Computational Synthetic Biology for Engineers
Knox - Rohner. et. al.
EC552/BE552 presents the field of computational synthetic biology through the lens of four distinct activities: Specification, Design, Assembly, and Test. Engineering students of all backgrounds are provided with an introduction to synthetic biology and then exposed to core challenges and approaches in each of these four areas. Homework assignments are provided which allow the students to use existing computational software to explore each of these themes. In addition, advanced concepts are presented around data management, design algorithms, standardization, and simulation challenges in the field. The course culminates in a group project in which the students apply computational design methods to an experimentally created system (working with graduate students in the Biological Design Center).
Note: This course is recommended for junior or senior undergraduate ECE students. Late-stage BME graduate students also are encouraged to take this course. In both cases, a strong programming background is required. Familiarity with C/C++, Java, and Python is strongly recommended. Large-scale class programming projects or experience with cloud computing services (e.g. AWS) are also a plus. No biology background is required but students should be interested in learning basic molecular biology experimental protocols and processes. Lab assignments include programming, discussions with wet lab scientists, and the design of microfluidic hardware. For more information see the class syllabus.
Douglas Densmore is an Associate Professor in the Department of Electrical and Computer Engineering at Boston University. His research focuses on the development of tools for the specification, design, and assembly of synthetic biological systems, he aims to raise the level of abstraction in synthetic biology by leveraging his experience in Electronic Design Automation (EDA).
Radhakrishna Sanka is a graduate student in CIDAR Lab whose primary research is developing design automation tools for realizing synthetic biology in microfluidics lab on a chip systems.
Topic 7 : Data Mining, Pattern Analysis and Microfluidics
Using examples from industry and academia, this week demonstrates how techniques like machine learning, data mining and pattern analysis are used to help engineer synthetic biology.
Topic 8 : Automation, Models of Computation, Genome Editing
This week goes over current hot topics in Synthetic Biology and discuss the possibilities for automation.