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My comments to this blog are presumably solicited. As for the topic of this thread, my own world (research wise) continues to shrink towards a singularity out of which I do hope a Ph.D. will emerge. So bear with my increasingly pedantic arguments.

A major trend in both genomic and structural biology is the emergence of minor parameters for RNA tertiary interactions. It would be amazing to see this emerge in a purely probabilistic model (which I believe it necessarily must, eventually) - a framework that is extensible mathematically. The overwhelming variety of non-specific interactions is of course a supreme challenge.

The host of pols that engage in translesion synthesis fascinates me at the moment. There has been a few papers recently which relate the interest in this biological puzzle.

I agree with the intent to reduce the complexity of computational biology operations. A wise man once told me that there is a linear time solution to nearly every algorithmic problem. It would be nice to see people spending more time on cleverness than brute force attacks.

Personally I would die a happy man if the misuse of quotations in sentences and posted placards was rigorously attacked. You know "what" I mean.

Finally, I hope for and fear the years of Google ahead. Most of the time I simply ruse my own ineptitude at not being able to build a supremely useful tool that Google would drool over and buy from me. I look forward to the net being pushed (by Google, and ruthlessly at that!) towards a model closer to its' initial goals (information accessibility). I gleefully wait for the final riddance of old money communication and media giant conglomerations. Yet I fear the eventual and inevitable misuse of gTechnologies by the companies which now run our countries.

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Two areas where I think computers will be helping to do some interesting things next year are cellular networks and neurobiology.

I am also frustrated about how useless the advances in studying protein interaction networks has been to our understanding of how the cell actually works. In this respect I think next year we will continue to see more interaction data but hopefully put into better use. We need more coverage and in more species to understand how robust are complexes inside the cell to interaction re-wiring over evolutionary time for example. So far projects are aiming at species that are 800My to 1By apart and are impossible almost to compare.
To understand (be able to simulate and predict) the behaviour of a cell maybe we could start by understanding simpler interaction networks like a bacteria. Binary interaction information is not enough. We need concentrations, Kds, localization, etc (most of this things can be predicted).

I know very little about neurobiology (even less than about cellular networks :) but one thing that still bothers me about neurobiology studies is the missing information in between molecular knowledge and behaviour/cognition/memory/learning. Computers are already helping to "read" arrays of probes and to understand how the firing of multiple neurons code for thoughts of mechanical action. Remember the monkey thought controlling the robot ? Computers can also help to make the connection between how a change at the molecular level or a drug changes the firing of neurons in live animals and therefore change behaviour.

Having done over the last couple of years my share of Bioinformatics with web browsers, also some grinding with BioPerl and BioJava, I am hoping to see more XML-integrated Life Sciences for 2006,,,

XML + RDF for storing our data and being able to manipulate them with ease; and integration of disparate data sources.

Web services for on-the-fly assembly of data analysis pipelines, on-demand Bioinformatics computing, being able to up-scale and get away from the tedious HTML interface of most data sources out there,,, more science and less technical stuff.

But I think there is a lot more to go until we get there. And will keep going until the labs that create genomics - proteomics data, stop putting them in a custom mySQL database that hide behind an HTML interface,,, they have adopted the large-dataset producing techs like chips, mass-spec etc. ... Why they don't also adopt some semantic web or XML technologies to offer easily consumed data to the rest of the world ???