An Exercise in Irrelevance

This is a live blog from Neuroinformatics 2009.

Creative Commons is based around issues with data and copyright, trying to change the idea that not sharing is the default. Science Commons looks at the issues specific to science.

Semantic web in a nutshell; adds to web standards and practices encouraging, common naming, ontology development, expression in knowledge representation language, easy integration over multiple sources, works both inside and outside the organisational boundaries.

Why should you want this? Network effects, people can use their own skills, and combine knowledge from many different sources. Provides efficiencies at the global scale.

Copy and paste for the semantic web; a mashup with knowledge from Allen brain institute, and google API. Had to screenscrape Allen brain for this.

Trying to look for druggable targets in pyramidal neurons. Google provides too many results, so does pubmed. Shows complex SPARQL query over the knowledge from the web; crossing from MESH to gene to GO. This may not be the best query, but it’s none the less useful and will make biologists happy.

A brief jump into ontology making. Terms that mix up material and neurotransmitter. Uses example, peptide, neurotransmitter, hormone and ligand; all of these could be peptides, although not necessarily. Need to untangle these. In many cases, these have already been done (ChEBI). Move from English to OWL.

How to build consensus in ontology building — somewhat related to OBOFoundary rules. Another program is INCF program for ontology of neural structures.

Challenges — building bigger ontologies is hard. Barrier to sharing are a major difficulty.

This is a live blog from Neuroinformatics 2009.

All of our observations about the brain are in some sense reductionist. We are looking at only thing at a time, and hope to infer knowledge from this. The knowledge is multi-technique — no single experiment is going to give the entire answer. Need to combine and integrate. Most of our data is descriptive — MRI is not that different from phrenology in one sense.

Process of dissemination — the web and equivalent — has been transformative of neurosciences. Large scale consortia are also important; has been involved in lots of these — sometimes painful — but useful. Good to learn the lessons from these.

The biggest lession from multisite brain mapping projects — the data needs to be open. If that data is open people will come, so long as it’s described.

Are new techniques coming along all the time; every near there is a new way of looking at stuff. Need to combine these forms of the data with knowledge from the past. There is a cost to this — digitizing and representing histology for instance, creates a lot of data. Currently can at 10 micrometre resolution on whole brain in terabytes of data.

One of the big issues is that, lots of the data is under patient confidentiality. Often can only store and check deidentified data. Are problems with metadata — some places have sent “phantom” images — which are used to callibrate the equipment, with a patient name on it. This sort of thing reduces the value. Need to check the data constantly.

Data Sharing and access control. Is a spectrum. Can release the data instantly it’s produced, six month after deposition, after publication, or never. Have a system to support this, with the acquirer having control over this.

Hardware — spend lots of money and eventually it will work. Have a 4PB system now, Uses a robotized tape system because spinning disks are too expensive.

Computer crashed out at this point, and I had to reboot, but he talked about Alzheimers. Gives a nice hypothesis that multi image databases could potentially answer.

With BIRN, data does not necessarily need to be centralised — it is possible to support distributed, but federated, databases. Have managed to aggregate and bring together information from many different resources. Databases need to have a suite of ancillary tools which we can use to look at the data.

Last example, ADNI — Alzheimers Diseaes, naturalistic study of AD progression. About 800 individuals with a variety of different techniques. Data is immediately released (same day often). Are about 90,000 images in the database; Downloads are highly periodic (not sure why!).

Data needs to be sufficiently well described, with integration across different datasets.

What works and what doesn’t. First, data — the data must be describable enough so that they can be understood. Second, the experiments need to be coordinated or they hard to integrate. Tools must be good. Needs to be a good focus. Size: the data needs to be big enough to have statistical power. Duration: databases must last, so must have enough funding. Mission: is it well enough defined. People: common purpose and leadership to carry forward. Sociology: do people agree what should be shared and when. Expertise: need this. Funding: need sustainability.

This is a live blog from Neuroinformatics 2009.

Neuron systems are incredible stable over time. Looking at a number of systems, including pyloric pattern generator — stomachs in crabs (?). Is a pacemaker system; it’s very stable between individuals and over time. Despite this, for example, the maximal conductance in the neurons varies pretty widely. How come this variability doesn’t affect stability?

Have generator a single compartment model, looking at 8 dimensional parameter space and making a big database. Trying to replicate the variance that they see within the biological systems. Tend, with their model, to get similar output for these different conditions. Conclusion of this is that neuronal system have a large solution space within which they maintain their functioning.

Question, how are these solution spaces distributed within the total parameter space? Could be a single unique solution, could be islands, etc. Been collaborating in high-dimensional space visualisation using dimensional stacking. Think it’s a slices through the dimensional space, stacked out side-by-side.

Talks about cell-type specific co-regulation of ion channels; there are lots of correlations between different forms of current. Interestingly, most of the relationships are linear and, so far, all of them are positive; it’s not clear that there are any negative correlations.

Have classified their model space. Found that correlation between channels is always in the same direction — a correlation which is positive in one cell type will never be negative in another. However, they are showing a negative relationship in some circumstances, when the experimental processes show a positive relationship which is an open question.

This talk was given as a keynote at Neuroinformatics 2009.

This is my first attempt at live blogging a conference talk, so please read it in this light.

There is an overlap between neuroinformatics and bioinformatics; one example of this is the necessity for data integration between the two. Looking to the future; suggests that every database will have a canonical atlas; high-throughout measurements; dynamic live-brain imaging and mesoscopic biology; relationship to disese and pathology.

First step was taken by Allen brain atlas, to expression of genes to atlas to be of any use. Altas is now linked out to pretty much everything else; mostly through genes and gene IDs.

Example of systems biology approach to prion disease — injected prion into a variety of different mouse backgrounds. Looked for changes in expression in many different genes. Are a number of factors affecting prion disease; distinct prion strains cause different effects in the same background.

Highlights the necessity for standards in mass spectrometry if you wish to make quantative comparisions. More generally, this allows integration from many different data types, producing extension descriptions, for example, of a macrophage response.

Building a big integrated database of lipid metabolism.

Looked at oxidative stress in endothelial cells; again, did this by integrating knowledge from many different forms of experiment.

Next gen sequencing, ChIPing and digital gene expression. ChIP is massive sequencing of immunopreciptated chromatin DNA. Requries no PCR, so no amplification bias which is a problem for repetitive DNA.

Molecular imaging in vitro and in vivo; again gives a set of examples of where this is being used in xenopus and human; suggests relating fMRI data to genomic and other data will be the next big challenge.

Molecular modelling is also useful for integrating data. Gives set of example,s including calcium control within the cell. Were able to reproduce Calcium profile of many different gene knockouts and knockdowns from this sort of model.

One of the questins was:

How much data can you share — answer, all of it, with metadata if you want it to be useful!

I’ve just arrived at the conference centre in Pilsen for Neuroinformatics 2009. I got into Pilsen last night, exhausted from the flights and lack of sleep, especially after Science Online. Ironically, I was so knackered last night, that I was in the bedroom by 10 which, follow the traditional hour of fiddling with the aircon to get a reasonable temperature, meant I still got a good nights sleep and managed to get up early. To the point that my phone alarm annouced “It’s time to get up”, loudly in the taxi here.

Pilsen (or Plzen) is famous as being home to the lager. If you didn’t know this before you got here, you will after. I’m staying in the Hotel Angelo, which is near the middle of town and right opposite the brewery. Last night, was Pilsfest — yep, land in Czech and there is a beer festival on; either I must be lucky, or they have a lot of beer festivals. We drove here from the Prague; the countryside is green and beautiful, with magnificient, dark green decidious trees everywhere. Pilsen is built around a central square with a church; there’s the odd motif about the place with makes it foreign to me; the odd tower that looks like a minaret; the ornamentation under the eves of the building and the strange psch, psch, psch of the language.

Weather is cool, and autumnal just like Newcastle (well, maybe a wee bit warmer). Just after I arrived, while I was wandering around the beer festival, the skys opened and it rained torrentially. Fortunately, it was over quickly, and we ended up going to the inevitable Italian resturant — has a pizza, wasn’t great.

I have a decision to make; the last few conferences I’ve been to, the roboblogger was there, knocking out live blogs; I think that these are useful but, generally, can’t be bothered to do them myself. But in the absence of 100 keen bloggers, perhaps I should have a go this time.