Is it enough to study individual molecules?
May 31, 2006
One could easily expand this question to encompass all life scientists, or perhaps all scientists, but in essence that is the question that Alex Palazzo tries to answer. In his opinion, biologists tend to be too molecule centric. That is certainly true, but his very doubt is a sign of the times.
Back in the day, we used to try and find out all we knew about a particular protein, and perhaps its “neighboring” system, since there was so much to find out about each and every protein. That was pre-genomics. In this day and age, the question we frequently ask is different. It is no longer enough to study a molecule in isolation. The context in which one studies molecules has becomes very important, e.g. the processes they are actively involved in. Perhaps the next generation of scientists will be experts at processes and not just on one of two constituents of a process. To some extent, I am talking about what Alex calls “Big Biology” and my interpretation of “Systems Biology”. How does everything interact, and what are the various functional pathways that they form. The body is complex and being able to understand how a change early in a process makes an impact 10 steps down is going to be critical to how we address and treat diseases. Before we can ask the question about the nature of an individual’s biology, we need to understand more general concepts and we have not reached that point yet.
This should, in theory, change the makeup of a lab. It is no longer sufficient to have a research group where everyone has similar skills. Labs need a mix of complementary skills and expertise, since it is very difficult for an individual to answer the complex questions that we need to address. The result will be a set of students and post-docs who are used to working in teams and are skilling in multiple systems and disciplines. That would be a welcome evolution of the life scientist.
Addendum
The Omics World lists a couple of papers that compare a reductionist approach to a systems approach. The papers are a must read
Technorati Tags: Biology, Learning, Science, Life Science, Molecules, Systems Biology, Synthetic Biology
Multiscale modeling of MscL
May 31, 2006
Yesterday, I got my latest content update from the Biophysical journal for my keyword search for CHARMM. The first article that caught my eyes was a paper by Qiang Cui and co-workers, a paper titled A finite element framework for studying the mechanical response of macromolecules: Application to the gating of the mechanosensitive channel MscL. The paper looks at the gating pathways of mechanisensitive ion channels of large conductance (MscL) in two bacterial using Finite Element Method (FEM), a technique routinely used in engineering to solve complex elasticity and structural analysis problems (e.g. for modeling the structural integrity of airframes). Using such methods on biological systems is part of a new trend that tries to go beyond the nano/micro-second timescales that traditional molecular dynamics (MD) methods are limited to.
Qiang Cui, whom I have known for some time, is a CHARMM developer and has taken his interest in multiscale phenomena to new heights. This paper presents a framework for multiscale modeling, specifically for mechanosensitive channels (I wonder if more generalization is at all possible). If I had to pick a weakness in the paper it is that the system is very specific. That said, I think the current study is just a first pass at building more general methods for studying such systems at multiple length and time scales.
MS channels are interesting systems since the kinds of atomistic simulations traditionally used to study biological processes are not ideally suited to studying mechanical forces and multiple time scales. The simple models developed by Cui and co-workers include only the TM helices as elastic rods embedded in an elastic membrane . They find that even with such simple representations, the results are in fairly good agreement with existing data.
The FEM models are parameterized using standard molecular mechanics energy functions (using CHARMM and several implicit membrane models in CHARMM). The key is the interaction potentials between FEM components, which are represented as simple pairwise potentials. The FEM calculations themselves are done using ABAQUS
I won’t go into the details of the theory or the results. For me the most important aspect of the paper is using FEM to study a biological process. True multiscale modeling has not yet found its way into the arsenal of most scientists, but I suspect that with the developments that have been seen in the last couple of years, the day is not far that those of us interested in studying biological phenomena will have more than traditional atomistic MD simulations and quantum mechanics to rely on. Personally I think in a few years, those interested in studying protein structure and function will require a healthy training in multiscale modeling (quantum chemistry, molecular dynamics, coarse grained simulations, continuum dynamics), bioinformatics, and mathematical modeling.
Further Reading
CHARMM Development Project
Cui Group Homepage
Mechanosensitive channels at UIUC
Multiscale modeling at Utah
Technorati Tags: Mechanosensitive channels, Molecular Simulation, Molecular Dynamics, FEM, Multiscale Modeling, Large Scale Simulations, Protein Structure and Function
IBM in the crosshairs
May 27, 2006
Robert X. Cringely has written an article on the malaise at IBM (which I found by way of deal architect). The line in the article that jumped out at me was the following
Unfortunately, I see IBM as a place run by salespeople and project managers with a sell and install mentality, even in services. There is no technical leadership, innovation or proper understanding of our customer’s needs and requirements. The architecture profession is dysfunctional and cannot remediate itself. These factors may change, but not in the short term and when it does it is likely to be brutal, and I’m not patient enough to wait around until it does.
The troubling part about this statement is that I have always admired IBM as a company that continued to do some of the most innovative research (and I know a few people at IBM still engaged in some wonderful work). If the company is lacking technical leadership, it is a move away from its core values.
Fundamentally IBM global services is a great idea, and with technology innovation at the heart of IBM capabilities, the services business has the potential to thrive in the long term. However, like many businesses today, it would appear that the long term good is being sacrificed for short term shareholder value. The results of such actions rarely have a good ending, so I hope IBM can get its act together. As someone who has admired IBM for a long time, I would hate to see IBM go the SUN or SGI way (to list too former hardware titans). Especially since, IBM is one of the few hardware vendors that actually understands the needs of BioIT and has scientific interests in life sciences and healthcare (I have been watching blue gene since before the machines were ever made). I can only hope that reality is not quite as bad as the doomsday scenario painted by Cringley, but something tells me that he is right on the mark.
Further Reading
Cringley makes IBM cringe
More from Robert X. Cringely
IBM = Innovative Business Model
IBM’s research juggling act
Technorati Tags: IBM, Robert X. Cringley
Buying hardware?
May 26, 2006
I would hate to be making IT decisions these days, or trying to decide what platforms to support for any forthcoming software (wait, I think I do need to do that). Recent changes by HPC vendors complicate an already confusing hardware landscape. Dell recently anounced that it will start selling four-way servers with Opteron chips from AMD. This was a move that Dell had to make, although the company seems to be playing nice with Intel, by not adding AMD chips to its desktop workstations. This brings Dell in line with IBM, HP, and Sun. What I dread is the day that the hardware choices will include 1-way servers, 2-way servers, 4-way servers with the possibility of single core, dual core and soon quad core CPUs. Not to mention that multicore chips will include dual core Xeons, Opteron and the new Intel core duo chips (the Xeon LV). Add to that Itanium-based machines, and things get even more complicated. Choice is good, but sometimes too much choice can be a headache.
My product marketing colleagues are probably spending a few sleepless nights trying to figure out what configurations to support, especially with the licensing implications of multicore chips. Luckily, most of the hardware discussed above is compatible with each other, but even slight differences can lead to problems. As a software developer, keeping pace with hardware developments gets harder all th time. At least the number of proprietary systems and hardware is somewhat limited for most applications, so there is one less variable. The problem arises primarily because we have to cater to three kinds of users. Those that wait a few months before adopting new hardware, giving developers sufficient time to QC new configurations, those that take years to change, often staying on hardware that could be considered obselete, and last, but usually the ones that cause the most trouble, those that adopt the latest technologies as soon as they become available, and get equally angry when their software doesn’t work the way it did before.
Technorati Tags: Hardware, High Performance Computing, Vendors, Product Marketing, Technology
Nodalpoint on grid computing for bioinformatics
May 26, 2006
In prior articles on grid computing, I have voiced my concerns on the potential applications of grid computing for pharma projects. Nodalpoint has an article on grid computing for life science that references articles by Tim Bray from Sun and Jim Gray from Microsoft. At the risk of sounding like a broken record, I maintain that while the grid computing economics that Jim Gray talks about work for certain cases, there are a number of cases where the economics break down. If you are doing routine crunching of genomes on an ongoing basis (annotation, etc) and essentially performing data collection, then grids make a lot of sense, at lease loosely distributed ones. The microarray data analysis that Duncan mentions is an ideal candidate for grid-based deployment. On the other hand, I am still not convinced that all in silico experiments are conducive to grids as opposed to clusters. The latter give you more control, more reliability and in the end probably help you achieve your goals faster.
From personal experience one aspect of grid computing never gets enough thought. What do you do with all the data that grid computing efforts routinely create. Just sifting through all the data can become a nightmare. Which is why, I think grid deployments work best for routine data generation projects, since then the scientist can focus on data analysis and let the grid continuously generate data.
Technorati Tags: grid computing, BioIT, Bioinformatics, Cluster Computing, High Performance Computing, Scientific Computing
