Using Simple Genome, Researchers Move Personalized Medicine Closer to Reality

October 14, 2009

( -- Researchers at Columbia University have developed a statistical method that accurately predicts how an organism will respond to dozens of commonly used drugs. This clinical and conceptual advance moves medical science a step closer to an era of personalized medicine -- one where doctors could prescribe treatments based on an individual patient's genome.

“Our study shows proof of concept and suggests how we should go about developing personalized medicine,” said Dana Pe’er, an assistant professor in the department of biological sciences and head of Columbia’s Computational Systems Biology Lab. “The hope is that one day you’ll go to the doctor, and for every disease you might have, they’ll say, ‘Here’s the medicine for you and the dose for you, and you won’t have side effects.’”

Pe’er and her co-authors, including doctoral student Bo-Juen Chen, worked with yeast as a . Their findings appear in the current issue of Molecular Systems Biology.

Unlike many other genomic-medicine studies that focus solely on , Pe’er’s study relied on a combination of DNA and RNA data to predict drug resistance in yeast and identify the genes that cause it. RNA, which is synthesized from DNA, reflects which genes in a cell are turned off and on—information known as gene expression.

“RNA gives us a much more sophisticated measure than DNA of what’s going on in the cell,” said Pe’er.

She and Chen started with a set of data containing information on how well or how poorly 104 strains of yeast grew in the presence of 94 chemicals, or drugs. Each strain came with a genomic sequence and a gene expression profile. After identifying the genes responsible for variation among the strains, the team designed a computer function to determine, for each drug, which genetic features correlated with resistance. Pe’er and her coworkers then took previously untested strains of yeast and asked the computer to predict their level of resistance based on genomic and data.

Their program, called Camelot, accurately predicted how well or how poorly the yeast grew in the presence of 87 out of 94 drugs. To test these results, they went back into the lab and genetically modified the yeast, deleting the genes they had identified as causing drug resistance. When they retested the modified yeast, exposing them to the drugs once more, they saw that the strains were no longer resistant, just as their model had predicted.

“To our knowledge, our approach is the first to systematically predict and identify the genes that cause it,” said Pe’er.

Provided by The Earth Institute at Columbia University (news : web)

Explore further: Genetic interactions are the key to understanding complex traits

Related Stories

Evolution and the workaround

December 10, 2006

Living things are resourceful, which is a comforting thought unless the living thing in question is a pathogen or a cancer cell. Noxious cells excel at developing drug resistance, outwitting immune systems, and evading cellular ...

Rewrite the textbooks: Transcription is bidirectional

January 25, 2009

Genes that contain instructions for making proteins make up less than 2% of the human genome. Yet, for unknown reasons, most of our genome is transcribed into RNA. The same is true for many other organisms that are easier ...

Recommended for you

Study shows how giraffe assassin bugs outwit spider prey

October 26, 2016

(—A biologist at Macquarie University in Australia has discovered the secret behind the giraffe assassin's ability to catch and kill spiders in their webs. In his paper published on the open access site Royal Society ...

New analysis of big data sheds light on cell functions

October 26, 2016

Researchers have developed a new way of obtaining useful information from big data in biology to better understand—and predict—what goes on inside a cell. Using genome-scale models, researchers were able to integrate ...

Structure of key DNA replication protein solved

October 25, 2016

A research team led by scientists at the Icahn School of Medicine at Mount Sinai (ISMMS) has solved the three-dimensional structure of a key protein that helps damaged cellular DNA repair itself. Investigators say that knowing ...


Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.