The End Draws Near....

....for 2011, at least.

I really don’t have much to add about the recent charges in the UCLA lab safety case, especially given the posts elsewhere (most of which are collected at The Safety Zone here: 1; 2; 3 ). As I can easily count the small molecule syntheses I’ve done since my undergraduate days on my hands with room to spare, I am definitely not someone who can offer hard-earned advice on safety in synthetic chemistry labs based on extensive personal experience.

I will note that – obviously due to my biological inclinations – that lab safety can be just as much as insulating your experiment from you as protecting you from any hazards. Which, given one’s perspective on human nature, might be a more effective means of motivating compliance with lab safety standards.

Onto cheerier subjects….

My plan to bring up Helmholtz when Gibbs is mentioned did not quite pan out this year. I don’t think it’s going to happen. I’ll have to be contrary in some other manner in the future.

I did get back to blogging and commenting a bit this year, and I intend to keep it up next year. While the notion of doing substantive ResearchBlogging is a reasonable one, it would entail winding back the semi-regular sarcasm a bit for thoughtful commentary. I’m not sure if my system could endure the shock. It might happen, though. Having said that, if one has any substantive questions where my thoughts might be of interest, ask away.

Best wishes to all for a happy, healthy, and productive New Year! Read more!

Trust but verify.

The question of how much to trust computational methods is brought up here at Chemiotics II. My answer is that it depends on what one is looking for in the first place.

If one is looking for some sort of completely accurate and precise way to have all biological phenomenona fall out of "first principles," well, I wouldn't hold my breath. Of course, I don't think anyone is really waiting for that. At least I hope not. I believe my feelings on these sorts of issues are best described by personal experiences I've had with computational methods.

In grad school, I had an interest in this one mid-sized protein (somewhere between 40 to 60 kDa) that was known to bind this particular ligand. There was a crystal structure of the protein with and without ligand, although of course it was hardly the entire story (which is why it was the subject of my research attentions). In any case, collaborators did some MD simulations, and it was consistent with what we had found and was known. In their next bit of work, they mentioned that they found something new regarding the mechanism of ligand binding. This was going on the same time as I was doing some work, and as it turned out, my data did not rule it out. And so new research was inspired for those who took up the project after I left.

Currently, I am embroiled in a sordid and complex tale of transmembrane signaling involving the receptor and varying amounts of soluble cytoplasmic proteins that propagate that signal. There was a fairly recent paper detailing MD studies of the signaling process. Well, part of it, I suppose - huge chunks on either end of the transmembrane receptor were not included, and none of the cytoplasmic proteins that bind and are modified by the receptor were included in the study. Certainly a daring attempt, but it's hard to get too worked up over it when it doesn't resemble anything that I actually work with on a daily basis.

In short....I think properly used, it can be a useful way to bridge what is measured experimentally with the metaphors we use to describe processes. (For example - people love using descriptions involving simple machines, but what is actually measured are thermodynamic or spectroscopic quantities. Of course, "force spectroscopy" looks to change this, but when you yank apart a protein, you are no longer just gently playing around at kT or sub-kT conditions to see what kinds of deformations you get naturally or as a response to some stimulus. Anyway....) Certainly, for small enough systems, I am inclined to give them a proper reading, and in cases where the system might be larger but is somewhat well characterized, the same applies. In giant systems where they toss out a number of critical components or oversimplify to the point of absurdity, I am generally far more skeptical.

Merry Christmas to those who celebrate, Happy Hanukkah to those who celebrate, and a delightful winter holiday season to the rest. Read more!

Chemists, controls, and computing.

I have no experience with drug discovery, so I suggest one reads the excellent commentary offered over at The Curious Wavefunction and In the Pipeline inspired by a recent article on the role of computer simulation in pharmaceutical research, presuming that they haven’t already done so. What I thought was interesting enough to post about in response is in Wavefunction’s blog post.

They are reluctant to carry out the kind of basic measurements … which would be enormously valuable in benchmarking modeling techniques.

Methods development research can be difficult to support. Even obtaining modest funding can be difficult. It’s one reason why it can usually seem incremental in nature, as it’s easier to scrounge a few small devices or specialty materials to use with existing research infrastructure. This one is near and dear to my heart, as I have two such projects going on at the moment, and a third which is still in the planning stages. Unfortunately, it’s not the kind of stuff one could convince people it needs to be funded and generously at that. That was really more just me griping. But that is par for the course for me here at my blog…..

Unlike chemists, engineers are usually more naturally inclined to learn programming and mathematical modeling. Most engineers I know know at least some programming. Even if they don't extensively write code they can still use Matlab or Mathematica, and this is independent of their specialty (mechanical, civil, electrical etc.). …The lesson to be drawn here is that programming, simulation and better mathematical grounding need to be more widely integrated in the traditional education of chemists of all stripes, especially those inclined toward the life sciences.

I of course agree, but am inclined to mention a few things. This may be an artifact from my recollection/experience and is no longer the case, but I’ve seen a tendency for computational methods & applications courses intended for chemists to be heavy on the typical computational chemistry aspects (basic electronic structure calculations, a dash of MD, some molecular mechanics) along with a fair bit of introductory programming. Not that there’s anything wrong with that….but wait, actually, it is problematic.

I would think a more useful course might still contain some introductory programming and some of the typical computational chemistry, but I’d like to think that one could also take the time to introduce the students to chemo/bioinformatics as well as a module on proper data fitting. Of course, it might be claimed that it’s better suited for an upper-division chemistry laboratory, which would be fine. The important thing is to get people weaned from MS Excel and to actually start fitting data, not algebraically torturing your data until it’s in a format that can be linearly plotted and then fit with Excel.

Also, given that I have this notion of this course being something that all students will probably find useful in the future, the programming and software elements should be those that will easily lend themselves to a broad range of applications and uses in the future. I would imagine that introducing students to something like Origin or Igor Pro would be useful, as well as (re)introducing them to Mathematica, Matlab, Maple, or other comparable software. While the power of Fortran is well established for the numerical-heavy applications in computational sciences, I feel it would be better to have students introduced to something like Python. You can leave the Fortran for those who want to do the computationally intensive theoretical chemistry, while I’m sure the majority can use Python as a useful tool in their work.

This above is clearly influenced by personal biases (I'm a bio/physical chemist who is in the process of adding "systems biologist" if he keeps it up for much longer), but I think that sort of mix in a "computers & chemistry" course would serve a good cross-section of the chemical community. Any and all commentary, feedback, suggestions, and brutal eviscerations of my points are welcomed. Read more!