printer friendly pageBIOE 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
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
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
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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
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
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
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
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