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).
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 2 : Specification
This week discusses the concepts of functional specification applied in Synthetic Biology.
Topic 3 : Design
This week discusses the current computational methods used for designing Synthetic Biology.
Topic 4 : Assemble
This week introduces the automation aspects used in assembly.
Topic 5 : Standards, Optimization, Analysis
This week introduces SBOL and the associated standards that helps allow for the automation and engineering of Synthetic Biology.
Topic 6 : Registries, Modeling and Simulation
This week goes over the various concepts behind modeling, simulation and in Synthetic Biology.
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.