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BIOE 44: Fundamentals for Engineering Biology Lab

An introduction to techniques in genetic, molecular, biochemical, cellular and tissue engineering. Lectures cover advances in the field of synthetic biology with emphasis on genetic engineering, plasmid design, gene synthesis, genetic circuits, and safety and bioethics. Lab modules will teach students how to conduct basic lab techniques, add/remove DNA from living matter, and engineer prokaryotic and eukaryotic cells. Team projects will support practice in component engineering with a focus on molecular design and quantitative analysis of experiments, device and system engineering using abstracted genetically encoded objects, and product development. Concurrent or previous enrollment in BIO 82 or BIO 83. Preference to declared BioE students. Students who have not declared BioE should email Alex Engel to get on a waitlist for a permission code to enroll. Class meets in Shriram 112, lab meets in Shriram 114. Scientific Method and Analysis (SMA).
Terms: Aut, Win | Units: 4 | UG Reqs: WAY-SMA

BIOE 101: Systems Biology (BIOE 210)

Complex biological behaviors through the integration of computational modeling and molecular biology. Topics: reconstructing biological networks from high-throughput data and knowledge bases. Network properties. Computational modeling of network behaviors at the small and large scale. Using model predictions to guide an experimental program. Robustness, noise, and cellular variation. Prerequisites: CME 102; BIO 82, BIO 84; or consent of instructor.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR

BIOE 141A: Senior Capstone Design I

First course of two-quarter sequence. Team-based project introduces students to the process of designing new bioengineering technologies to address unmet societal needs. Methods and processes include need specification, brainstorming, concept selection, system specification, and system engineering/design via iterative prototyping and experimentation. First quarter focuses on the innovation process, with teams going from need-specification to initial project plans. Lectures and labs include interactive project work and expert speakers. Prerequisites: BIOE123 and BIOE44.
Terms: Aut | Units: 4

BIOE 191: Bioengineering Problems and Experimental Investigation

Directed study and research for undergraduates on a subject of mutual interest to student and instructor. Prerequisites: consent of instructor and adviser. (Staff)
Terms: Aut, Win, Spr | Units: 1-5 | Repeatable for credit
Instructors: Abu-Remaileh, M. (PI) ; Altman, R. (PI) ; Andriacchi, T. (PI) ; Appel, E. (PI) ; Bammer, R. (PI) ; Banik, S. (PI) ; Barron, A. (PI) ; Batzoglou, S. (PI) ; Bintu, L. (PI) ; Boahen, K. (PI) ; Brophy, J. (PI) ; Bryant, Z. (PI) ; Butte, A. (PI) ; Camarillo, D. (PI) ; Carter, D. (PI) ; Cochran, J. (PI) ; Coleman, T. (PI) ; Covert, M. (PI) ; Cremer, J. (PI) ; Daniel, B. (PI) ; Deisseroth, K. (PI) ; Delp, S. (PI) ; Endy, D. (PI) ; Engel, A. (PI) ; Ennis, D. (PI) ; Eshel, N. (PI) ; Fahrig, R. (PI) ; Feinstein, J. (PI) ; Fischbach, M. (PI) ; Fisher, D. (PI) ; Fordyce, P. (PI) ; Garten, M. (PI) ; Gold, G. (PI) ; Goodman, S. (PI) ; Graves, E. (PI) ; Gurtner, G. (PI) ; Hargreaves, B. (PI) ; Heilshorn, S. (PI) ; Hernandez-Lopez, R. (PI) ; Huang, K. (PI) ; Huang, P. (PI) ; Kornberg, R. (PI) ; Kovacs, G. (PI) ; Krummel, T. (PI) ; Kuhl, E. (PI) ; Lee, J. (PI) ; Levenston, M. (PI) ; Levin, C. (PI) ; Lin, M. (PI) ; Liphardt, J. (PI) ; Longaker, M. (PI) ; Lundberg, E. (PI) ; Moore, T. (PI) ; Nuyujukian, P. (PI) ; Palmer, M. (PI) ; Pauly, K. (PI) ; Plevritis, S. (PI) ; Prakash, M. (PI) ; Qi, S. (PI) ; Quake, S. (PI) ; Rogers, K. (PI) ; Sanger, T. (PI) ; Sapolsky, R. (PI) ; Schnitzer, M. (PI) ; Scott, M. (PI) ; Shenoy, K. (PI) ; Skylar-Scott, M. (PI) ; Smolke, C. (PI) ; Spielman, D. (PI) ; Steinmetz, L. (PI) ; Swartz, J. (PI) ; Tang, S. (PI) ; Taylor, C. (PI) ; Thiam, H. (PI) ; Venook, R. (PI) ; Wakatsuki, S. (PI) ; Wall, J. (PI) ; Wang, B. (PI) ; Wang, P. (PI) ; Woo, J. (PI) ; Wu, J. (PI) ; Yang, F. (PI) ; Yock, P. (PI) ; Zeitzer, J. (PI) ; Zenios, S. (PI)

BIOE 191X: Out-of-Department Advanced Research Laboratory in Bioengineering

Individual research by arrangement with out-of-department instructors. Credit for 191X is restricted to declared Bioengineering majors pursuing honors and requires department approval. See http://bioengineering.stanford.edu/education/undergraduate.html for additional information. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-15 | Repeatable 15 times (up to 60 units total)

BIOE 206: Mixed-Reality in Medicine (BMP 206, RAD 206)

Mixed reality uses transparent displays to place virtual objects in the user's field of vision such that they can be aligned to and interact with actual objects. This has tremendous potential for medical applications. The course aims to teach the basics of mixed-reality device technology, and to directly connect engineering students to physicians for real-world applications. Student teams will complete guided assignments on developing new mixed-reality technology and a final project applying mixed-reality to solve real medical challenges. Prerequisites: (1) Programming competency in a language such as C, C++. or Python. (2) A basic signal processing course such as EE102B (Digital Signal Processing), while not required, will be helpful. (3) A medical imaging course, while not required, will be helpful. Please contact the instructors with any questions about prerequisites.
Terms: Aut | Units: 3
Instructors: Daniel, B. (PI) ; Hargreaves, B. (PI) ; Leuze, C. (PI) ; Niu, W. (TA)

BIOE 209: Mathematical Modeling of Biological Systems (CME 209)

The course covers mathematical and computational techniques needed to solve advanced problems encountered in applied bioengineering. Fundamental concepts are presented in the context of their application to biological and physiological problems including cancer, cardiovascular disease, infectious disease, and systems biology. Topics include Taylor's Series expansions, parameter estimation, regression, nonlinear equations, linear systems, optimization, numerical differentiation and integration, stochastic methods, ordinary differential equations and Fourier series. Python, Matlab and other software will be used for weekly assignments and projects.Prerequisites: Math 51, 52, 53; prior programming experience (Matlab or other language at level of CS 106a or higher)
Terms: Aut | Units: 3
Instructors: Marsden, A. (PI) ; Papamanolis, L. (TA)

BIOE 210: Systems Biology (BIOE 101)

Complex biological behaviors through the integration of computational modeling and molecular biology. Topics: reconstructing biological networks from high-throughput data and knowledge bases. Network properties. Computational modeling of network behaviors at the small and large scale. Using model predictions to guide an experimental program. Robustness, noise, and cellular variation. Prerequisites: CME 102; BIO 82, BIO 84; or consent of instructor.
Terms: Aut | Units: 3

BIOE 213: Stochastic and Nonlinear Dynamics (APPPHYS 223, BIO 223, PHYSICS 223)

Theoretical analysis of dynamical processes: dynamical systems, stochastic processes, and spatiotemporal dynamics. Motivations and applications from biology and physics. Emphasis is on methods including qualitative approaches, asymptotics, and multiple scale analysis. Prerequisites: ordinary and partial differential equations, complex analysis, and probability or statistical physics.
Terms: Aut | Units: 3
Instructors: Fisher, D. (PI)

BIOE 214: Representations and Algorithms for Computational Molecular Biology (BIOMEDIN 214, CS 274, GENE 214)

BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology ( BIOE 214, CS 274, GENE 214)Topics: This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, BLAST, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D structure, knowledge controlled terminologies for molecular function, expression analysis, chemoinformatics, pharmacogenetics, network biology. Lectures are supplemented with assignments and programming projects, which allow students to implement important computational biology algorithms.Firm prerequisite: CS 106B.
Terms: Aut | Units: 3-4
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