‘Big data’ drills down into metabolic details

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Science Daily.

Rice University bioengineers have introduced a fast computational method to model tissue-specific metabolic pathways. Their algorithm may help researchers find new therapeutic targets for cancer and other diseases.

Metabolic pathways are immense networks of biochemical reactions that keep organisms functioning and are also implicated in many diseases.

They present the kind of challenges “big data” projects are designed to address. In recent years, computer scientists have built many ways to model these networks in humans, particularly since the 2007 introduction of the first genome-scale model of human metabolic pathways.

But the big picture doesn’t have all the answers. A pathway in the liver might not act the same way as an identical chain in the muscle. To that end, the Rice lab of bioengineer Amina Qutub designed an algorithm, Cost Optimization Reaction Dependency Assessment (CORDA), to model metabolic pathways specific to their home tissues.

CORDA is detailed in an open-access paper by Qutub and Rice graduate student André Schultz this month in PLOS Computational Biology.

In the new CORDA algorithm, metabolic reactions not supported by experimental data are assigned a high “cost” that gives them less importance. For accuracy, this cost is minimized in a method that depends on flux…

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