printer friendly pageBIOE 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, Sum
| Units: 1-15
| Repeatable
15 times
(up to 60 units total)
Instructors:
Abu-Remaileh, M. (PI)
;
Annes, J. (PI)
;
Ashley, E. (PI)
...
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Instructors:
Abu-Remaileh, M. (PI)
;
Annes, J. (PI)
;
Ashley, E. (PI)
;
Attardi, L. (PI)
;
Baumer, F. (PI)
;
Blau, H. (PI)
;
Blish, C. (PI)
;
Buckwalter, M. (PI)
;
Chen, J. (PI)
;
Czechowicz, A. (PI)
;
Daneshjou, R. (PI)
;
De Leo, G. (PI)
;
Diehn, M. (PI)
;
Druckmann, S. (PI)
;
Elias, J. (PI)
;
Eshel, N. (PI)
;
Feldman, B. (PI)
;
Fire, A. (PI)
;
Gomez-Ospina, N. (PI)
;
Gruber, T. (PI)
;
Han, M. (PI)
;
Han, S. (PI)
;
Hanawalt, P. (PI)
;
Heilshorn, S. (PI)
;
Hong, G. (PI)
;
Keller, C. (PI)
;
Khosla, C. (PI)
;
Klein, T. (PI)
;
Kornberg, R. (PI)
;
Kuo, C. (PI)
;
Linderman, S. (PI)
;
Luo, L. (PI)
;
Mochly-Rosen, D. (PI)
;
Monje-Deisseroth, M. (PI)
;
Nusse, R. (PI)
;
Okamura, A. (PI)
;
Oro, A. (PI)
;
Owen, S. (PI)
;
Pasca, S. (PI)
;
Pitteri, S. (PI)
;
Qi, S. (PI)
;
Relman, D. (PI)
;
Satpathy, A. (PI)
;
Schuele, B. (PI)
;
Shrager, J. (PI)
;
Snyder, M. (PI)
;
Stoyanova, T. (PI)
;
Sudhof, T. (PI)
;
Tang, S. (PI)
;
Wakatsuki, S. (PI)
;
Weissman, I. (PI)
;
Williams, L. (PI)
;
Wu, J. (PI)
;
Wu, S. (PI)
;
Wyss-Coray, T. (PI)
;
Yang, Y. (PI)
;
Yang, Y. (PI)
BIOE 199A: Inventing Synthetic Biosystems
Biology as a technology is burgeoning, leading to diverse cultural, economic, geopolitical, and natural outcomes. Students in this course will learn to step back from the overwhelming immediacy of biotechnology's application and to instead adopt a culture of play that enables qualitative expansion of ideas and possibilities. So enriched students will also learn to map ideas onto a future constrained by practical realities and market dynamics. Active in-class participation and a team-based final project are required.
Terms: Spr
| Units: 1-2
| Repeatable
2 times
(up to 4 units total)
BIOE 201C: Diagnostic Devices Lab (BIOE 301C)
This course exposes students to the engineering principles and clinical application of medical devices through lectures and hands-on labs, performed in teams of two. Teams take measurements with these devices and fit their data to theory presented in the lecture. Devices covered include X-ray, CT, MRI, EEG, ECG, Ultrasound and BMI (Brain-machine interface). Prerequisites:
BIOE 103 or
BIOE 300B.
Terms: Spr
| Units: 2-5
BIOE 204: Genetic and Epigenetic Engineering
This course will cover the fundamental principles of genetic and epigenetic engineering, starting from the key biological discoveries to the current technological applications. We will be dissecting classic literature, formulating our own scientific questions, and designing experiments or calculations to test our answers. Topics include: gene editing using transposases, integrases and nucleases, gene regulation with a focus on transcriptional control, chromatin-mediated epigenetic regulation, and epigenetic editing.
Terms: Spr
| Units: 2
Instructors:
Bintu, L. (PI)
;
Rios-Martinez, C. (TA)
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
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 212: Introduction to Biomedical Data Science Research Methodology (BIOMEDIN 212, CS 272, GENE 212)
Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites:
BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Spr
| Units: 3-5
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. NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Aut
| Units: 3-4
Instructors:
Altman, R. (PI)
;
Koodli, R. (TA)
;
McCann, H. (TA)
...
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