A UT Dallas interdisciplinary researcher has launched a human genome analysis project intended to create a mathematical model that improves the efficacy of cancer drugs while reducing their manufacturing costs by as much as 30 percent.
A three-year, $305,000 grant from the National Science Foundation will fund Dr. Mathukumalli “Sagar” Vidyasagar’s development of a new method for detecting DNA sequence similarities.
“What I propose to do is to find mathematical models of how a cross section of cancer patients would react to a particular clinical trial drug,” he said. “If it is likely that an unacceptably high fraction would react adversely, that would allow the developers to kill that program in a timely manner.”
Sagar’s statistical method would reduce the size of the massive databases that store genetic data. With his model, clinicians would be able to sub-sample those databases while still retaining inherent statistical features such as correlations, thus saving time and money without sacrificing accuracy.
This is really interesting. It’s cool to see statistics research in a diverse range of fields, particularly ones such as this that are potentially very beneficial.
Also, “systems biologist” is a really cool title/description.
Mathematicians have now figured out the dynamics that drive locusts across the landscape, devastating everything underfoot — and the math says people will never be able to predict where the little buggers will go.
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Further analysis showed that a number of random factors influence when the insects decide to change direction. Mathematically, the change in the locusts’ direction is similar to switches in magnetic properties that occur among clumps of magnetic particles at high temperatures, Escudero says. In both cases, random influences accumulate until suddenly the whole system changes its behavior.
(via ijbarton)
