Computer scientists develop new way to study molecular networks

Jan. 24, 2013 — In biology, molecules can have multi-way interactions within cells, and until recently, computational analysis of these links has been "incomplete," according to T. M. Murali, associate professor of computer science in the College of Engineering at Virginia Tech.

His group authored an article on their new approach to address these shortcomings, titled "Reverse Engineering Molecular Hypergraphs," that received the Best Paper Award at the recent 2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine.

Intricate networks of connections among molecules control the processes that occur within cells. The "analysis of these interaction networks has relied almost entirely on graphs for modeling the information. Since a link in a graph connects at most two molecules (e.g., genes or proteins), such edges cannot accurately represent interactions among multiple molecules. These interactions occur very often within cells," the computer scientists wrote in their paper.

To overcome the limitations in the use of the graphs, Murali and his students used hypergraphs, a generalization of a graph in which an hyperedge can connect multiple molecules.

"We used hypergraphs to capture the uncertainty that is inherent in reverse engineering gene to gene networks from systems biology datasets," explained Ahsanur Rahman, the lead author on the paper. "We believe hypergraphs are powerful representations for capturing the uncertainty in a network's structure."

They developed reliable algorithms that can discover hyperedges supported by sets of networks. In ongoing research, the scientists seek to use hyperedges to suggest new experiments. By capturing uncertainty in network structure, hyperedges can directly suggest groups of genes for which further experiments may be required in order to precisely discover interaction patterns. Incorporating the data from these experiments might help to refine hyperedges and resolve the interactions among molecules, resulting in fruitful interplay and feedback between computation and experiment.

Murali, and his students Ahsanur Rahman and Christopher L. Poirel, both doctoral candidates, and David L. Badger, a software engineer in Murali's group, all of Blacksburg, Va., and all in the computer science department, used funding from the National Institutes of Health and the National Science Foundation to better understand this uncertainty in these various forms of interactions.

Murali is also the co-director of the Institute for Critical Technology and Applied Science's Center for Systems Biology of Engineered Tissues and the associate program director for the computational tissue engineering interdisciplinary graduate education program at Virginia Tech.

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Chemist applies Google software to webs of the molecular world

ScienceDaily (Feb. 13, 2012) — The technology that Google uses to analyze trillions of Web pages is being brought to bear on the way molecules are shaped and organized. Aurora Clark, an associate professor of chemistry at Washington State University, has adapted Google's PageRank software to create moleculaRnetworks, which scientists can use to determine molecular shapes and chemical reactions without the expense, logistics and occasional danger of lab experiments.

"What's most cool about this work is we can take technology from a totally separate realm of science, computer science, and apply it to understanding our natural world," says Clark.

Clark and colleagues from the University of Arizona discuss the software in a recent online article in The Journal of Computational Chemistry. Their work is funded by the U.S. Department of Energy's Basic Energy Sciences program.

The software focuses on hydrogen bonds in water, earth's most abundant solvent and a major player in most every biological process.

"From a biological or chemical standpoint, water is where it's at," says Clark.

In living things, water can perform key functions like helping proteins fold or organizing itself around the things it dissolves so molecules stay apart in a fluid state. But the processes are dazzlingly complex, changing in fractions of a second and in myriad possible forms.

Much like the trillion-plus Web domains on the Internet.

Google's PageRank software, developed by its founders at Stanford University, uses an algorithm -- a set of mathematical formulas -- to measure and prioritize the relevance of various Web pages to a user's search. Clark and her colleagues realized that the interactions between molecules are a lot like links between Web pages. Some links between some molecules will be stronger and more likely than others.

"So the same algorithm that is used to understand how Web pages are connected can be used to understand how molecules interact," says Clark.

The PageRank algorithm is particularly efficient because it can look at a massive amount of the Web at once. Similarly, it can quickly characterize the interactions of millions of molecules and help researchers predict how various chemicals will react with one another.

Ultimately, researchers can use the software to design drugs, investigate the roles of misfolded proteins in disease and analyze radioactive pollutants, Clark says.

"Computational chemistry is becoming the third leg in the stool of chemistry," the other two being experimental and analytical chemistry, says Clark. "You can call it the ultimate green chemistry. We don't produce any waste. No one gets exposed to anything harmful."

Clark, who uses Pacific Northwest National Laboratories supercomputers and a computer cluster on WSU's Pullman campus, specializes in the remediation and separation of radioactive materials. With computational chemistry and her Google-based software, she says, she "can learn about all those really nasty things without ever touching them."

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The above story is reprinted from materials provided by Washington State University. The original article was written by Eric Sorensen.

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Journal Reference:

Barbara Logan Mooney, L.René Corrales, Aurora E. Clark. MoleculaRnetworks: An integrated graph theoretic and data mining tool to explore solvent organization in molecular simulation. Journal of Computational Chemistry, 2012; DOI: 10.1002/jcc.22917

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