last week, I noticed several items coming down the RSS tubes that seemed to be involved with the permission barriers we place on scientific data.  These posts also seemed to be interrelated as well.  At the time I couldn’t give them the attention they deservered, so I filed them away for later digestion.  I’d like to discuss them here now, at risks of kicking an anthill that seems to have settled down a bit over the weekend.

As far as I can tell, things seemed to start when Chemspider chose to license their data under a Creative Commons license.  This is obviously (from my point of view) an attempt on their part to do the right thing - ensure that their data is freely available, and to give them some controls to “enforce the freedom”.  Then the wonderfully muddy communication medium of the internet kicked in, and people started getting angry at one another.  It seems that Peter Murray Rust published (somewhat erroneously) a conversation between himself and John Wilbanks.  This conversation was taken somewhat out of context by the folks over at Chemspider, and the ball was rolling.

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Roderic Page (and I) want to know!

Some time back I surfed around trying to find the original source, but didn’t have any luck turning it up.

The Baker lab at the University of Washington has been influential in the development of computational tools to model protein folding.  They are most well known for the ROSETTA package, which does a fantastic job in many cases of predicting a protein fold based on fragments of other known structures.

They have now developed a protein folding game, of all things, called FoldIt!.  The user registers for an account on the website and downloads a program which runs the game locally.  As of now they only have Windows and Mac clients, but I got the game running under Wine no problem.

FoldIt! presents the player with “puzzles” which involve resolving steric clashes in the proteins.  The easy tutorial levels teach you how to play the game (which mostly involves clicking and dragging on different parts of the protein), and you’re guided by a funny cartoon version of Dr. Baker himself.  You get points based on how “good” your solution to the puzzle is, and complete a given puzzle when you cross a certain threshold score.

It’s sort of fiendishly fun to play, although maybe that’s just because I’m a biochemist.  It also seems like it would make a GREAT learning tool for use in biochemistry courses.  You can form groups as well, so it might be fun to make a group out of your class and see how highly they can place.

I’ve got about 70 pages written on a document that I call “my thesis”. The problem is, I hate it. I’ve written it all in fits and spurts, jumping around from one section to the next. Some days I’ll write pages and pages and it seems like it’s going really well, and other days I’ll spend all day staring at emacs and not getting anything down. Lately it’s been much more of the latter.

I decided to give something a shot. I fired up OpenOffice and set it to full screen web view. This gives you a glorious screen of space to write in, without any distractions. I just started writing, trying as hard as I could to keep things like formatting, citations, paragraph structure, and above all sections out of my mind. I want to write something that flows.  Maybe it’s a crazy idea.  A thesis is a really large document to write from start to finish, and the nature of research (especially my research) makes combining the whole thing into a fluid narrative tough.  Call me stupid, but I’m going to take a stab at it.

I’m not sure what this document is yet, to be honest.  It may become part of my thesis.  It may just be a convenient way to help me organize my thoughts before I write them into the official document.  It’s just as likely (more likely in some ways) that I’ll give up on this a week from now.  Until then, I’ll be posting it here on Plausible Accuracy for you to read and comment on.  Any feedback is greatly appreciated.

I’m planning on just posting chunks whenever they get to some modiucm of “done” - that is to say more or less when one train of thought has been laid down.  I’m not editing this as I go (besides the most obvious of typos), not including citations or figures.  The idea is just to write.  I’ll post links to all the sections somewhere as I add on.

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I’m still testing out some of the advanced features of using Tachyon to render nice images of biological macromolecules. I came across these beautiful images of bacteria which are able to consume radioactive waste, and decided to tinker a bit to see if I could get something similar out of VMD.

First of all I loaded in my molecule and set it up similar to the exercises from the other day: white background, surface representation, diffuse material. I also added the Depth Cue feature of VMD, which adds a fog which increases in density with depth. This helps to add a bit of a 3D feel to the representation. I also played around with the various lights, settling on having lights 0 & 2 on.
I rendered the image with:

"/usr/local/lib/vmd/tachyon_LINUX" -aasamples 4 -rescale_lights 0.3 -add_skylight 0.9 %s -format TARGA -o %s.tga

Note: this takes about 8 minutes to render on my laptop at about 700×700 resolution.
If my understanding is correct, this should give a scene that is dominated a fair bit by the skylight parameter, and this is more or less the case. The image, while interesting in some ways, is far too bright!
Let’s drop the skylight down then:

"/usr/local/lib/vmd/tachyon_LINUX" -aasamples 4 -rescale_lights 0.3 -add_skylight 0.6 %s -format TARGA -o %s.tga

Well that darkened the shadows a bit, but the overall image is still way too bright. How about dropping the lights?

"/usr/local/lib/vmd/tachyon_LINUX" -aasamples 4 -rescale_lights 0.1 -add_skylight 0.6 %s -format TARGA -o %s.tga

Well, still far too light. What’s happening is that the depth cue fades the image to the background color (in this case white) as it goes. Let’s drop the depth cue density in order to cut back on the lightening. This setting is found in Display–>Display Settings. I adjusted it to a value of 0.15, still using the Exp2 function for the density. When I rendered this (using the same settings as the last one above, it looked OK, but not fantastic. Mostly it was just “flat”, if that makes sense - not a lot of visual appeal. I rescaled the lights back up to 0.3, and this was better.

Something still isn’t “there”, though. To be sure, the tachyon renders look nice, but I just don’t feel like this is the best that can be done. I’ll have to keep toying with it.

Those not intimately involved in science sometimes think that a given researcher is fully versed in the entire body of knowledge their field has ever produced.  For instance, since I am a biochemist, I should be able to perform MacGyver-esque transformations of household products into psychedelic drugs at a moment’s notice.  Also, since I’m a biochemist, I am fully familiar with the migratory patterns of african swallows.  Of course those of us who are involved with science know that this is definitely not the way of things.  In truth, science is incredibly specialized, almost to the point of balkanization.  Even when I go to our weekly biochemistry seminar, at times it can be a stretch to wrap my head around anything deeper than a basic understanding of the work being presented.

Of course, the reasoning for this is that the things we study are remarkably complex, and it’s often necessary to immerse yourself deep into the intricacies of a given system in order to make advances in understanding.

The problem that I have with this specialization at the moment is that it makes finding a job pretty tough.  It’s abundantly clear from most postings that the hiring party has a very specific candidate in mind that has a very particular skillset matching the duties they will be expected to perform.  As a job candidate, this makes me feel like I’m locked into doing the exact same types of experiments on the same systems indefinitely.

Professors seem to have some ability to break out, but it’s still tough.  In some ways, they are even more restricted - funding agencies aren’t very likely to give someone money to do something that they’ve never done before.  On the other hand, once the money is in hand, the investigator has more leeway to use whatever tools are necessary (including those that they might not have a history of using before).

My question is to those who are in science - how do you deal with this in finding jobs?  Is this a problem in your field?  If you are in the position of hiring, do you always look for candidates that will be ready to run your experiments on day 1 due to prior experience, or do you just look for the best overall candidate, even if they may need some training?

Who completed the requirements for her Ph.D. in Microbial Ecology today.

Peter Suber has for some time kept up a tireless catalog of things Open Access at his blog Open Access News.  His efforts to keep this resource as comprehensive as possible have been commendable, but given the growth of interest in Open Access, it’s clear that it’s a lot of work just to try and keep track of everything.  In order to crowdsource some of this work, Dr. Suber and others have started the Open Access Directory, a Wiki which can be edited by registered users.  Go sign up!

As with any young wiki, the content as of this writing is a bit light.  In time of course it will only grow, and should become a very valuable resource for those of us interested in expanding the adoption of Open Access.  Also, it’s one of the few places in which my anonymity is completely blown, so if you’re dying to figure out the identity of the mastermind behind PA, you should be able to figure it out over there.