Improving the quality of laboratory data with computer modeling

Feb 29, 2008

Many areas of research and medicine rely critically upon knowing a person’s individual immune system proteins, as they determine an individual’s ability to fight disease or mistakenly attack their own tissues. However, obtaining this information is costly and difficult.

A new study, published February 29 in the open-access journal PLoS Computational Biology, Listgarten et al demonstrate how statistical modeling can help researchers obtain this information more easily and cost effectively.

At the core of the human immune response is the train-to-kill mechanism in which specialized immune cells are sensitized to recognize small pieces of foreign pathogens (e.g., HIV). Following this sensitization, these cells are then activated to kill cells that display this same piece of pathogen. However, for sensitization and killing to occur, the pathogen must be “paired up” with one of the infected person’s specialized immune proteins—an HLA (human leukocyte antigen).

The way in which pathogen peptides interact with these HLA proteins defines if and how an immune response will be generated. Therefore, knowing which HLA proteins a person has is vital in transplant medicine, finding immunogenetic risk factors for disease, and understanding the way viruses like HIV mutate inside their host and evade the immune system.

The model uses a large set of previously measured, high-quality HLA data to find statistical patterns in this type of data. Using these patterns, the team from Microsoft Research, the National Cancer Institute, Massachusetts General, and the University of Oxford is able to take low-quality HLA data and clean it up so that it is of higher quality than that originally measured in the laboratory. With this publication, Listgarten and co-authors have made a public tool available to the research community so that others can improve the quality of their HLA data and thus study individual immune systems more effectively.

Citation: Listgarten J, Brumme Z, Kadie C, Xiaojiang G, Walker B, et al. (2008) Statistical Resolution of Ambiguous HLA Typing Data. PLoS Comput Biol 4(2): e1000016. doi:10.1371/journal.pcbi.1000016 (www.ploscompbiol.org/doi/pcbi.1000016)

Source: Public Library of Science

Explore further: Battling superbugs with gene-editing system

add to favorites email to friend print save as pdf

Related Stories

New Parkinson's gene is linked to immune system

Aug 27, 2010

A hunt throughout the human genome for variants associated with common, late-onset Parkinson's disease has revealed a new genetic link that implicates the immune system and offers new targets for drug development.

Recommended for you

Battling superbugs with gene-editing system

5 hours ago

In recent years, new strains of bacteria have emerged that resist even the most powerful antibiotics. Each year, these superbugs, including drug-resistant forms of tuberculosis and staphylococcus, infect ...

Dwindling wind may tip predator-prey balance

Sep 19, 2014

Bent and tossed by the wind, a field of soybean plants presents a challenge for an Asian lady beetle on the hunt for aphids. But what if the air—and the soybeans—were still?

Environmental pollutants make worms susceptible to cold

Sep 19, 2014

Some pollutants are more harmful in a cold climate than in a hot, because they affect the temperature sensitivity of certain organisms. Now researchers from Danish universities have demonstrated how this ...

Research helps steer mites from bees

Sep 19, 2014

A Simon Fraser University chemistry professor has found a way to sway mites from their damaging effects on bees that care and feed the all-important queen bee.

User comments : 0