Virginia Tech researchers Padma Rajagopalan and T.M. Murali go beyond the boundaries of engineering disciplines to gain insights into emerging viral infections, such as COVID-19.
22 Aug 2022
Biomedical discovery can be as much about relationships as it is about cell structures and artificial intelligence — at least in the case of married College of Engineering researchers Padma Rajagopalan and T.M. Murali.
For the past decade, the couple has paired Rajagopalan’s tissue engineering work in the Department of Chemical Engineering with Murali’s specialty in computational biology in the Department of Computer Science to address critical global challenges.
Their most recent project, published in GigaScience, used computational methods to identify human proteins targeted by COVID-19 infection to help determine if treatments already approved by the U.S. Food and Drug Administration might be used to prevent infection or treat serious complications of the disease, such as blood clots.
“If you imagine a protein from the virus, perhaps it binds to a protein in a human cell. And this binding is necessary in order for the virus to replicate itself,” Rajagopalan said. “If that were the case, then what if we were to target and control that human protein in order to prevent the virus from replicating itself? That’s the basic idea — to try to develop host-oriented drugs.”
During the course of the work, the interdisciplinary team was able to prove that its computational method can accurately predict which human proteins and biological processes are affected by the virus. The method has wider potential, too, as the overall framework developed might be extended to other viruses that could spawn epidemics and even pandemics.
The framework can also save time and reduce costs in the lab by identifying which tissue-based experiments have the highest likelihood of success.
“We’ve been able to use computation as a way to do more targeted tissue engineering, so that’s really how we have built a scientific collaboration,” Rajagopalan said. “And so when we did this project, it was relatively easy to figure out how to take this computational data and then build a biological framework that would be of interest to physicians who may be looking for targets.”
This project began before COVID-19 vaccines were available, but its results remain important, Rajagopalan said. Some people can’t take the vaccines, others get breakthrough infections, and mutations can circumvent existing immunity.
“If vaccines cannot prevent every infection, perhaps we can dial up auxiliary medicines which can be given to patients who have been infected by the virus,” Rajagopalan said.
The work could help speed up the response to emerging illnesses.
“Discovering new drugs is a very time-consuming and costly process, so what we were trying to see initially was if we could repurpose existing drugs to treat symptoms of COVID-19,” said Nure Tasnina, a graduate student and an author of the paper.
This is just one of several joint projects the couple and their students have worked on over more than a decade. Together, these efforts illustrate the power of interdisciplinary research to meet emerging biomedical needs. But it took many years to get there.
The couple met at Brown University, where both were pursuing doctoral degrees. They later married, and Rajagopalan came to Virginia Tech in 2007 to join Murali, who had been in Blacksburg since 2003.
They brought their labs together at that point, when Rajagopalan needed some computational work done for a liver tissue project she was working on. They wrote a joint proposal that received funding and ultimately changed the trajectory of their work.
Together they formed the Center for Systems Biology of Engineered Tissues, which operated from 2009 to 2016, bringing in about $16 million in funding and including faculty from several colleges across the university, Rajagopalan said.
The broad collaborations benefitted the research, but it also led them to a new educational model when that first liver project paired a student from each of their labs together.
“He was a computer science student, and she was a chemical engineering student,” Rajagopalan said. “She became very fluent in computational techniques, and he became far more aware of the biology of the systems that we developed. Then we saw the synergy between our two students. We were really very, very pleasantly happy to see how much they learned from each other.”
The COVID-19 project alone included graduate students at Virginia Tech and an undergraduate from Boston University, as well as researchers from Boston University, the University of California, Los Angeles; Colorado School of Mines; and Microsoft’s AI for Good lab in Redmond, Washington.
“Meeting the global threat of future pandemics requires these broad-reaching collaborations,” Murali said.
To facilitate this and other work, Rajagopalan and Murali together direct the Computational Tissue Engineering Interdisciplinary Graduate Education Program, and are both affiliated with the Interdisciplinary Ph.D. Program in Genetics, Bioinformatics, and Computational Biology.
Their teams are currently collaborating with faculty across Virginia Tech on another high-impact joint research project that goes beyond disciplinary boundaries.
Rajagopalan’s lab focuses on liver tissue research, especially liver toxicity studies. Testing new drugs on liver cells is an important component of ensuring that pharmaceuticals and other products are safe. But finding the best human liver cells for use in the lab is tricky — and expensive. A range of cell donor behaviors, from use of alcohol and drugs to weight gain, can affect the research usefulness of human liver cells.
In recent years, scientists have developed a method for genetically modifying adult skin cells to function more like stem cells. These can then be grown into liver cells. However, not all their characteristics meet the standards needed for all research, Rajagopalan said.
Together she and Murali are working on a computational approach to identifying where these new liver mimics excel, where they fall short, and how they can be improved.
Since Rajagopalan and Murali brought their first team of students together, the demand for interdisciplinary work has only grown.
“I see people with a computer science background getting into every discipline. We now have access to big data and can take advantage of machine learning and deep learning models,” graduate student Nure Tasnina said. “So there will be collaboration among computer science and other departments in the future as this is the smartest way to make the best out of everyone’s expertise.”
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