The Big Switch
January 3, 2008
I was lucky enough to get an advance pre-release bopy of The Big Switch, Nick Carr’s new book on the socioeconomic impact of the today and tomorrow of computing. For a couple of weeks I have struggling with how to review the book. I was going to write a long review, but I believe that does not justify the diverse range of ideas that the book generates, so what I am going to do is serialize it and address different sections of the book over multiple posts.
I found Nick Carr via Vinnie Mirchandani’s blog, and realized that this was the same person who wrote the HBR article IT Doesn’t Matter, since converted into a book. As a great believer in the power of computing and the new web, my initial reaction to Nick was rather negative, but over time, I have come to grudgingly respect his thoughtfulness and ability to present a point. We might not always agree, although we seem to in the utilitarian potential of the web, but his blog is one of my must reads.
I will add one thing though. Carr is a great columnist and blogger, but I am not so sure his style necessarily translates as well into book form. If you approach the book as a collection of connected essays, it reads a lot better. The book is a must read though if you care about the power of computing, the web and how it might impact our lives in the future. Material is presented both in a historical context, but also speculates rather intelligently about our future and references abound.
Hold your breath. The next week is going to be busy.
Technorati Tags: Nick Carr, The Big Switch
Eigenfactor: Ranking and mapping scientific knowledge
October 7, 2007
Yesterday, I proposed that Google Scholar would benefit from a Plus Box. In that post I alluded to Eigenfactor, a wonderful way of evaluating the network of scientific information, with publishing at its heart.
I had the pleasure of meeting Carl Bergtrsom from the University of Washington at Scifoo, where he showcased Eigenfactor. I remember sitting there, my head churning, admiring how wonderfully his group had implemented it, and the potential the service had for scientific publishing and finding scientific information
The Eigenfactor website is rich with information, but I will pull out some of the aspects that are of most interest to me. The key to Eigenfactor is how it evaluates scientific information, journals in particular. First, and perhaps most importantly, the algorithm uses a holistic network view of the publication universe
In addition to journals, Eigenfactor also lists newsprint, PhD theses, popular magazines and more in the social and natural sciences. I’d like to get a better understanding of how listings are chosen and how the reach can be extended to blogs, wikis and other sources of scientific information, since they are often better than popular magazines.
As the figure shows using this kind of approach does a much better job of linking across fields and once again emphasizes the holistic approach that Eigenfactor takes
Perhaps most importantly Eigenfactor adjusts for citation differences across disciplines. This has always been one of my pet peeves. There are certain fields which have far smaller reach and a much more niche audience, but the journals in that field are the ones that people care about. Unfortunately they are often not easy to find unless you know what you are looking for. The best explanation comes from the Eigenfactor pages
The average article in a leading cell biology journal might receive 10-30 citations within two years; the average article in leading mathematics journal would do very well to receive 2 citations over the same period. By using the whole citation network, Eigenfactor automatically accounts for these differences and allows better comparison across research areas.
You can search this network at multiple levels including a list of journals for any publisher, e.g. NPG.
Carl’s group has also added some wonderful ways to visualize the information. If you do a search for biophysical journal, you can get to the following information
In addition, there are some cool dynamic visualizations of maps, based on work done with Martin Rosvall, e.g. (for the field Molecular & Cellular Biology)
The slider allows you to drill down into a network. All the circles are clickable and change the focus of the network.
There are also some really cool stats
So where am I going with all this? As I mentioned in the Google Scholar post, Google should work with Carl to incorporate the ideas behind Eigenfactor into Google Scholar. The added visualizations would be cool as well, and as this is continuing work, Google could benefit directly from any enhancements being made.
However, nothing is perfect, or complete. I feel there are some new areas that Eigenfactor could explore. How about author level relevance and influence? What if I want to explore the network around a specific citation, a network driven by relevance. Two citations could be linked, but not for the purpose that interests me. Following the citational graph could yield some very interesting information.
As I mentioned earlier, extending the scope of Eigenfactor to cover resources like OpenWetWare and Useful Chem would also be ideal, given the increasing role of such resources in science.
This review doesn’t do Eigenfactor enough justice, so I encourage you to try it yourself.
Technorati Tags: Eigenfactor, Carl Bergstrom, Scifoo, Science Publishing, Relevance
From tech to biotech: One person’s journey through radio
September 9, 2007
As a long time listener of Tech Nation and Biotech Nation (mostly online these days), when I got a chance to review Moira Gunn’s new book I jumped at the opportunity. Not only was this going to be a rare formal book review, but also a book by someone whose show I like and best of all, a book on an industry I can claim to know something about; Biotech.
Welcome to Biotech Nation: My Unexpected Odyssey into the Land of Small Molecules, Lean Genes, and Big Ideas is a book that talks about how Biotech Nation was born and what Moira Gunn gleaned along the way. First things first; if you are expecting a book that goes into technical detail about the inner workings of the biotech industry, this is not where you want to go. If you want to get a first hand look into the wonders of the world of biotechnology written in a breezy, easy to consume style, you have reached the right place. Welcome to Biotech Nation has a pace and delivery that will appeal to many, especially those not familiar with biotech. Via her radio program, Dr. Gunn has been able to get access to some luminaries in the biopharma industry and others associated with it. The stature and roles of some of the people she has interviewed, and how she interacted with them, makes for some fascinating reading.
More than anything else, Welcome to Biotech Nation is a book of anecdotes. It often starts in one place and then goes on to a personal account or a related incident before returning to where everything started, bringing the thought to its logical conclusion. As someone who came to biotech from the outside looking it (albeit with a very strong tech background), it is very interesting to read how Dr. Gunn reacts to certain pieces of information. The anecdotes, often around the industry’s annual BIO showcase, range from meetings with a lord to stories about Brooke Shields and her attempts to have a child to stories about India and Africa. Through all of those, we learn a little bit more about the industry, , its global impact and implications, its complications, how things might get discovered, and some of the breakthroughs. The chapter on The amazing Chakrabarty, where she talks about her interview with Dr. Ananda Chakrabarty is a classic example of how she manages to fill in a lot of information around one interview.
Dr. Chakrabarty was the first person to be awarded a life science patent, for genetically engineered bacteria. In this chapter, using Dr. Chakrabarty as a backdrop, Dr. Gunn manages to discuss patents, the WTO, globalization, and the Indian generics industry (which she talks about a few times in the book). All this without making things so jargony that the layperson would be left completely befuddled. In the chapter about Brooke Shields, one learns a little more about sex selection, just one of many such examples in the whole book. The last chapter, Lesson’s Learned is one of my favorites, as it brings some points home, e.g. you don’t have to have a PhD to work in the biotech industry.
My take away from reading the book (I don’t want to add too many spoilers) is this. if you think you know anything and everything about the biotech industry, you should read it. You will walk away with a few laughs about people who you just might know or nodding your head about some of the issues and quirks with the biotech industry. If you are not an insider, the book provides a breezy, casual look at an industry that most outsiders just don’t understand. Once you pick up the book, you are unlikely to put it down, a rare statement to make for a book about technology and an industry not always considered riveting reading.
I had a chance to speak briefly with Dr. Gunn, and it was evident that she had a lot of fun writing this, her first, book. I am glad she chose to make it personal and anecdotal. There are other books which do an excellent job of dissecting the industry. This one does a great one of making it just a little more accessible.
Footnotes:
Welcome to Biotech Nation is published by the American Management Association
Moira A. Gunn, Ph.D. is the host of BioTech Nation and Tech Nation, which airs weekly on 200 public radio stations, on NPR channels on Sirius Satellite Radio, and internationally to 133 countries via American Forces Radio. A former NASA computer scientist with a doctorate in Mechanical Engineering and a patent in Human Nutrition Research, she has recently been named a Science Laureate for her contributions to science journalism.
Technorati Tags: Moira Gunn, Biotech Nation
Nature.com gets a facelift
August 14, 2007
… and I like it. Nature Network, a social network for us science types is featured prominently as are Nature Precedings and Connotea. The launchpad on the right features Scintilla, one of my favorite resources.
Nature continues to innovate. Their podcasts are already one among the best there are and I noticed that they include video for select articles. All that’s missing, a video podcast (perhaps a documentary format, or a best of postgenomic this week). Adam Rutherford, are you listening?
Talking of Postgenomic, it’s conspicuously missing. I wonder why? The other thing that would be good to see; a dropdown for all their journals.
Whoever did the icons deserves a raise.
All in all, an excellent update. I might even add it as one of my default Firefox tabs. Next step - A Google Gadget.
Technorati Tags: Nature.com, Nature Publishing Group, Web 2.0
Coarse graining molecular simulations
February 11, 2007
In the past I’ve talked about Elastic Network Models (ENM) at bbgm. These can be looked at as a coarse grained scheme that allows scientists to look at motions in time scales beyond the traditional realm of atomistic molecular dynamics simulation. In fact, over the past few years, the trend towards multiscale simulations has increased significantly. This is partly due to the fact that increased compute power is making it possible to study larger systems at longer timescales, but also because it is becoming increasingly necessary to approach a problem from multiple directions. Multiscale modeling can be simplified into the following scheme (in length scale)
Electrons (Quantum mechanics) –> Atoms (Molecular mechanics) —> Segments/Reduced representations (Coarse grained/Mesoscale) –> Continuum/Bulk (Continuum dynamics/Finite element methods).
In addition to the length scale, these are also fairly representative of the addressable time scale (fs –> ns –> ms –> higher respectively) In this post, I will talk about the move towards addressing longer time scales and larger systems using various coarse graining techniques.
Normal mode analysis (NMA) is a common way of trying to look at longer timescale vibrational motions in molecular systems, e.g. proteins. However, NMA is computationally expensive, which limits the sizes of the systems to which it is applicable and the number of modes that can realistically be calculated. Qiang Cui and others have developed Block Normal Mode methods which use a sparser Hessian and can be applied to larger systems which provide some computational advantages. Personally I think BNM type approaches might be very relevant to the sort of MM-PBSA methods described in the previous article. However to study large scale cooperative motions ENM is probably the best method that I know of.
One of the problems that began to interest me a couple of years ago were ways that people were using to take results from atomistic simulations, usually MD simulations of biomolecules or polymers and transfer that information into coarse-grained models that could be used to study molecular machinery and look at mechanical properties of biomolecular system. Much of that interest came after listening to a talk by Greg Voth. Protocols to go from quantum mechanical representations of model systems and transfer those parameters to classical molecular mechanics force fields have been fairly well established for a while now. On the other hand, the jump from molecular systems to the kinds of coarse graining schemes used to represent more macroscopic motions and properties are relatively less well understood, especially for biomolecular systems. Mesoscale approaches for polymer mixes have been used for a while, but are still a work in progress (Disclaimer: I was actively involved in this area towards the end of my stay at Accelrys and some of the most knowledgeable people in the field still work there). For biomolecules the systems create their own challenges and a chunk of the community is actively looking at this problem.
One of the key challenges in any such coarse-graining scheme is the need to retain as much information content as possible without losing the advantages that coarse-graining brings. The general approach is to reduce the system into some coarse-grained representation and then represented the forces between the reduced segments in a meaningful way. Much of the challenges lie in part two. What I learnt from Voth’s talk and subsequently from reading his papers was the lack of information content in most methods in place. The other thing that started becoming apparent was how difficult generalizing CG potentials was, if at all. Voth’s approach to coarse graining is a method he calls Multiscale Coarse-Graining (MS-CG), which is used to systematically derive a coarse-grained potential from atomistic-level interactions.
The method used by Voth is called force matching, a method that was developed for condensed-phase systems. The system uses a trajectory to derive a pairwise effective forcefield. The method is agnostic of how the trajectory is generated, the most common method being atomistic MD trajectories. I won’t go into the detailed derivation here, but here are some of the features that jumped out at me
(a) The authors fit their CG force field to a number of shorter system MD trajectories and average over those. Previous techniques used whole trajectories
(b) CG sites are associated with the CM of the underlying atomic groups. Applying the FM procedure to these data yields the effective interaction between the CG sites as it is present in the underlying atomistic simulation.
This is not a mandatory selection. The geometrical center could also be chosen as can a hybrid approach.
A lot of the work on multiscale biomolecular simulations has been done on lipids, vesicle formation, etc. To generate the FM data, MD simulations in explicit solvent (TIP3P) are carried out (in the original paper at least), and the Particle Mesh Ewald (PME) method was chosen to model long range electrostatics. This system was then coarse-grained (using the center of mass approach) and the force matching procedure was applied to 4000 configurations from a 40 ps trajectory (which is fairly short). The authors found that their approach was able to reproduce the structural properties of the lipid bilayer quite accurately, and the input data is not any different from a typical MD simulation. For more complex systems, one might need to be more creative about the CG procedure. Automation of any such procedure would be a must for large scale application as well.
This is of course not the only method. Work by Julian Shillcock, Mikko Kartunnen, Qiang Cui, Aatto Laaksonen, etc should also be considered, but I have always found Voth’s approach to be the most elegant.
Thus ends the formal part of Just Science week. As Arunn has mentioned, blogging about pure science is a lot harder than it sounds. I found a lot of great science blogs though and got a lot of traffic this week, so at least a few people are interested in biomolecular simulation and protein structure prediction. Now .. back to our usually scheduled programming.
Further Reading:
Multiscale modeling of MscL
Technorati Tags: Just Science Week, Multiscale Modeling, Greg Voth, Biomolecular Simulation, Molecular Modeling
