Just saw this interesting story in the Observer: Cancer scientists to classify gut bacteria to prevent the side-effects of radiotherapy | Science | The Observer. It discusses an effort to give more consideration to protecting and / or repopulating the microbiome in relation to radiation therapy. I think this is critically important. I want to note - people should give some credit to DARPA for being ahead of their time on this issue. I went to a workshop in 2004 organized by Brett Giroir and Manley Heather. The topic was "Radiation Protection" and one of the points of discussion was the gut microbiome and the effect of radiation on it.
Anyway - since that meeting I have been following this topic on and off. And I do think thinking about the microbiome in relation to radiation therapy (and any radiation exposure) is critically important.
Showing posts with label DARPA. Show all posts
Showing posts with label DARPA. Show all posts
Sunday, March 23, 2014
Thursday, November 01, 2012
Story behind the Paper: Functional biogeography of ocean microbes
Guest Post by Russell Neches, a PhD Student in my lab and Co-Author on a new paper in PLoS One. Some minor edits by me.
For this installment of the Story Behind the Paper, I’m going to discuss a paper we recently published in which we investigated the geographic distribution of protein function among the world’s oceans. The paper, Functional Biogeography of Ocean Microbes Revealed through Non-Negative Matrix Factorization, came out in PLOS ONE in September, and was a collaboration among Xingpeng Jiang (McMaster, now at Drexel), Morgan Langille (UC Davis, now at Dalhousie), myself (UC Davis), Marie Elliot (McMaster), Simon Levin (Princeton), Jonathan Eisen (my adviser, UC Davis), Joshua Weitz (Georgia Tech) and Jonathan Dushoff (McMaster).
For this installment of the Story Behind the Paper, I’m going to discuss a paper we recently published in which we investigated the geographic distribution of protein function among the world’s oceans. The paper, Functional Biogeography of Ocean Microbes Revealed through Non-Negative Matrix Factorization, came out in PLOS ONE in September, and was a collaboration among Xingpeng Jiang (McMaster, now at Drexel), Morgan Langille (UC Davis, now at Dalhousie), myself (UC Davis), Marie Elliot (McMaster), Simon Levin (Princeton), Jonathan Eisen (my adviser, UC Davis), Joshua Weitz (Georgia Tech) and Jonathan Dushoff (McMaster).
Using projections to “see” patterns in complex biological data
Biology is notorious for its exuberant abundance of factors, and one of its central challenges is to discover which among a large group of factors are important for a given question. For this reason, biologists spend a lot of time looking at tables that might resemble this one :
Which factors are important? Which differences among samples are important? There are a variety of mathematical tools that can help distill these tables into something perhaps more tractable to interpretation. One way or another, all of these tools work by decomposing the data into vectors and projecting them into a lower dimensional space, much the way object casts a shadow onto a surface.
The idea is to find a projection that highlights an important feature of the original data. For example, the projection of the fire hydrant onto the pavement highlights its bilateral symmetry.
So, projections are very useful. Many people have a favorite projection, and like to apply the same one to every bunch of data they encounter. This is better than just staring at the raw data, but different data and different effects lend themselves to different projections. It would be better if people generalized their thinking a little bit.
Biology is notorious for its exuberant abundance of factors, and one of its central challenges is to discover which among a large group of factors are important for a given question. For this reason, biologists spend a lot of time looking at tables that might resemble this one :
sample A
|
sample B
|
sample C
|
...
| |
factor 1
|
3.3
|
5.1
|
0.3
|
...
|
factor 2
|
1.1
|
9.3
|
0.1
|
...
|
factor 3
|
17.1
|
32.0
|
93.1
|
...
|
...
|
...
|
...
|
...
|
...
|
Which factors are important? Which differences among samples are important? There are a variety of mathematical tools that can help distill these tables into something perhaps more tractable to interpretation. One way or another, all of these tools work by decomposing the data into vectors and projecting them into a lower dimensional space, much the way object casts a shadow onto a surface.
 |
So, projections are very useful. Many people have a favorite projection, and like to apply the same one to every bunch of data they encounter. This is better than just staring at the raw data, but different data and different effects lend themselves to different projections. It would be better if people generalized their thinking a little bit.
Friday, September 09, 2011
Attention - all interested in synthetic biology - DARPA is interested, big time
For those interested in synthetic biology, check out the new DARPA call for proposals: Living Foundries: Advanced Tools and Capabilities for Generalizable Platforms (ATCG) - Federal Business Opportunities: Opportunities
And check out the "teaming" site trying to bring together groups and people with diverse backgrounds and interests. Kudos to Alicia Jackson at DARPA and DARPA in general for recognizing the importance of synthetic biology.
I note - Alicia Jackson's announced this push at the Synthetic Biology meeting at Stanford earlier in the summer:
For some other information about this see:
I note - Alicia Jackson's announced this push at the Synthetic Biology meeting at Stanford earlier in the summer:
- Twitter / Jonathan Eisen: Alica Jackson from DARPA announcing DARPA is getting into synthetic biology in a big big big way w/ "Living Foundries Program
- The Tree of Life: Some quick notes on #Synbio5: Synthetic Biology ...
- Check out video from the Synthetic Biology meeting where this was announced:
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