CSE 427: Computational Biology Algorithmic and analytic techniques underlying analysis of large-scale biological data sets such as DNA, RNA, and protein sequences or structures, expression and proteomic profiling. Hands-on experience with databases, analysis tools, and genome markers. Applications such as sequence alignment, BLAST, phylogenetics, and Markov models. Prerequisite: CSE 312; CSE 332.
CSE 487: Advanced Systems And Synthetic Biology Introduces advanced topics in systems and synthetic biology. Topics include advanced mathematical modeling; computational standards; computer algorithms for computational analysis; and metabolic flux analysis, and protein signaling pathways and engineering. Prerequisite: either BIOEN 401, BIOEN 423,E E 423, or CSE 486. Offered: jointly with BIOEN 424/E E 424; W.
CSE 488: Laboratory Methods In Synthetic Biology Designs and builds transgenic bacterial using promoters and genes taken from a variety of organisms. Uses construction techniques including recombination, gene synthesis, and gene extraction. Evaluates designs using sequencing, fluorescence assays, enzyme activity assays, and single cell studies using time-lapse microscopy. Prerequisite: either BIOEN 423, E E 423, or CSE 486; either CHEM 142, CHEM 144, or CHEM 145. Offered: jointly with BIOEN 425/E E 425.
CSE 490i: Neurobotics The field of Neurobotics lies at the intersection of robotics and medicine. It aims to build a robot-human closed loop system to alter the neural control of movement as a way to rehabilitate, assist, and enhance human motor control and learning capabilities. Typically, the primary target population is individuals with strokes, spinal cord injuries, traumatic brain injuries, and other injuries that inhibit daily activities. However, it could also target sports medicine, military, and entertainment applications. This course is an introductory design course in Neurobotics focusing on learning about human neural control of movement, using physiological signals as inputs, and controlling a mechanical device. Students will learn simple control laws, hands on experience and programming in controlling robots, and applying knowledge of human movements to move the robot. There is a design project competition at the end of quarter.
CSE 527: Computational Biology Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as network reconstruction and analysis, sequence analysis, regulatory analysis and genetic analysis. Techniques such as Bayesian networks, Gaussian graphical models, structure learning, expectation-maximization. Prerequisite: graduate standing in biological, computer, mathematical or statistical science, or permission of instructor.
CSE 528: Computational Neuroscience Introduction to computational methods for understanding nervous systems and the principles governing their operation. Topics include representation of information by spiking neurons, information processing in neural circuits, and algorithms for adaptation and learning. Prerequisite: elementary calculus, linear algebra, and statistics, or by permission of instructor. Offered: jointly with NEUBEH 528.
CSE 529: Neural Control Of Movement: A Computational Perspe Systematic overview of sensorimotor function on multiple levels of analysis, with emphasis on the phenomenology amenable to computational modeling. Topics include musculoskeletal mechanics, neural networks, optimal control and Bayesian inference, learning and adaptation, internal models, and neural coding and decoding. Prerequisite: vector calculus, linear algebra, MATLAB, or permission of instructor. Offered: jointly with AMATH 533; W.