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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
Instructors: ; Brophy, J. (PI); Engel, A. (PI); Hernandez-Lopez, R. (PI); Kyriakakis, P. (PI); Abney, N. (TA); Archibald, B. (TA); Field, H. (TA); Fu, M. (TA); Goldaracena Aguirre, I. (TA); Johnson, A. (TA); Mille Fragoso, S. (TA); Mishra, V. (TA)

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
Instructors: ; Covert, M. (PI); Heath, J. (TA); Kuglen, B. (TA); Samavedi, S. (TA); Yang, M. (TA)

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
Instructors: ; Dunn, J. (PI); Fan, R. (PI); Korndorffer, J. (PI); Liphardt, J. (PI); Lundberg, E. (PI); Qi, S. (PI); Venook, R. (PI); Wall, J. (PI); Bradfield, A. (TA); Espinoza Campomanes, L. (TA); Franke, C. (TA); Sanabria, C. (TA); Soh, J. (TA)

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, Sum | 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); Chiu, W. (PI); Cochran, J. (PI); Coleman, T. (PI); Covert, M. (PI); Cremer, J. (PI); Daniel, B. (PI); Deisseroth, K. (PI); Delp, S. (PI); Dunn, J. (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); Pasca, S. (PI); Pauly, K. (PI); Pelc, N. (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); 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); Au, J. (GP)

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, Sum | Units: 1-15 | Repeatable 15 times (up to 60 units total)
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); Vasquez, P. (GP)

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
Instructors: ; Covert, M. (PI); Heath, J. (TA); Kuglen, B. (TA); Samavedi, S. (TA); Yang, M. (TA)

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); Nayar, G. (TA); Xiong, B. (TA)

BIOE 222: Physics and Engineering Principles of Multi-modality Molecular Imaging of Living Subjects (BMP 222, RAD 222)

Physics and Engineering Principles of Multi-modality Molecular Imaging of Living Subjects (RAD 222A). Focuses on instruments, algorithms and other technologies for non-invasive imaging of molecular processes in living subjects. Introduces research and clinical molecular imaging modalities, including PET, SPECT, MRI, Ultrasound, Optics, and Photoacoustics. For each modality, lectures cover the basics of the origin and properties of imaging signal generation, instrumentation physics and engineering of signal detection, signal processing, image reconstruction, image data quantification, applications of machine learning, and applications of molecular imaging in medicine and biology research.
Terms: Aut | Units: 3-4
Instructors: ; Levin, C. (PI)

BIOE 240: Principles of Synthetic Biology

Synthetic biology is the fundamental science and engineering research that advances building with biology. The key idea is to make biology easier to engineer, which enables biology as a general use technology to make what is needed, where and when it is needed, on a sustainable and renewable basis. From just-add-water biotechnology to cellular therapies to distributed diagnostics for human and environmental health to transforming pollution into materials we use every day, synthetic biology holds promise to allow us to rethink how we meet human needs on a planetary scale. In this course, the field of synthetic biology and its natural scientific and engineering basis are introduced and discussed.
Terms: Aut | Units: 3
Instructors: ; Jewett, M. (PI); Cai, S. (TA); Myers, K. (TA)

BIOE 241: Biological Macromolecules (BIOC 241, BIOPHYS 241, SBIO 241)

The physical and chemical basis of macromolecular function. Topics include: forces that stabilize macromolecular structure and their complexes; thermodynamics and statistical mechanics of macromolecular folding, binding, and allostery; diffusional processes; kinetics of enzymatic processes; the relationship of these principles to practical application in experimental design and interpretation. The class emphasizes interactive learning, and is divided among lectures, in-class group problem solving, and discussion of current and classical literature. Enrollment limited to 30. Prerequisites: Background in biochemistry and physical chemistry recommended but material available for those with deficiency in these areas; undergraduates with consent of instructor only.
Terms: Aut | Units: 3-5
Instructors: ; Das, R. (PI); Ferrell, J. (PI); Harbury, P. (PI); Sharp, L. (GP)

BIOE 273: Biodesign for Digital Health (MED 273)

Health care is facing significant cross-industry challenges and opportunities created by a number of factors, including the increasing need for improved access to affordable, high-quality care; growing demand from consumers for greater control of their health and health data; the shift in focus from sick care to prevention and health optimization; aging demographics and the increased burden of chronic conditions; and new emphasis on real-world, measurable health outcomes for individuals and populations. Moreover, the delivery of health information and services is no longer tied to traditional brick and mortar hospitals and clinics: it has increasingly become "mobile," enabled by apps, sensors, wearables. Simultaneously, it has been augmented and often revolutionized by emerging digital and information technologies, as well as by the data that these technologies generate. This multifactorial transformation presents opportunities for innovation across the entire cycle of care, from wellness, to acute and chronic diseases, to care at the end of life. But how does one approach innovation in digital health to address these health care challenges while ensuring the greatest chance of success? At Stanford Biodesign, we believe that innovation is a process that can be learned, practiced, and perfected; and, it starts with an unmet need. In Biodesign for Digital Health, students will learn about digital health and the Biodesign needs-driven innovation process from over 50 industry experts. Over the course of 10weeks, these speakers will join the teaching team in a dynamic classroom environment that includes lectures, panel discussions, and breakout sessions. These experts represent startups, corporations, venture capital firms, accelerators, research labs, healthcare providers, and more. Student teams will take actual digital and mobile health challenges and learn how to apply Biodesign innovation principles to research and evaluate needs, ideate solutions, and objectively assess them against key criteria for satisfying the needs. Teams take a hands-on approach with the support of need coaches and other mentors. On the final day of class, teams present to a panel of digital health experts and compete for project extension funding. Friday section will be used for team projects and for scheduled workshops. Limited enrollment for this course. Students should submit their application online via: https://stanforduniversity.qualtrics.com/jfe/form/SV_dnY6nvUXMYeILkO
Terms: Aut | Units: 3-4
Instructors: ; Aalami, O. (PI); Rosenbluth, K. (PI); de Haaff, M. (SI)

BIOE 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOPHYS 279, CME 279, CS 279)

Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut | Units: 3
Instructors: ; Dror, R. (PI); Bresette, L. (TA); Li, D. (TA); McAvity, J. (TA); Sayana, R. (TA); Suriana, P. (TA); Xu, J. (TA)

BIOE 291: Principles and Practice of Optogenetics for Optical Control of Biological Tissues

Principles and practice of optical control of biological processes (optogenetics), emphasizing bioengineering approaches. Theoretical, historical, and current practice of the field. Requisite molecular-genetic, optoelectronic, behavioral, clinical, and ethical concepts, and mentored analysis and presentation of relevant papers. Final projects of research proposals and a laboratory component in BioX to provide hands-on training. Contact instructor before registering.
Terms: Aut | Units: 3
Instructors: ; Deisseroth, K. (PI); Encarnacion-Rivera, L. (TA); Liu, T. (TA)

BIOE 300B: Quantitative Physiology

An engineering approach to understanding physiological phenomenon. Course introduces weekly topics in biology and human physiology paired with a mathematical approach to modeling and understanding that week's topic. No strict prerequisites. No prior background in biology is required or assumed. Familiarity with linear algebra, statistics, and programming is recommended. Course information at: http://bioe300b.stanford.edu
Terms: Aut | Units: 3
Instructors: ; Nuyujukian, P. (PI); Harold, K. (TA); Knudsen, C. (TA); Marshall, K. (TA)

BIOE 301E: Computational Protein Modeling Laboratory

This course covers hands-on computational methods related to protein structural modeling. Through solving a series of curated problems, students build their own software tools and develop protocols to model and analyze structures. Topics: protein visualization, Rosetta software suite, structural prediction, homology modeling and protein design.
Terms: Aut | Units: 2
Instructors: ; Huang, P. (PI); Lu, T. (TA)

BIOE 305: Dynamics and Feedback Control of Living Systems (ME 305)

In this course, students will explore feedback control mechanisms that living organisms (cells) implement to execute their function. In addition, students will learn the basics of re-engineering feedback control systems in order for cells to execute new decision making behaviors. The focus will be on molecular level feedback control mechanisms for single cells with mention of cooperative feedback control for multicellular coordination as time permits. We will incorporate principles from Systems Biology, Control and Dynamical Systems Theory with Numerical and Stochastic Simulation. Basic biological mechanisms will be reviewed within the course to provide context and conceptual understanding. Ultimately, students with interest in control theoretic applications will learn how to use notions from control theory to accurately reason about cellular behavior.
Terms: Aut | Units: 3
Instructors: ; Mayalu, M. (PI); Huang, Y. (TA)

BIOE 335: Molecular Motors I

Physical mechanisms of mechanochemical coupling in biological molecular motors, using F1 ATPase as the major model system. Applications of biochemistry, structure determination, single molecule tracking and manipulation, protein engineering, and computational techniques to the study of molecular motors.
Terms: Aut | Units: 3

BIOE 370: Medical Scholars Research

Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum | Units: 4-18 | Repeatable 6 times (up to 108 units total)
Instructors: ; Wang, P. (PI)

BIOE 376: Startup Garage: Design (SUSTAIN 376)

Startup Garage is an intensive, hands-on, project-based course where students apply human-centric design, lean startup methodology, and the Business Model Canvas to conceive, design, and field-test new business concepts that address real world needs. Teams get out of the building and interact directly with users, industry participants, and advisors to deeply understand one or more unmet customer needs. They proceed to design, prototype, and test their proposed products or services and a business model. Teams working on impact-focused ventures will apply the same methodology to address the needs of their beneficiaries. Students develop entrepreneurial skills as they learn critical, cutting-edge techniques about launching a venture. The course is offered by the Graduate School of Business. PREREQUISITE: Team application required. See details and apply at http://startupgarage.stanford.edu/details (login required).
Terms: Aut | Units: 4
Instructors: ; Glickman, M. (PI); Lisbonne, B. (PI); Nakache, P. (PI); Baker, K. (SI); Chen, E. (SI); Deshpande, S. (SI); Eison, J. (SI); Galen, D. (SI); Gur, S. (SI); Laws, M. (SI); Perkash, M. (SI); Prakash, A. (SI); Scull, J. (SI); Waldorf, G. (SI); Wallace, C. (SI); Alvarez, G. (GP); Zenios, S. (GP)

BIOE 390: Introduction to Bioengineering Research (MED 289)

Preference to medical and bioengineering graduate students with first preference given to Bioengineering Scholarly Concentration medical students. Bioengineering is an interdisciplinary field that leverages the disciplines of biology, medicine, and engineering to understand living systems, and engineer biological systems and improve engineering designs and human and environmental health. Students and faculty make presentations during the course. Students expected to make presentations, complete a short paper, read selected articles, and take quizzes on the material.
Terms: Aut | Units: 1-2 | Repeatable 5 times (up to 10 units total)
Instructors: ; Narayan, S. (PI); Wang, P. (PI); Yang, Y. (PI)

BIOE 391: Directed Study

May be used to prepare for research during a later quarter in 392. Faculty sponsor required. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: ; Airan, R. (PI); Alizadeh, A. (PI); Altman, R. (PI); Appel, E. (PI); Baccus, S. (PI); Bammer, R. (PI); Banik, S. (PI); Bao, Z. (PI); Barron, A. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bhatt, A. (PI); Bintu, L. (PI); Boahen, K. (PI); Boettiger, A. (PI); Bowden, A. (PI); Brongersma, M. (PI); Brophy, J. (PI); Bryant, Z. (PI); Butte, A. (PI); Camarillo, D. (PI); Carter, D. (PI); Chang, H. (PI); Chaudhari, A. (PI); Chiu, W. (PI); Cochran, J. (PI); Coleman, T. (PI); Cong, L. (PI); Covert, M. (PI); Curtis, C. (PI); Dahl, J. (PI); Daniel, B. (PI); Daniels, K. (PI); Davis, M. (PI); Davis, R. (PI); DeSimone, J. (PI); Deisseroth, K. (PI); Delp, S. (PI); Dror, R. (PI); Druckmann, S. (PI); Dunn, A. (PI); Endy, D. (PI); Engreitz, J. (PI); Ennis, D. (PI); Fahrig, R. (PI); Feinstein, J. (PI); Fischbach, M. (PI); Fordyce, P. (PI); Fox, E. (PI); Fuller, G. (PI); Gao, A. (PI); Gao, X. (PI); Garten, M. (PI); Gevaert, O. (PI); Giaccia, A. (PI); Giocomo, L. (PI); Gitler, A. (PI); Goins, L. (PI); Gold, G. (PI); Goodman, S. (PI); Graves, E. (PI); Greenleaf, W. (PI); Gurtner, G. (PI); Hargreaves, B. (PI); Haroush, K. (PI); Heilshorn, S. (PI); Hernandez-Lopez, R. (PI); Hie, B. (PI); Hong, G. (PI); Hosseini, H. (PI); Huang, K. (PI); Huang, P. (PI); Jarosz, D. (PI); Jerby, L. (PI); Jewett, M. (PI); Khatri, P. (PI); Kim, P. (PI); Kingsley, D. (PI); Kogan, F. (PI); Konermann, S. (PI); Kovacs, G. (PI); Krummel, T. (PI); Kuhl, E. (PI); Kuo, C. (PI); Lee, J. (PI); Leskovec, J. (PI); Levenston, M. (PI); Levin, C. (PI); Lin, M. (PI); Linderman, S. (PI); Liphardt, J. (PI); Liu, K. (PI); Loh, K. (PI); Longaker, M. (PI); Lundberg, E. (PI); Luo, L. (PI); Marsden, A. (PI); Mayalu, M. (PI); McNab, J. (PI); Melosh, N. (PI); Menon, V. (PI); Mitra, A. (PI); Montgomery, S. (PI); Moore, T. (PI); Newman, A. (PI); Nishimura, D. (PI); Nolan, G. (PI); Nuyujukian, P. (PI); Okamura, A. (PI); Pauly, K. (PI); Pelc, N. (PI); Plevritis, S. (PI); Pohl, K. (PI); Poldrack, R. (PI); Prakash, M. (PI); Qi, S. (PI); Qiu, X. (PI); Quake, S. (PI); Quirin, S. (PI); Ramayya, A. (PI); Reticker-Flynn, N. (PI); Rogers, K. (PI); Salzman, J. (PI); Sapolsky, R. (PI); Sattely, E. (PI); Schnitzer, M. (PI); Scott, M. (PI); Skotheim, J. (PI); Skylar-Scott, M. (PI); Smolke, C. (PI); Snyder, M. (PI); Soh, H. (PI); Soltesz, I. (PI); Spielman, D. (PI); Steinmetz, L. (PI); Swartz, J. (PI); Tang, S. (PI); Tass, P. (PI); Taylor, C. (PI); Theriot, J. (PI); Thiam, H. (PI); Ting, A. (PI); Vasanawala, S. (PI); Venook, R. (PI); Wall, D. (PI); Wall, J. (PI); Wang, B. (PI); Wang, S. (PI); Woo, J. (PI); Wu, J. (PI); Wyss-Coray, T. (PI); Yang, F. (PI); Yang, Y. (PI); Yeh, E. (PI); Yock, P. (PI); Zaharchuk, G. (PI); Zeineh, M. (PI); Zenios, S. (PI); Zou, J. (PI); Au, J. (GP); Choudhry, S. (GP); Dang, V. (GP); McSwain, R. (GP); Misquez, E. (GP); Ramalho, D. (GP)

BIOE 392: Directed Investigation

For Bioengineering graduate students. Previous work in 391 may be required for background; faculty sponsor required. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
Instructors: ; Airan, R. (PI); Alizadeh, A. (PI); Altman, R. (PI); Andriacchi, T. (PI); Annes, J. (PI); Appel, E. (PI); Baccus, S. (PI); Baker, J. (PI); Bammer, R. (PI); Bao, Z. (PI); Barron, A. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bertozzi, C. (PI); Bhatt, A. (PI); Bintu, L. (PI); Boahen, K. (PI); Bowden, A. (PI); Brophy, J. (PI); Bryant, Z. (PI); Butte, A. (PI); Camarillo, D. (PI); Carter, D. (PI); Chang, H. (PI); Chaudhari, A. (PI); Chaudhuri, O. (PI); Chen, X. (PI); Cheng, C. (PI); Chichilnisky, E. (PI); Chiu, W. (PI); Cochran, J. (PI); Coleman, T. (PI); Contag, C. (PI); Cortez Guerrero, A. (PI); Covert, M. (PI); Criddle, C. (PI); Curtis, C. (PI); Dabiri, J. (PI); Dahl, J. (PI); Daniels, K. (PI); Das, R. (PI); Davis, M. (PI); De Leo, G. (PI); DeSimone, J. (PI); Deisseroth, K. (PI); Delp, S. (PI); Demirci, U. (PI); Dionne, J. (PI); Elias, J. (PI); Endy, D. (PI); Engleman, E. (PI); Engreitz, J. (PI); Ennis, D. (PI); Etkin, A. (PI); Fahrig, R. (PI); Feinstein, J. (PI); Feng, L. (PI); Ferrara, K. (PI); Fire, A. (PI); Fischbach, M. (PI); Fordyce, P. (PI); Fuller, G. (PI); Ganguli, S. (PI); Gao, X. (PI); Garcia, C. (PI); Garten, M. (PI); Giaccia, A. (PI); Glenn, J. (PI); Glover, G. (PI); Gold, G. (PI); Goodman, S. (PI); Graves, E. (PI); Greenleaf, W. (PI); Gurtner, G. (PI); Hargreaves, B. (PI); Heilshorn, S. (PI); Heller, S. (PI); Hernandez-Lopez, R. (PI); Herschlag, D. (PI); Hie, B. (PI); Hosseini, H. (PI); Huang, K. (PI); Huang, P. (PI); Idoyaga, J. (PI); Ingelsson, E. (PI); James, M. (PI); Jarosz, D. (PI); Jewett, M. (PI); Jonikas, M. (PI); Khuri-Yakub, B. (PI); Kim, P. (PI); Kogan, F. (PI); Konermann, S. (PI); Kovacs, G. (PI); Krasnow, M. (PI); Krummel, T. (PI); Kuhl, E. (PI); Kuo, C. (PI); Lee, J. (PI); Leskovec, J. (PI); Levenston, M. (PI); Levin, C. (PI); Lin, M. (PI); Liphardt, J. (PI); Liu, K. (PI); Longaker, M. (PI); Lundberg, E. (PI); Malenka, R. (PI); Marsden, A. (PI); Melosh, N. (PI); Monje-Deisseroth, M. (PI); Montgomery, S. (PI); Moore, T. (PI); Nishimura, D. (PI); Nolan, G. (PI); Nuyujukian, P. (PI); O'Brien, L. (PI); Okamura, A. (PI); Pauly, J. (PI); Pauly, K. (PI); Peay, K. (PI); Pelc, N. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Prakash, M. (PI); Qi, S. (PI); Quake, S. (PI); Rando, T. (PI); Raymond, J. (PI); Red-Horse, K. (PI); Reddy, S. (PI); Reijo Pera, R. (PI); Relman, D. (PI); Rose, J. (PI); Rutt, B. (PI); Saggar, M. (PI); Salerno, M. (PI); Sanger, T. (PI); Santa Maria, P. (PI); Sapolsky, R. (PI); Satpathy, A. (PI); Sattely, E. (PI); Schnitzer, M. (PI); Scott, M. (PI); Skotheim, J. (PI); Skylar-Scott, M. (PI); Smolke, C. (PI); Snyder, M. (PI); Soh, H. (PI); Soltesz, I. (PI); Sonnenburg, J. (PI); Spielman, D. (PI); Straight, A. (PI); Sunwoo, J. (PI); Swartz, J. (PI); Tass, P. (PI); Taylor, C. (PI); Theriot, J. (PI); Thiam, H. (PI); Walbot, V. (PI); Wall, D. (PI); Wang, B. (PI); Wang, P. (PI); Wang, S. (PI); Weissman, I. (PI); Wernig, M. (PI); Woo, J. (PI); Wu, J. (PI); Wu, S. (PI); Wyss-Coray, T. (PI); Xing, L. (PI); Yang, F. (PI); Yang, Y. (PI); Yock, P. (PI); Zeineh, M. (PI); Zenios, S. (PI); Arzate, M. (GP); Au, J. (GP); Choudhry, S. (GP); Dang, V. (GP); Johnson, S. (GP); Jones, D. (GP); McSwain, R. (GP); Misquez, E. (GP); Ramalho, D. (GP)

BIOE 393: Bioengineering Departmental Research Colloquium

Bioengineering department labs at Stanford present recent research projects and results. Guest lecturers. Topics include applications of engineering to biology, medicine, biotechnology, and medical technology, including biodesign and devices, molecular and cellular engineering, regenerative medicine and tissue engineering, biomedical imaging, and biomedical computation.
Terms: Aut | Units: 1 | Repeatable for credit
Instructors: ; Camarillo, D. (PI); Au, J. (GP)

BIOE 500: Thesis (Ph.D.)

(Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit
Instructors: ; Alizadeh, A. (PI); Altman, R. (PI); Andriacchi, T. (PI); Appel, E. (PI); Baker, J. (PI); Bammer, R. (PI); Bao, Z. (PI); Barron, A. (PI); Batzoglou, S. (PI); Bertozzi, C. (PI); Bintu, L. (PI); Boahen, K. (PI); Bryant, Z. (PI); Butte, A. (PI); Camarillo, D. (PI); Carter, D. (PI); Chang, H. (PI); Chaudhuri, O. (PI); Cheng, C. (PI); Chichilnisky, E. (PI); Cochran, J. (PI); Contag, C. (PI); Covert, M. (PI); Dabiri, J. (PI); Dahl, J. (PI); Deisseroth, K. (PI); Delp, S. (PI); Demirci, U. (PI); Elias, J. (PI); Endy, D. (PI); Engleman, E. (PI); Etkin, A. (PI); Fahrig, R. (PI); Feinstein, J. (PI); Feng, L. (PI); Fire, A. (PI); Fischbach, M. (PI); Fordyce, P. (PI); Ganguli, S. (PI); Garcia, C. (PI); Glenn, J. (PI); Glover, G. (PI); Gold, G. (PI); Goodman, S. (PI); Graves, E. (PI); Greenleaf, W. (PI); Hargreaves, B. (PI); Heilshorn, S. (PI); Huang, K. (PI); Huang, P. (PI); Khuri-Yakub, B. (PI); Kim, P. (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); Montgomery, S. (PI); Moore, T. (PI); Nishimura, D. (PI); Nuyujukian, P. (PI); Okamura, A. (PI); Pauly, J. (PI); Pauly, K. (PI); Pelc, N. (PI); Plevritis, S. (PI); Prakash, M. (PI); Qi, S. (PI); Quake, S. (PI); Rando, T. (PI); Raymond, J. (PI); Reijo Pera, R. (PI); Relman, D. (PI); Rose, J. (PI); Sanger, T. (PI); Sapolsky, R. (PI); Sattely, E. (PI); Schnitzer, M. (PI); Scott, M. (PI); Smolke, C. (PI); Soh, H. (PI); Spielman, D. (PI); Swartz, J. (PI); Taylor, C. (PI); Theriot, J. (PI); Wang, B. (PI); Wang, P. (PI); Weissman, I. (PI); Wernig, M. (PI); Woo, J. (PI); Wu, J. (PI); Xing, L. (PI); Yang, F. (PI); Yock, P. (PI); Zenios, S. (PI); Au, J. (GP); Dang, V. (GP); Jones, D. (GP)

BIOE 802: TGR Dissertation

(Staff)
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
Instructors: ; Airan, R. (PI); Alizadeh, A. (PI); Altman, R. (PI); Andriacchi, T. (PI); Appel, E. (PI); Baccus, S. (PI); Baker, J. (PI); Bammer, R. (PI); Bao, Z. (PI); Barron, A. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bertozzi, C. (PI); Bhatt, A. (PI); Bintu, L. (PI); Boahen, K. (PI); Bowden, A. (PI); Brophy, J. (PI); Bryant, Z. (PI); Butte, A. (PI); Camarillo, D. (PI); Carter, D. (PI); Chang, H. (PI); Chaudhuri, O. (PI); Cheng, C. (PI); Chichilnisky, E. (PI); Chiu, W. (PI); Cochran, J. (PI); Coleman, T. (PI); Contag, C. (PI); Covert, M. (PI); Curtis, C. (PI); Cutkosky, M. (PI); Dabiri, J. (PI); Dahl, J. (PI); DeSimone, J. (PI); Deisseroth, K. (PI); Delp, S. (PI); Demirci, U. (PI); Dionne, J. (PI); Elias, J. (PI); Endy, D. (PI); Engleman, E. (PI); Ennis, D. (PI); Etkin, A. (PI); Fahrig, R. (PI); Feinstein, J. (PI); Feng, L. (PI); Ferrara, K. (PI); Fire, A. (PI); Fischbach, M. (PI); Fordyce, P. (PI); Ganguli, S. (PI); Gao, X. (PI); Garcia, C. (PI); Garten, M. (PI); Giaccia, A. (PI); Glenn, J. (PI); Glover, G. (PI); Gold, G. (PI); Goodman, S. (PI); Graves, E. (PI); Greenleaf, W. (PI); Gurtner, G. (PI); Hargreaves, B. (PI); Heilshorn, S. (PI); Huang, K. (PI); Huang, P. (PI); Ingelsson, E. (PI); James, M. (PI); Jarosz, D. (PI); Jewett, M. (PI); Khuri-Yakub, B. (PI); Kim, P. (PI); Kogan, F. (PI); Konermann, S. (PI); Kovacs, G. (PI); Krummel, T. (PI); Kuhl, E. (PI); Lee, J. (PI); Leskovec, J. (PI); Levenston, M. (PI); Levin, C. (PI); Lin, M. (PI); Liphardt, J. (PI); Longaker, M. (PI); Lundberg, E. (PI); Mackall, C. (PI); Marsden, A. (PI); McNab, J. (PI); Montgomery, S. (PI); Moore, T. (PI); Nishimura, D. (PI); Nolan, G. (PI); Nuyujukian, P. (PI); Okamura, A. (PI); Pauly, J. (PI); Pauly, K. (PI); Pelc, N. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Prakash, M. (PI); Qi, S. (PI); Quake, S. (PI); Rando, T. (PI); Raymond, J. (PI); Reijo Pera, R. (PI); Relman, D. (PI); Rose, J. (PI); Sanger, T. (PI); Sapolsky, R. (PI); Sattely, E. (PI); Schnitzer, M. (PI); Scott, M. (PI); Skylar-Scott, M. (PI); Smolke, C. (PI); Soh, H. (PI); Soltesz, I. (PI); Sonnenburg, J. (PI); Spielman, D. (PI); Sunwoo, J. (PI); Swartz, J. (PI); Taylor, C. (PI); Theriot, J. (PI); Thiam, H. (PI); Vasanawala, S. (PI); Walbot, V. (PI); Wall, D. (PI); Wang, B. (PI); Wang, P. (PI); Wang, S. (PI); Weissman, I. (PI); Wernig, M. (PI); Woo, J. (PI); Wu, J. (PI); Wyss-Coray, T. (PI); Xing, L. (PI); Yang, F. (PI); Yock, P. (PI); Zarins, C. (PI); Zeineh, M. (PI); Zenios, S. (PI); Au, J. (GP); Choudhry, S. (GP); Cortez Guerrero, A. (TA); Dang, V. (GP); Jones, D. (GP); McSwain, R. (GP); Ramalho, D. (GP)
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