Kate E Galloway
synthetic biology. molecular systems biology. cell fate circuits.
TEACHING INTERESTS
My comprehensive Chemical Engineering education from UC Berkeley and the California Institute of Technology has equipped me to teach a range of classes. From quantitative engineering courses such as kinetics, transport, and thermodynamics to topics in systems biology and biochemistry, I am prepared to teach at both undergraduate and graduate levels. Also, through my research I am very familiar with control theory, molecular and cellular biology, genetics, neurobiology, epigenetics, and bioinformatics.
COURSE DEVELOPMENT
At Caltech, I co-founded and led an informal “biocontrol” journal club and later co-authored a review article on synthetic biology. From my experience, I envision developing a course that I would title “Design principles in systems and synthetic biology.” This course, which focuses on molecular and system design with applications in stem cell biology and neurobiology, will formally introduce students to concepts related to my research, highlighting engineering principles in stem cell biology. The goal of the course is to introduce advanced undergrad and grad students from quantitative backgrounds into problem-solving opportunities at the interface of molecular biology, biochemistry, and engineering.
The first two-thirds of the course will cover the following: conceptual frameworks for biological design; advances in genetic synthesis; layers of control; a synthetic biology toolbox of parts and devices; synthetic circuits for dynamic, logical, and computing functions; approaches in engineering digital and analog biological systems; illuminating principles in native systems; and applications to questions in stem cell biology. In order to promote critical thinking skills and foster independence in research, students will be asked to use scientific literature to identify engineering opportunities within a biological system to improve performance of a defined metric (e.g. speed, fidelity, novel function). Students will design an enhanced system, outline experiments to optimize the performance of their chosen system, and utilize computational models to demonstrate their improved system. In the final third of the course, students will present their systems and proposals in class.