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Posted on Dec 12, 2013

Medicinal Microbes

If you asked most undergraduates whether they would be interested in factory work, you’d probably get some puzzled looks. But what if the “factory” was E. coli bacteria, and the “work” involved creating entire libraries of enzymes that might help defeat antibiotic resistance or cancer?

Undergraduates in Dr. Gavin Williams’ lab get to do just that, with hands-on, real research experience that starts as soon as they walk through the door.

Nature’s Factories

Williams is interested in drug development. Specifically, he’s interested in using E. coli as a factory to produce molecules that can be used as anti-cancer, anti-microbial and anti-viral drugs. What does that mean? Think about penicillin – it comes from a natural product (mold) that has antibacterial properties.

Nature makes these complex molecules by putting different types of enzymes into assembly lines, each of which can construct a molecule that may have a desirable quality, like the ability to stop bacterial growth or kill cancerous cells. In traditional drug development, scientists try to mimic natural processes by using chemicals. The problem is that these chemical processes are wasteful: in some cases it can take almost a ton of starting materials to produce one gram of the compound needed.

“I came to NC State because I knew I wanted to do research” — Matthew Draelos, senior chemistry and biochemistry major.

Williams and other chemists have found a shortcut. They go straight to the source – DNA – and pull out the particular genes that encode the enzymes they’re interested in. They can then manipulate those enzymes so that they only bind to other specific proteins. It’s like assembling a LEGO sculpture – but one in which the individual blocks, or enzymes, can only fit together in a certain way.

Then they put the enzymes they want inside E. coli bacteria. The enzymes self-assemble (by snapping together like LEGO bricks), and create the molecules needed for drug development. By piggybacking on nature, Williams and his team can quickly and inexpensively create huge numbers of different kinds of molecules that they can test, to find out which ones are the most effective against the diseases they want to treat.

Hands-on Research

Matthew Draelos and Taylor Courtney are both undergraduates doing work in the Williams lab. Draelos is a senior majoring in chemistry and biochemistry. He’s been part of the Williams lab since his freshman year.

“I came to NC State because I knew I wanted to do research and had heard it was easy to do undergraduate research here,” he says. “What captured my imagination about Williams’ research was that it’s both basic research and it’s medically relevant – we’re creating enzyme ‘mutants’ that have practical uses in creating new drugs.”

Undergraduates Taylor Courtney, Vishwas Rao and Matthew Draelos work in Gavin Williams' chemistry lab.

Undergraduates Taylor Courtney, Vishwas Rao and Matthew Draelos work in Gavin Williams’ biochemistry lab.

Courtney, a junior who is also majoring in chemistry and biochemistry, has only been with the lab for a few months, although she has previous research experience. “This kind of chemical biology meets a growing need in drug discovery, and I want to pursue this research in graduate school, so it’s a great opportunity for me,” she says.

Both students were encouraged to jump into the research from day one.

“You get trained to become a full partner in the lab,” Draelos says. “From the beginning I learned how to do DNA extraction, purification and analysis to make sure that we had what we wanted in its purest form. Now I’m creating my own libraries of enzyme mutants to test for usefulness in drug development. As each year passes, you are given more leeway to pursue projects independently that contribute to the lab.”

Courtney agrees. “You are definitely a full member of this lab – not just an assistant or someone who takes care of the supplies. And you’re definitely ready for graduate school or research work when you graduate.”

Hemant Desai, who earned a B.S. in chemistry and a B.A. in chemistry this year, is one of those graduates.

“I came in as a freshman, and even when my first project didn’t quite work out, it taught me a lot. My experiences in the lab prepared me really well for the topics I’m studying now in medical school.”

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Posted on Nov 5, 2013

Smarter Searching

NC State researchers have developed a way for search engines to provide users with more accurate, personalized search results. The challenge in the past has been how to scale this approach up so that it doesn’t consume massive computer resources. Now the researchers have devised a technique for implementing personalized searches that is more than 100 times more efficient than previous approaches.

At issue is how search engines handle complex or confusing queries. For example, if a user is searching for faculty members who do research on financial informatics, that user wants a list of relevant webpages from faculty, not the pages of graduate students mentioning faculty or news stories that use those terms. That’s a complex search.

“Similarly, when searches are ambiguous with multiple possible interpretations, traditional search engines use impersonal techniques. For example, if a user searches for the term ‘jaguar speed,’ the user could be looking for information on the Jaguar supercomputer, the jungle cat or the car,” says Dr. Kemafor Anyanwu, an assistant professor of computer science at NC State and senior author of a paper on the research. “At any given time, the same person may want information on any of those things, so profiling the user isn’t necessarily very helpful.”

NC State computer science researcher Kemafor Anyanwu.

NC State computer science researcher Kemafor Anyanwu.

Anyanwu’s team has come up with a way to address the personalized search problem by looking at a user’s “ambient query context,” meaning they look at a user’s most recent searches to help interpret the current search. Specifically, they look beyond the words used in a search to associated concepts to determine the context of a search.

So, if a user’s previous search contained the word “conservation,” it would be associated with concepts likes “animals” or “wildlife” and even “zoos.” Then, a subsequent search for “jaguar speed” would push results about the jungle cat — not the automobile or supercomputer — higher up in the results. The more recently a concept has been associated with a search, the more weight it receives when ranking results of a new search.

Search engines have also tried to identify patterns in user clicking behavior on search results to identify the most probable user intent for a search. However, such techniques are impersonal and are applied on a global basis. So, if the most frequent click pattern for a set of keywords is in a particular context, then that context becomes associated with queries for most or all users – even if your recent search history indicates that your query context is about jungle cats.

“What we are doing is different,” Anyanwu says. “We are identifying the context of search terms for individual users in real time and using that to determine a user’s intention for a specific query at a specific time. This allows us to deal more effectively with more complex searches than traditional search engines. Such searches are becoming more prevalent as people now use the Web as a key knowledge base supporting different types of tasks.”

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While Anyanwu and her team developed a context-aware personalized search technique over a year ago, the challenge has been how to scale this approach up. “Because running an ambient context program for every user would take an enormous amount of computing resources, and that is not feasible,” Anyanwu says.

However, Anyanwu’s research team has now come up with a technique that includes new ways to represent data, new ways to index that data so that it can be accessed efficiently, and a new computing architecture for organizing those indexes. The new technique makes a significant difference.

“Our new indexing and search computing architecture allows us to support personalized search for about 2,900 concurrent users using an 8GB machine, whereas an earlier approach supported only 17 concurrent users. This makes the concept more practical, and moves us closer to the next generation of search engines,” Anyanwu says.

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