ÐãÉ«¶ÌÊÓƵ

Dr. Forrest Sheng Bao, principal investigator, has been awarded a three-year National Science Foundation grant of $232,523 for his research project titled “Collaborative Research: Productivity Prediction of Microbial Cell Factories Using Machine Learning and Knowledge Engineering.” The grant began on Aug. 1.

Through this NSF grant, Bao, an assistant professor in the Department of Electrical and Computer Engineering, will develop methods to use two artificial intelligence approaches — machine learning and knowledge representation — to extract models and knowledge hidden in the published data from tens of thousands of synthetic biology papers about two model species, the yeast and the E. coli. Bao hopes this approach will speed up the costly design-build-test-learn cycle of strain development. 

Dr. Forrest Sheng Bao

Bacteria are known to be able to convert chemicals. Yeast for example, can transform sugars into ethanol. Systems and synthetic biology researchers have been long attempting to develop "cell factories" by genetically engineering bacteria so that they are able to produce chemicals such as medications or biofuels, or to degrade chemicals such as crude oil, more effectively. However, existing modeling approaches fail to capture the complicated metabolic responses in such engineered cells.

“A technology that can not be commercialized is a good idea on paper only,” says Bao. “Through our work, we can use artificial intelligence to mine the knowledge out of massive amounts of data to significantly shorten the design cycle of bacterial factories.”

A data-driven approach will allow computers to learn lessons from previous data to facilitate the future design.

“According to the late Steve Jobs (co-founder of Apple), the computer is a bicycle for our minds. I have faith that artificial intelligence can help us understand the beauty between molecules in cells from the published data that is too big to comprehend by humans,” adds Bao, who joined ÐãÉ«¶ÌÊÓƵ in fall 2013.