Call for Bio::Blogs #12
I am collecting submissions for the 12th edition of Bio::Blogs. Send in links to blog posts you want to share from your blog or that you enjoyed reading in other blogs to bioblogs at gmail until the end of the month. The next edition will be up at Nodalpoint on the 1st of Jully.
Maybe it could be cool to try out a section on papers of the month as voted by everyone (Neil used to do this once in a while). Anyone interested in participating just has to send a link to a paper, published last month and related to bioinformatics, with a short paragraph explaining what is nice about the paper.
Mike over at Bioinformatics Zen is asking how to continue the Tips and Tricks section of Bio::Blogs. He has put up a wiki page on open science in Nodalpoint to collect information for a possible future edition of the special section.
Monday, June 25, 2007
Synthetic Biology 3.0
I am not attending the 3rd edition of the Synthetic Biology conference but there are several bloggers attending and reporting.
The Seven Stones
Nature Newsblog (part I and part II)
The ETC blog (intro and part I)
I am not attending the 3rd edition of the Synthetic Biology conference but there are several bloggers attending and reporting.
The Seven Stones
Nature Newsblog (part I and part II)
The ETC blog (intro and part I)
Thursday, June 21, 2007
Structures in Systems Biology (a double bill)
Once in a while I get to write about what I have been working on. The last time it was about the evolution of protein interaction networks. This time it is about two papers that I contributed too. A review about the use of structures in systems biology and an article about structure based prediction of Ras/RBD interactions. I am sorry to say that both require a subscription (pedrobeltrao *at* gmail).
Main conclusions
Structural data can be used to predict Ras/RBD interactions with approximately 80% accuracy
We can and should use structural information to understand the main molecular properties before abstracting away the atomic details. Structural genomics can serve as a bridge between the abstract network view and the atomic detail.
The Making off
Although I am not the first author of the article I think it is safe to say that the main inspiration for the line of work done by Kiel (see also previous publication) is the work by Aloy and Russell where they first showed that it was possible to use a protein complex to predict if similar proteins would be able to interact in a similar way. What Kiel showed is that more accurate predictions can be made by modeling the protein domains under test onto the complex and evaluating the binding energy using a protein design program under development in the lab (FoldX). She used pull-down experiments and available information on Ras/RBD interactions to benchmark the predictions.
The predicted binding energies inform us about the probability that the two protein domains would bind in vitro. Inside the cell there are many other factors contributing to the likelihood of binding (gene expression, localization, complex formation, post-translational modifications, etc). To try to add some of this knowledge to the predictions I contributed with a Naive Bayes predictor that combines information on gene expression, GO functions, conserved physical/genetic interactions in other species and shared binding partners. The likelihood score obtained can be used to further rank the predicted interactions according to the likelihood that these are occurring inside the cell. In supplementary information there are the methods and tables with individual likelihood scores that can be used to reproduce the Naive Bayes predictor.
From atoms to nodes and edges
I think one of the main goals of the the review was to show the current progress that has been made in using structural information to obtain the fundamental properties (binding sites, catalytic sites, protein dynamics, etc) of cellular components that may allow us to create models of cellular functions. There has been some work in approximating the very abstract "nodes and edges" view of cellular interactions to a more traditional pathway model. This has been done typically by searching for modules and particular node roles that depend on the patterns of intra or inter module interactions (see Guimera et al). We should be able to automatically decorate interaction networks (and the pathway modules) with structural data that can further help to computationally generate meaningful models of cellular functions.
The picture was obtained from Beltrao et al , it is Copyright © 2007 Elsevier Ltd and it used here hopefully under fair use.
In the pipeline
There are several important details to iron out before we can just apply this structure based prediction of protein interactions to any protein that we can model onto complexes. We are in the process of testing the approach with other different domain types. Some of if I have been more directly involved and we started now the submission process. I tried to get everyone to agree to submit it to a preprint server but not everyone was comfortable with the idea.
Once in a while I get to write about what I have been working on. The last time it was about the evolution of protein interaction networks. This time it is about two papers that I contributed too. A review about the use of structures in systems biology and an article about structure based prediction of Ras/RBD interactions. I am sorry to say that both require a subscription (pedrobeltrao *at* gmail).
Main conclusions
Structural data can be used to predict Ras/RBD interactions with approximately 80% accuracy
We can and should use structural information to understand the main molecular properties before abstracting away the atomic details. Structural genomics can serve as a bridge between the abstract network view and the atomic detail.
The Making off
Although I am not the first author of the article I think it is safe to say that the main inspiration for the line of work done by Kiel (see also previous publication) is the work by Aloy and Russell where they first showed that it was possible to use a protein complex to predict if similar proteins would be able to interact in a similar way. What Kiel showed is that more accurate predictions can be made by modeling the protein domains under test onto the complex and evaluating the binding energy using a protein design program under development in the lab (FoldX). She used pull-down experiments and available information on Ras/RBD interactions to benchmark the predictions.
The predicted binding energies inform us about the probability that the two protein domains would bind in vitro. Inside the cell there are many other factors contributing to the likelihood of binding (gene expression, localization, complex formation, post-translational modifications, etc). To try to add some of this knowledge to the predictions I contributed with a Naive Bayes predictor that combines information on gene expression, GO functions, conserved physical/genetic interactions in other species and shared binding partners. The likelihood score obtained can be used to further rank the predicted interactions according to the likelihood that these are occurring inside the cell. In supplementary information there are the methods and tables with individual likelihood scores that can be used to reproduce the Naive Bayes predictor.
From atoms to nodes and edges
I think one of the main goals of the the review was to show the current progress that has been made in using structural information to obtain the fundamental properties (binding sites, catalytic sites, protein dynamics, etc) of cellular components that may allow us to create models of cellular functions. There has been some work in approximating the very abstract "nodes and edges" view of cellular interactions to a more traditional pathway model. This has been done typically by searching for modules and particular node roles that depend on the patterns of intra or inter module interactions (see Guimera et al). We should be able to automatically decorate interaction networks (and the pathway modules) with structural data that can further help to computationally generate meaningful models of cellular functions.
The picture was obtained from Beltrao et al , it is Copyright © 2007 Elsevier Ltd and it used here hopefully under fair use. In the pipeline
There are several important details to iron out before we can just apply this structure based prediction of protein interactions to any protein that we can model onto complexes. We are in the process of testing the approach with other different domain types. Some of if I have been more directly involved and we started now the submission process. I tried to get everyone to agree to submit it to a preprint server but not everyone was comfortable with the idea.
Thursday, June 07, 2007
Tangled Bank #81 is know available
I participated with a submission to the latest edition of Tangled Bank (the first sciencecarnival blog journal around) that is available at the Behavioral Ecology Blog. Thanks to RPM at Evolgen for "peer-reviewing" my post on protein evolution :).
I participated with a submission to the latest edition of Tangled Bank (the first science
Nature Precedings, a pre-print server for biomedical research
It was hard to hold off from blogging about this but I can finally write about Nature Precedings, a new free service provided by the Nature Publishing Group. The official announcement is in this editorial:
"... this site will enable researchers to share, discuss and cite their early findings. It provides a lightly moderated and relatively informal channel for scientists to disseminate information, especially recent experimental results and emerging conclusions."
"...the site will host a wide range of research documents, including preprints, unpublished manuscripts, white papers, technical papers, supplementary findings, posters and presentations."
I have been participating in the beta for some months now and as it is mentioned in the editorial it will be openly available starting next week. All documents are citable (have DOIs), are not peer-reviewed (in the formal sense) and are archived under a creative commons license (derivatives allowed). The site has the community features (tagging/commenting/rating/RSS feeds) that you would expect and that will hopefully allow for requesting and providing comments on early findings. In summary an nicer version of ArXive for biomedical research.
I think this is great news that serves on one hand to improve access to research (open access by pre-print archiving) and increase the openness of research. This can provide a place for independent time-stamping of early findings and could be improved (hopefully with community feedback) until it is appropriate for formal submission to a peer-reviewed journal.
A framework for open science (in biology) can now go from blogs/wikis to pre-print server to peer-reviewed journals. Many ideas might die along the way and many collaborations might form by connecting early findings in an unexpected way.
Of course if you are in maths/physics you have arXive and you are probably wondering what is taking us biomedical researchers so long to get into this.
It was hard to hold off from blogging about this but I can finally write about Nature Precedings, a new free service provided by the Nature Publishing Group. The official announcement is in this editorial:
"... this site will enable researchers to share, discuss and cite their early findings. It provides a lightly moderated and relatively informal channel for scientists to disseminate information, especially recent experimental results and emerging conclusions."
"...the site will host a wide range of research documents, including preprints, unpublished manuscripts, white papers, technical papers, supplementary findings, posters and presentations."
I have been participating in the beta for some months now and as it is mentioned in the editorial it will be openly available starting next week. All documents are citable (have DOIs), are not peer-reviewed (in the formal sense) and are archived under a creative commons license (derivatives allowed). The site has the community features (tagging/commenting/rating/RSS feeds) that you would expect and that will hopefully allow for requesting and providing comments on early findings. In summary an nicer version of ArXive for biomedical research.
I think this is great news that serves on one hand to improve access to research (open access by pre-print archiving) and increase the openness of research. This can provide a place for independent time-stamping of early findings and could be improved (hopefully with community feedback) until it is appropriate for formal submission to a peer-reviewed journal.
A framework for open science (in biology) can now go from blogs/wikis to pre-print server to peer-reviewed journals. Many ideas might die along the way and many collaborations might form by connecting early findings in an unexpected way.
Of course if you are in maths/physics you have arXive and you are probably wondering what is taking us biomedical researchers so long to get into this.
Friday, June 01, 2007
Bio::Blogs# 11
The 11th edition of Bio::Blogs, is online at Nodalpoint. We tried to do something different this time. Michael Barton volunteered to host a special section dedicated to tips and tricks for bioinformatics that is hosted separately in Bioinformatics Zen. Because there were so many posts this month about personalized medicine there is also a special section on that.
There are three separate PDFs for this edition: 1) the main PDF can be found here; 2) The one on personalized medicine can be downloaded here; the one for tips and tricks available from Bioinformatics Zen. Michael did a great job with this special section, with a very cool design.
The 11th edition of Bio::Blogs, is online at Nodalpoint. We tried to do something different this time. Michael Barton volunteered to host a special section dedicated to tips and tricks for bioinformatics that is hosted separately in Bioinformatics Zen. Because there were so many posts this month about personalized medicine there is also a special section on that.
There are three separate PDFs for this edition: 1) the main PDF can be found here; 2) The one on personalized medicine can be downloaded here; the one for tips and tricks available from Bioinformatics Zen. Michael did a great job with this special section, with a very cool design.
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