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Data mining opens the door to predictive neuroscience
Date:4/11/2012

The discovery, using state-of-the-art informatics tools, increases the likelihood that it will be possible to predict much of the fundamental structure and function of the brain without having to measure every aspect of it. That in turn makes the Holy Grail of modelling the brain in silicothe goal of the proposed Human Brain Projecta more realistic, less Herculean, prospect. "It is the door that opens to a world of predictive biology," says Henry Markram, the senior author on the study, which is published this week in PLoS ONE.

Within a cortical column, the basic processing unit of the mammalian brain, there are roughly 300 different neuronal types. These types are defined both by their anatomical structure and by their electrical properties, and their electrical properties are in turn defined by the combination of ion channels they presentthe tiny pores in their cell membranes through which electrical current passes, which make communication between neurons possible.

Scientists would like to be able to predict, based on a minimal set of experimental data, which combination of ion channels a neuron presents. They know that genes are often expressed together, perhaps because two genes share a common promoterthe stretch of DNA that allows a gene to be transcribed and, ultimately, translated into a functioning proteinor because one gene modifies the activity of another. The expression of certain gene combinations is therefore informative about a neuron's characteristics, and Georges Khazen and co-workers hypothesised that they could extract rules from gene expression patterns to predict those characteristics.

They took a dataset that Prof Markram and others had collected a few years ago, in which they recorded the expression of 26 genes encoding ion channels in different neuronal types from the rat brain. They also had data classifying those types according to a neuron's morphology, its electrophysiological properties and its
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Contact: Lionel Pousaz
lionel.pousaz@epfl.ch
41-795-597-161
Ecole Polytechnique Fdrale de Lausanne
Source:Eurekalert  

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