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Housekeeping & Self References Meetings & Conferences Papers Science Online

Social Networking and Guidelines for Life Science Conferences

ResearchBlogging.org
I had a great time in Sweden this past summer, at ISMB 2009 (ISMB/ECCB 2009 FriendFeed room). I listened to a lot of interesting talks, reconnected with old friends and met new ones. I went to an ice bar, explored a 17th-century ship that had been dragged from the bottom of the sea, and visited the building where the Nobel Prizes are handed out.

While there, many of us took notes and provided commentary through live blogging either on our own blogs or via FriendFeed and Twitter. The ISCB were very helpful, having announced and advertised the live blogging possibilities prior to the event. Once at the conference, they provided internet access, and even provided extension cords where necessary so that we could continue blogging on mains power.

Those of us who spent a large proportion of our time live blogging were asked to write a paper about our experiences. This quickly became two papers, as there were two clear subjects on our minds: firstly, how the live blogging went in the context of ISMB 2009 specifically; and secondly, how our experiences (and that of the organisers) might form the basis of a set of guidelines to conference organisers trying to create live blogging policies. The first paper became the conference report, a Message from ISCB published today in PLoS Computational Biology. This was published in conjunction with the second paper, a Perspective published jointly today in PLoS Computational Biology, that aims to help organisers create policies of their own. Particularly, it provides “top ten”(-ish) lists for organisers, bloggers and presenters.

So, thanks again to my co-authors:
Ruchira S. Datta: Blog FriendFeed
Oliver Hofmann: Blog FriendFeed Twitter
Roland Krause: Blog FriendFeed Twitter
Michael Kuhn: Blog FriendFeed Twitter
Bettina Roth
Reinhard Schneider: Blog FriendFeed
(you can find links to my social networking accounts on the About page on this blog)

If you have any questions or comments about either of these articles, please comment on the PLoS articles themselves, so there can be a record of the discussion.

Lister, A., Datta, R., Hofmann, O., Krause, R., Kuhn, M., Roth, B., & Schneider, R. (2010). Live Coverage of Scientific Conferences Using Web Technologies PLoS Computational Biology, 6 (1) DOI: 10.1371/journal.pcbi.1000563

Lister, A., Datta, R., Hofmann, O., Krause, R., Kuhn, M., Roth, B., & Schneider, R. (2010). Live Coverage of Intelligent Systems for Molecular Biology/European Conference on Computational Biology (ISMB/ECCB) 2009 PLoS Computational Biology, 6 (1) DOI: 10.1371/journal.pcbi.1000640

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Meetings & Conferences

Special Session 4: Panel Session (ISMB 2009)

Panel Session with the three previous speakers plus Philip Bourne, Editor-In-Chief PLoS Computational Biology
Part of the Advances and Challenges in Computational Biology, hosted by PLoS Computational Biology

Changing the scientific culture to increase collaboration. How to make it easy to collaborate? Make it easy / required to submit work in a standard way to a journal, perhaps?

  • AA: the problem is reward. The reward for publishing a model as SBML is zero, or even negative right now. Until it stops “preventing” us from doing science (e.g. doing stuff we don’t “have to” do), you won’t see as much an uptake as you should. Even now, it’s somewhat limited in what it can capture.
  • DS: People only put data into public databases when they have to.
  • PB: The publishing industry also has to figure out if and how it should deal with these things (OA, standardization, transparency, etc). How to bring together the efforts of publishing and funding agencies scientists towards this end? We should start having DOIs and PubMed IDs to things that are a little less than traditional. It has the potential to push forward science in incredible ways.
  • AA: You donate your sequence data to the public because you get homology out of it. So, scientists are OK about donating data but it’s not alwasy obvious to the researcher all of the benefits.
  • PB: Very few people sit and look through an entire journal anymore: they go to specific papers.  So, you should be able to navigate between papers, abstracts and other components of the paper.
  • DS: Scanning abstracts first, and then drill down as necessary either to paper or supplementary material (or more).
  • AA: supplementary markup is a huge onus on whoever does it. Markup would only be useful if it is in a common format.
  • DS: Many people who expose data satisfy the letter of the law (expose the data) but not the spirit (someone don’t make the best bits available) (Allyson: I’m paraphrasing here as I didn’t catch it all. I may not have this bit correct.)
  • Question: Her hospital is about to give her some clinical data – before that happens, she has an opportunity to say what the best sort of data is. What should be included to make it the most useful?
    • DS: Veterans’ Association (useful taking orders from the top) got it right. A working group that starts with the VA standards and then goes from there might be a good thing.

Allysons thoughts (summarized from the earlier talks): what about standardization efforts for format/syntax/scope? CARMEN? MIBBI-like efforts: is the checklist effort part of MIBBI? And, to all panelists, rather than making your own simulation format, why not use FuGE, which can be used to describe ANY experimental metadata if you want it to, either wet lab or in silico? Tried to ask this, but ran out of time 🙂

FriendFeed Discussion

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

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Meetings & Conferences

Special Session 4: Donna Slonim on human development / TM (ISMB 2009)

Donna Slonim
Part of the Advances and Challenges in Computational Biology, hosted by PLoS Computational Biology

Should be from Bench to Bytes to Bedside (and Back), not from Bench to Bedside. There have been many successes in TM, but it’s not always clear that the loop is being closed between new discoveries and the clinical practices. Common complaint is lack of funding, and time it takes to get new drugs to market. What might be helpful in closing the loop: strongly-interdisciplinary collaborations, availability of clinical data, and standards to ensure that data are shared in useful ways.

Translational Development Genomics: while there has been progress in screening, is there anything more that we can get out of genomics data and help with diagnoses? Collaboration with Diana Bianchi. In addition to DNA screening, real-time fetal RNA from amniotic fluid could get lots of information. Also, you can find fetal RNA in maternal blood. They identified 157 genes upregulated in pregnant moms and their babies, compared to postpartum moms. In the UniGene db, 6 of their proteins were unique to the fetus and 76 have predominately fetal or neonatal expression. Over-represented functions include neural development, muscle development, lung development, cell division, visual perception, digestion, perception of smell, response to pathogens. This hinted that these were fetal. So, they looked for SNPs in the transcript to verify the fetal source of RNA. They were seeing fetal transcripts, as SNPs suggested antepartum e.g. A/A + A/T, fetal was A/T and postpartum was A/A.

So they did a pilot study for Down’s Syndrome (caused by trisomy 21). They profiled amniotic fluid from trisomy 21 and control sample pairs matched by sex – looking for 1.5-fold expression in chromosome 21 to account for the extra genes. There is quite a range of expression – very little is significant. BUT there’s huge disregulation of the genome. Everything (414 individual genes, 82 leading edge genes in Chr21q22, connectivity map) pointed to the same functional results. Categories significant in both gene sets (chr21 and rest of genome): response to oxidative stress ,G-protein signaling, ion transport, cell structure proteins, circulatory system function, developmental pathways, sensory perception. The connectivity map further implicates oxidative stress: this map correlates observed expression patterns with gene expression changes caused by various drugs. The top compounds (positive correlation) relate to oxidation and ion transport. Trying to find any drugs that affect these are also approved for use in pregnant women (will take a while).

Their work relies on shared public data and tools. An additional obstacle to progress is that functional annotation is not designed for this developmental stage, and there is need for a shared annotation effort.

FriendFeed Discussion

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

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Meetings & Conferences

Special Session 4: Adam Arkin on Synthetic Biology (ISMB 2009)

Running the Net: Finding and Employing the OPerating Principles of Cellular Systems
Adam Arkin
Part of the Advances and Challenges in Computational Biology, hosted by PLoS Computational Biology

The need for scientific standards and cooperation. Very much data driven in synthetic biology. We’ve been genetic engineering since the dawn of agriculture (teosinte, cows etc). And with dogs, which started around 10,000 years ago. Then the extremely different breeds we have today. That such differences would cause survival effects in the “wild” doesn’t bother many people. Next is the classic example of the cane toad, which destroyed environmental diversity.

Synthetic biology is dedicated to making the engineering of new complex functionsin cells vastly more transparent, and that openness is a really important part. It is trying to find solutions to problems in health, energy, environment, and security.

How can we reduce the time and improve the reliability of biosynthesis? Engineering is all about well-characterized, standard parts and devices. You need standards in parts, protocols, repositories, registries, publications, data and metadata. This helps a lot when you have groups and need to perform coordinated sciences: linux is an example of this working. But is design scalable? While applications will always have application-specific parts, there are sets of functions common or probable in all applications.

You can have structures that regulate most parts of gene expression. In talking about probability of elongation, they use an antisense-RNA-mediated transcription attenuator, which has a recognition motif, a possible terminator, and a coding sequence. Through a series of steps, if a antisense RNA absent, then you get transcription (and the opposite is true too): this is a NOT gate. For transcriptional attenuators, it is possible to design orthogonal mutant lock-key mechanisms. You can obtain orthogonal pairs by rational design but there is a certain attenuation loss. They can’t explain everything about the functioning of these devices. Want to improve communication in this respect. If you put two attenuators on the same transcript, it behaves about as you expect: a NOT-OR gate.

Bacteria engineered as pathogens to target particular human tissue (e.g. tumors). To do that, you have to build many different modules with its own computational and culure unit tests. These different modules/models can be re-used, e.g. in the iGEM competition. The problem is that the complexity of the engineering problem is greatly increased beyond that found in chemical production / bioreactors.

Absolute requirements: openness, transparency, standards, team-science approaches.

FriendFeed Discussion

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

Categories
Meetings & Conferences

Special Session 4: Abigail Morrison on Neuroscience (ISMB 2009)

Abigail Morrison: Communicating Ideas in Computational Neuroscience
Part of the Advances and Challenges in Computational Biology, hosted by PLoS Computational Biology

In computational neuroscience, the key ideas to be communicated are mathematical and computational models as well as data analysis methods. She will mainly focus on computational models in this talk, though what she says mostly holds for data analysis methods as well.  They type of modelling being done is getting more and more complicated all of the time. And yet, there is no standardization in notation, simulation software, and best practices for describing models. As a result, we cannot reproduce the work of others or critically evaluate or compare models.

A researcher comes up with an interesting model and simulation, and then they want to publish it. So they try to write down what they did in the model. Then another researcher in a similar area reads it and wants to reproduce or build on it. Then, they run into problems: how do they figure out what parameters to use, what dynamics are present? Ultimately, the system they’re running their simulations on will probably be different, and their version of the model won’t work right. So, she’s working on a system that can be more standardized.  Abigail Morrison can think of only one model, in all the times she’s worked on it, that they’ve been able to reproduce without going back to the authors.

Is it science, or is it travel reporting?

Approaches to solve this problem have to be both sociological (large collaborations with defined software and protocols) and technological (version control, high-level APIs, testing and unit testing), or even socio-technological (work together to create tools to facilitate reproducibility).

A lot of interesting work is happening with INCF and NeuralEnsemble/PyNN. INCF has been running since 2005 and tries to coordinate neuroinformatics (databases & data sharing, tool development and analysis, computational models) internationally. INCF also involved with portals, standards incl. ontologies. The Japanese node focuses on the visual side of things, and has produced Visiome, which attempts to collect both papers and figures separately as well as model parameters, simulation scripts and figure-generation scripts. This can all be downloaded, and then hopefully run it on your own system. Another project there is the Simulation Server Platform, intended to provide online test trials for simulation scripts on a virtual machine. All elements are reproduced in the VM such as OS and hardware emulation, compilers, simulation software and viewers. In this way, it supports reproducibility of results by other researchers and testing by journal reviewers.

At the German node, the main focus is to support interactions between experimental and computational neuroscientists, and focuses on collaboratively develop Open-source tools for data access and analysis. The problem is that there are many different recording devices and analysis tools, and no standardization. So, they want a unified data format, implement open source input and export functions for common data formats. Develop and provide a repo for these tools. They also want to design and implement a machine-readable declarative language to describe neural network model (like SBML) – first meeting in March 2009 so still new.

NeuralEnsemble provides hosting for open-source Python-based software projects in Neuroscience, and a key project is PyNN, a common scripting language for all simulators. This facilitates cross-checking of results between simulators, and incremental porting of a model from one simulator to another.

There’s a paper coming in PLoS later this year making a checklist of common suggestions for how network models could be described in words.

Allyson’s thoughts: what about standardization efforts for format/syntax/scope? CARMEN? MIBBI-like efforts: is the checklist effort part of MIBBI? Also, is it really “reproducibility” of results if you have to go to a VM somewhere to get it to work? Probably not, but at least it’s a first step on the road to better tyes of (more complete/generic) reproducibility.

FriendFeed Discussion

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!