I love me some O’Reilly books. After recently reading Beautiful Code (I should write a review soon). I am eagerly awaiting a new book I just bought from Amazon called Programming Collective Intelligence.
It was written by Toby Segaran, a developer at Genstruct
“This fascinating book demonstrates how you can build web applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it.”
Some of the most successful spots on the web are winning on this exact principle. Google, Facebook, Digg, MySpace, Flikr, Twitter, YouTube, and the list goes on and on. As much as I hate the almost cliched phrase Web 2.0, there is clearly a difference in how successful web applications are designed today as opposed to 5 years ago. We need more community based efforts in biology. Educational efforts like Bioscreencast. Or competitions like CASP which I have participated in. The tools are in place. Given the grand challenges to our health and understanding of life, we can no longer afford to work alone.
Science blogs are emerging everywhere. The-Scientist did an article about biomed at the PSC in the August issue. They’re now running a ‘Top Science Blogs’ poll which I found through Deepak’s post. Here’s my top 5 list according to my Google Reader trends view
by number of items read:
- business|bytes|genes|molecules
- What You’re Doing Is Rather Desperate
- Depth-First
- chem-bla-ics
- Propeller Twist
It’s not fair to stop at 5, since there’s so many other great science blogs that I read frequently..
BleedingEdgeBiotech is now listed in the wiki at Nodalpoint thanks to Greg. The community there is fantastic and I am often inspired by the high quality content throughout. There’s even a nodalpoint facebook group that is growing rapidly.
A fundamental process of life is the selective and efficient catalysis of chemical reactions by enzymes. Enzymes are usually proteins (ribozymes are one exception), and when these catalysts are chained together they form pathways. Enzyme pathways can be loosely described by their inputs and outputs. An even better abstraction than pathways though is to think in terms of networks. Networks have hubs which are critical to the operation of the network. [Vidal Lab is doing great work in this area of cancer proteomics]
In biology, allosteric enzymes are typically the regulatory elements in a catalytic network. More importantly, interactions distant from the catalytic site can induce changes in activity. One of the first examples of regulated enzyme networks is a system of 5 enzymes in bacteria which catalyze the conversion of L-Threonine to L-Isoleucine. Threonine dehydratase, the first enzyme in the pathway, is specifically inhibited by the end product of the pathway. This is simple feedback-inhibition, where buildup of the end product regulates and slows down the entire pathway by modulating the first step. This simple model illustrates an important aspect of protein interaction. It’s not good enough to simply say that enzyme A “interacts with” enzyme B. We need models that can express things like feedback, messaging, and other more abstracted language about protein relationships.
Compared to genomics, the proteomics universe appears to be pretty messy. Proteins interact in networks with enormous complexity. The challenges for a reverse engineering approach are overwhelming. There is no high-throughput method for reliably characterizing protein functions. Systems Biology is applying simplistic network models, and the Gene Ontology Consortium is working to develop a language for cellular functions. Both of these efforts have much to gain from structural biology.
Further Reading:
Ligand binding and allostery can emerge simultaneously
Is allostery an intrinsic property of all dynamic proteins?
The changing landscape of protein allostery
I have some strong reservations about data ownership when we talk about a Google-dependent world. However I think the current state of biomedical informatics is begging for the information giant to step into the picture. Deepak over at BBGM linked to some intriguing screenshots of Google Health. My first thought, “It’s about time!”. Health information management is a disaster in the U.S., so maybe Google was afraid of tackling this problem since so much of it is still out of their control.
We shouldn’t have to fill out lengthy forms on paper every time we need health care. We need open health information systems. We need predictive, preventive, and personalized medicine, as described by Dr. Leroy Hood of the Institute for Systems Biology. Health care as I see it today is reactive, meaning it applies some generalized solution only after the problem manifests itself as an ailment. We can do better. It’s an unfortunate situation that physicians deal with lumbering and bureaucratic information systems. Medical records should exist in decentralized repositories with real-time services for care providers as well as for patients themselves.
Eric Rollins did some interesting tests with Ruby. It should be possible to run parallel tasks effectively with DRb. I am going to do some more playing around with this.
The Ruby fanfare on the net seems to be approaching the “multi-core” crisis and several bloggers are talking about Ruby as a language for parallel environments.
Multicore Hardware and the Future of Ruby
Multi-core hysteria and the thread confusion
News for Week 25/2007
Distributed Ruby Workers on EC2
Listening to the latest FIB podcast,
What is the nutrient and caloric value in food? Is it an absolute term? … a box of cheerios says 110 calories per serving. Now does each person that eats a serving of cheerios extract 110 calories or are there subtle differences in our caloric harvest?
Dr. Jeffrey Gordon talks about microbial metagenomics. His research looks to make a huge impact on our knowledge of nutrition and dieting. Gut microbial communities have a dynamic influence on host genomes in mice. They sampled obese mouse genomes and found some clear indications that the massive amount of bacterial organisms living inside each of us are impacting our health in way that is mutually beneficial.
I see two big opportunities here. The treatment of malnutrition and obesity should be more focused on the physiological conditions imposed by these micro-communities. Second, the ability to engineer new bacterial components with the complexity and efficiency we are seeing in nature is forward thinking. Mash up detailed human microbe metabolomic data against all the new ocean community microbe genomes and you have quite an exciting study!
Let me further define mash up as to not sound too journalistic.
- Rebuild those trees, phylogenetics is getting much more interesting
- Metabolic pathway ontologies better be in order
- Networks, networks, networks. Signals from microbial genomes are coordinated with our own microphysiology.
Nintendo’s latest video console the Wii is doing very well. What I’ve found interesting is the exploration people are doing with the Wiimote controller itself. It’s already been used for video editing, playing Half-life, and even business presentations. A couple of months ago we were using it as a mouse in VMD and Chimera. Some interest has peaked in having an application designed specifically for the use of the wiimote and scientific visualization.
Wii Linux
Wiimote playing Half-life
I was using iTunes to listen to podcasts but I hardly ever put them on my ipod. Recently, iTunes wouldn’t let me download every podcast for a channel (ironically, the rubyology channel). So I thought a Ruby command-line utility might be nice.
I also learned some YAML thanks to Rubyology, so why not have a subscriptions.yaml
Continue reading ‘Scraping Podcast RSS with Ruby’
