Featured Research

from universities, journals, and other organizations

Algorithm Finds The Network -- For Genes Or The Internet

Date:
March 19, 2008
Source:
Washington University in St. Louis
Summary:
Human diseases and social networks seem to have little in common. However, at the crux of these two lies a network, communities within the network, and farther even, substructures of the communities. Computer scientists and geneticists can now use a new computer program to automatically discover communities and their subtle structures in a variety of networks.

Weixiong Zhang has created a mathematical recipe - also known as an algorithm - that automatically discovers communities and their subtle structures in various networks, from the Internet to genetic lattices.
Credit: Image courtesy of Washington University in St. Louis

Human diseases and social networks seem to have little in common. However, at the crux of these two lies a network, communities within the network, and farther even, substructures of the communities. In a recent paper in Physical Review E, Weixiong Zhang, Ph.D., Washington University associate professor of computer science and engineering and of genetics, along with his Ph.D. student, Jianhua Ruan, published an algorithm (a recipe of computer instructions) to automatically identify communities and their subtle structures in various networks.

Many complex systems can be represented as networks, Zhang said, including the genetic networks he studies, social networks and the Internet itself. The community structure of networks features a natural division in which the vertices in each subnetwork are highly involved with each other, though connected less strongly with the rest of the network. Communities are relatively independent of one another structurally, but researchers think that each community may correspond to a fundamental functional unit. A community in a genetic network usually contains genes with similar functions, just as a community on the World Wide Web often corresponds to Web pages on similar topics.

All Zhang and Ruan need are data. Their algorithm is more scalable than existing similar algorithms and can detect communities at a finer scale and with a higher accuracy. One impact of having such a computational biology tool is found in the genomics field. Using this tool, researchers may be better able to identify and understand communities of genes and their networks as well as how they cooperate in causing diseases, such as sepsis, virus infections, cancer and Alzheimer's disease.

Versatile math tool

Zhang and Ruan's algorithm is so versatile that it has been applied to identify the community structure of a network of co-expressed genes involved in bacterial sepsis.

"This is a tool not only for biological research, but also for sociological research," Zhang said. It can determine, for instance, how people interact in social networks and how scientists collaborate in scientific research.

In biological systems there are lots of communities with many proteins involved to form complexes. "We can use this tool to identify structures embedded in the data," Zhang said. "We've identified the substructures of three different RNA polymerase complexes from noisy data, for instance, which are crucial for gene transcription."

Zhang began his computer science career as a specialist in artificial intelligence, but in recent years he has focused more on computational biology. His goal is to use computational means to solve some basic biology problems and those related to human diseases. For example, his group studied a basic problem of the transcription mechanism of microRNAs, which are small, noncoding RNAs that regulate the development and stress responses of nearly all eukaryotic species that have been studied. Using machine learning techniques, Zhang and his collaborators showed that almost all intergenic microRNA genes in four model species, human, mouse, rice and mustard plant (Arabidopsis), are transcribed by RNA polymerase II, which transcribes protein-coding genes. The results were published in PLoS Computational Biology, 3(3):e37 (2007).

Multidisciplinary research that combines computational approaches with biological data is a hallmark of research themes in Zhang's group. As another example, in a paper published in Genome Biology, 7(6):R49 (2006), Zhang and his Ph.D. student, Guandong Wang, developed an algorithm called WordSpy that identifies cis-regulatory elements — short DNA sequences that are critical to the regulation of gene expression — from a large amount of genome sequences.

Stealth from the ancient Greeks

WordSpy was inspired by an old information-hiding technique called stegography, which can be traced back to ancient Greece. As such, their method can be used to analyze not only genomic sequences, but also natural languages. In fact, their method has been extended to segment words and phrases in Chinese.

Aside from studying networks, Zhang also has formed a broad network of collaborations with scientists across the WUSTL campus and outside of the university. The problems he studies are diverse, ranging from stress responses and virus infection in plants, such as rice, to human diseases, including Alzheimer's disease, herpes virus infection, sepsis, cardiac hypertrophy, lung cancer and lung transplantation. The computational tools his group has developed are helping him and his collaborators come to grips with how perturbation to gene expression can lead to complex traits and human diseases as well as how microRNAs regulate gene expression.

Zhang recently was awarded a grant from the Alzheimer's Association to develop computational systems biology methods for analyzing gene expression perturbation in diseased brains. He has been collaborating with scientists in the Washington University School of Medicine and Scripps Institute in La Jolla, Calif., to study roughly 30 postmortem brain samples of people who died from Alzheimer's disease.

"I'm interested in modeling gene expression perturbation in diseased brains and am looking for the genetic signature," Zhang said. "Due to the complexity of Alzheimer's disease, we are developing other tools. It's a polygenic disease, with a lot of genes at work. I'm sure we'll find that a network is involved."

Reference: Physical Review E 77:016104 (2008)


Story Source:

The above story is based on materials provided by Washington University in St. Louis. Note: Materials may be edited for content and length.


Cite This Page:

Washington University in St. Louis. "Algorithm Finds The Network -- For Genes Or The Internet." ScienceDaily. ScienceDaily, 19 March 2008. <www.sciencedaily.com/releases/2008/03/080317123237.htm>.
Washington University in St. Louis. (2008, March 19). Algorithm Finds The Network -- For Genes Or The Internet. ScienceDaily. Retrieved April 17, 2014 from www.sciencedaily.com/releases/2008/03/080317123237.htm
Washington University in St. Louis. "Algorithm Finds The Network -- For Genes Or The Internet." ScienceDaily. www.sciencedaily.com/releases/2008/03/080317123237.htm (accessed April 17, 2014).

Share This



More Computers & Math News

Thursday, April 17, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

German Researchers Crack Samsung's Fingerprint Scanner

German Researchers Crack Samsung's Fingerprint Scanner

Newsy (Apr. 16, 2014) German researchers have used a fake fingerprint made from glue to bypass the fingerprint security system on Samsung's new Galaxy S5 smartphone. Video provided by Newsy
Powered by NewsLook.com
Twitter, Apple Social Data Purchases Likely to Spur More Mergers and Acquisitions

Twitter, Apple Social Data Purchases Likely to Spur More Mergers and Acquisitions

TheStreet (Apr. 16, 2014) The social media data space is likely to see more mergers and acquisitions following Twitter Inc.'s acquisition of tweet analyzer Gnip Inc. on Tuesday and Apples Inc.'s purchase of Topsy Labs Inc. back in December. One firm in particular, the U.K.'s DataSift Inc., could be on the list of potential buyers. Among other social media startups that could be ripe for picking is Banjo, whose mobile app provides aggregated content by topic and location. Banjo could also be a good fit for Twitter. Video provided by TheStreet
Powered by NewsLook.com
Bitcoin Exchange Mt. Gox to Liquidate After Rebuilding Rejected

Bitcoin Exchange Mt. Gox to Liquidate After Rebuilding Rejected

TheStreet (Apr. 16, 2014) Bitcoin exchange Mt. Gox has agreed to liquidate after a Japanese court rejected its plans to rebuild, according to a report by the Wall Street Journal. Mt. Gox filed for bankruptcy protection in February after announcing about 850,000 bitcoins, worth around $454 million at today's rates, may have been stolen by hackers. It has since recovered 200,000 of the missing bitcoins. The court put Mt. Gox's assets under a provisional administrator's control until bankruptcy proceedings begin. Video provided by TheStreet
Powered by NewsLook.com
BlackBerry: The Crash That Launched 1,000 Startups

BlackBerry: The Crash That Launched 1,000 Startups

Reuters - Business Video Online (Apr. 16, 2014) Tech startups in BlackBerry's hometown of Waterloo, Ontario, are tapping talent from the struggling smartphone company and filling the void left in the region by its meltdown. Reuters correspondent Euan Rocha visits the region that could become Canada's Silicon Valley. Video provided by Reuters
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:

Breaking News:
from the past week

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile: iPhone Android Web
Follow: Facebook Twitter Google+
Subscribe: RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins