Your Monday evening simulations

June 30, 2008

Image via WikipediaHot, long day, so just wanted to point folks to a couple of interesting papers that crossed my feed reader.

Alexey Onufriev and colleagues at Virginia Tech have carried out the kinds of simulations that get me all excited. An atomistic-level study of dynamic pathways of ligand binding in myoglobin. The paper will be published in PNAS (I can’t find it on the PNAS site yet). Strategies to study molecular motions at this level of detail and longer timescales are going to be the driver of MD over the next few years and I suspect you’ll see more such studies.

Another paper in PNAS is a simulation of the binding of ADP to a carrier protein (~~although I don’t get the part about the first simulation of binding. I am sure I’ve done some of my own before~~Update: Clarified in the comments). Some pretty impressive stuff from Emad Tajkhorshid whose work I’ve long followed.

Posted by Deepak Singh | Filed Under Modeling & Simulation

BioBarCamp: A reminder about participation and sponsorship

June 29, 2008

The venue and dates for BioBarCamp have been finalized and we would like to continue to get the word out for participants and sponsors.

BioBarCamp is a gathering of people interested in the life sciences and we hope to have about a 100 people to come together for a 24 hours of fun and serious discussion. Hopefully, a project or two will come out of the event. So those of you who are interested, please hop on over to the Google Group to signup

Several people, individuals and companies, have come forward with sponsorship for meals and beverages (geeks need their caffeine). But we could always do with more. So if you are an individual who wants to pitch in, or a company that would like to sponsor the event please contact either myself (deepak AT deepaksingh [DOT] net) or Attila Csordas ([attilacsordas] [at] [gmail.com]).

Posted by Deepak Singh | Filed Under Event

Building scientific communities

June 29, 2008

Image via WikipediaRun into this subject in a couple of places today, e.g. FriendFeed and it’s top of mind following the New Communication Channels in Biology workshop.

I will start with something I have quoted all too often

Data finds data, then people find people

That quote by Jon Udell, channeling Jeff Jonas is one that, to me at least, defines what the modern web is all about. Too many people tend to put the people first, but in the end without common data to commune around, there can be no communities.

Science is an intellectual pursuit, whether it is formal academic science or just casual common interest. That’s where all the tools available today come into the picture. The data has always been there. Whether at the backend, or at the front end, we can think about how to get everything together, but being able to discovery and find some utility is very important. One of the reasons the informatics community seems to thrive online, apart from inherent curiosity and interest in such matters, is that we have a general set of interests to talk about, from programming languages, to tools to methods, to just whining about the fact that we spend too much time data munging. Successful life science communities need that common ground. In a blog post, Egon talks about JMOL and CDK. Why would I participate in the CDK community, or the JMOL one? Cause I have some interest in using or modifying JMOL, or finding out more about the CDK toolkit and perhaps using it. Successful communities are the ones that can take this mutual interest around the data and bring together the people.

So my advice to anyone building a scientific community (the one that jumped out at me during the workshop was the EcoliHub) is to think about what the underlying data that could bring together people is first. Data here is used in a general sense. Not just scientific raw data, but information and interests as well. Then trying and figure out what the goals are that will make these people come together around the data and then figure out what the best mechanism for that might be. Don’t put the cart before the horse. In most such cases, you need a critical mass to make a community successful, to truly benefit from the wealth of networks. In science that’s often hard, so any misstep in step 1, will usually end up in a community that has little or no traction.

Posted by Deepak Singh | Filed Under Social Networking, Software & Internet

Great time on Day 1

June 27, 2008

The first day on Day 1 of the New Communication Channels for Biology workshop was great. Lots of interesting talks and Q&A sessions at a very good venue. More after I return to Seattle, but it is good to see scientists discuss wikis, blogs, video, open science, etc openly, without too much rhetoric. There are also many people really leveraging wikis and other platforms for real science, and doing a pretty good job of thinking about APIs and making sure their resources exist on the programmable web. Also got a chance to plug FriendFeed :).

Posted by Deepak Singh | Filed Under Event

Chris Anderson, you are wrong

June 25, 2008

Chris Anderson is a man I respect and Wired a magazine I like most of the time. During my chat with Jon Udell, I had bemoaned the gap between science and the general public, and Anderson’s latest article on Wired only serves as a reinforcement of that frustration. In an article entitled The End of Theory: The Data Deluge Makes the Scientific Method Obselete he writes about how the petabyte era is going to change science has one thing very right, and one thing very wrong. He is write more is not more. It is different. But more does not mean the end of the scientific method. I would argue that it allows us to think about new ways to apply the scientific method to try and solve problems.

Biology has changed a lot since the 70s, and its not just genetics. The very field has been turned around on its head. What does that mean? It means new methods, new techniques. The human and other genomes, high throughput structure prediction, more data points, might lead to a lot of confusion at first as we try and apply our old methods to new problems and data types, but it also leads to the creation of new techniques, our ability to tap into all this data, to build better models which can help describe what we know. It’s always a moving target. How can we develop better, more refined models that explain all kinds of phenomena. I am a life scientist (although with a physical sciences background). I have spent the better part of the last two years evangelizing “what science can learn from Google”, but I don’t see this as science under threat. I see this as opportunity. How can I (or others who actually still do science) take the new paradigms of computing (by the way, bioinformaticians have been using methods typically used in “collective intelligence” for years), and take biology, which is now very much a digital science, and combine them with our scientific reasoning, our ability to take phenomena and develop models that explain those phenomena and do something meaningful with them. I have seen many computer scientists develop some very elegant theoretical models for biological information, but often without any biological context. Yes scientists need to adopt new techniques, develop new theoretical approaches, even rethink the very basic tenets that they know, but to say the scientific method is dead or approaching the end is sensationalist in the least, and completely uneducated in the extreme. Having to use those words for an article pen by someone whose writing I admire hurts, but science is not a game of hype. It’s hard, and all the easy problems have been solved.

Footnote: We are barely tapping into quantum theory to solve life science problems. Most of our methods to look at protein structure and dynamics, to look at drug-protein interactions are either rule-based or Newtonian in nature, with quantum mechanics providing many of the parameters. So lets not put the cart before the horse, at least in the life sciences. I am sure the physicists are trying to develop new theories. That only means physics is very and truly alive. I wonder if people said science was dead when Schrodinger gave the world his equation.