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1 - 10 of 22 results for: BIOE

BIOE 44: Fundamentals for Engineering Biology Lab

An introduction to next-generation techniques in genetic, molecular, biochemical, cellular and tissue engineering. Lectures cover advances in the field of synthetic biology with emphasis on genetic engineering, 3D bioprinting, plasmid design, gene synthesis, genetic circuits, safety and bio ethics. Lab modules will teach students how to: conduct basic lab techniques, add/remove DNA from living matter, engineer prokaryotic and eukaryotic cells, build a 3D bioprinter, and print cells. Group projects will build upon current research including: 3D bioprinting as an emerging field of synthetic biology, gene and genome engineering via decoupled design, 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 based on useful applications of biological technologies. Concurrent or previous enrollment in BIO 82 or BIO 83
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

Lecture/Lab. First course of two-quarter capstone sequence. Team based project introduces students to the process of designing new biological technologies to address societal needs. Topics include methods for validating societal needs, brainstorming, concept selection, and the engineering design process. First quarter deliverable is a design for the top concept. Second quarter involves implementation and testing. Guest lectures and practical demonstrations are incorporated. Prerequisites: BIOE 123 and BIOE 44. This course is open only to seniors in the undergraduate Bioengineering program.
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, Sum | Units: 1-5 | Repeatable for credit
Instructors: Abu-Remaileh, M. (PI) ; Altman, R. (PI) ; Andriacchi, T. (PI) ; Appel, E. (PI) ; Bammer, R. (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) ; 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) ; Gambhir, S. (PI) ; Garten, M. (PI) ; Gold, G. (PI) ; Goodman, S. (PI) ; Graves, E. (PI) ; Gurtner, G. (PI) ; Hargreaves, B. (PI) ; Heilshorn, S. (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) ; 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) ; Shenoy, K. (PI) ; Skylar-Scott, M. (PI) ; Smolke, C. (PI) ; Spielman, D. (PI) ; Swartz, J. (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

Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable 15 times (up to 60 units total)

BIOE 210: Systems Biology (BIOE 101)

Terms: Aut | Units: 3

BIOE 214: Representations and Algorithms for Computational Molecular Biology (BIOMEDIN 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, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, chemoinformatics, pharmacogenetics, network biology. Note: For Fall 2021, Dr. Altman will be away on sabbatical and so class will be taught from lecture videos recorded in fall of 2018. The class will be entirely online, with no scheduled meeting times. Lectures will be released in batches to encourage pacing. A team of TAs will manage all class logistics and grading. Firm prerequisite: CS 106B.
Terms: Aut | Units: 3-4

BIOE 221G: Gut Microbiota in Health and Disease (GENE 208, MI 221)

Preference to graduate students. Focus is on the human gut microbiota. Students will receive instruction on computational approaches to analyze microbiome data and must complete a related project.
Terms: Aut | Units: 3

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

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 welln more »
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_3O90E6zVaW3rRtQ
Terms: Aut, Sum | Units: 3
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