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Using Synthetic Biology to Teach Data Science

Author(s): Margaret S Saha1, Beteel Abu-Ageel1, Sanjana Challa1, Xiangyi Fang1, Chai Hibbert1, Anna Isler1, Elias Nafziger1, Adam Oliver1, Hanqiu Peng1, Julia Urban1, Vivian Zhu1

College of William and Mary

383 total view(s), 11 download(s)


The overall goal of synthetic biology is the application of engineering principles to design or redesign a variety of biological components and/or systems that serve useful purposes and do not currently exist in the natural world. This multidisciplinary field has already had significant impacts in medicine, agriculture, conservation, and green energy among others and has increasingly attracted the attention and interest of undergraduates.  With the exponentially expanding availability of sequence data and functional information from a diverse array of organisms and the growing number of modular, synthetic DNA parts, synthetic biology is increasingly a field that entails a substantial amount of data mining and analysis. Synthetic biology is also a discipline that is amenable to the undergraduate classroom as an effective means of teaching concepts in molecular genetics, cell biology and systems biology and simultaneously introducing students and increasing their comfort level with mining and analysis of big data. Here we will discuss various strategies and approaches at all levels of the curriculum to employ the exciting field of synthetic biology to introduce students to meaningful data mining, analysis, and application to engineering novel biological constructs.


This version contains an updated poster file and supplemental material.

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