## 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 for credit

Instructors:
Annes, J. (PI)
;
Appel, E. (PI)
;
Barres, B. (PI)
;
Blau, H. (PI)
;
Buckwalter, M. (PI)
;
Diehn, M. (PI)
;
Elias, J. (PI)
;
Han, M. (PI)
;
Hanawalt, P. (PI)
;
Heilshorn, S. (PI)
;
Kuo, C. (PI)
;
Mochly-Rosen, D. (PI)
;
Nusse, R. (PI)
;
Okamura, A. (PI)
;
Relman, D. (PI)
;
Shrager, J. (PI)
;
Weissman, I. (PI)
;
Wu, J. (PI)
;
Wu, S. (PI)
;
Yang, Y. (PI)
;
Yang, Y. (PI)

## 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 or
EE 122B.

Terms: Spr
| Units: 2

Instructors:
Lee, J. (PI)
;
Mayer, A. (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 41,
BIO 42; or consent of instructor.

Terms: Aut
| Units: 3

## BIOE 211: Biophysics of Multi-cellular Systems and Amorphous Computing (BIOE 311, BIOPHYS 311, DBIO 211)

Provides an interdisciplinary perspective on the design, emergent behavior, and functionality of multi-cellular biological systems such as embryos, biofilms, and artificial tissues and their conceptual relationship to amorphous computers. Students discuss relevant literature and introduced to and apply pertinent mathematical and biophysical modeling approaches to various aspect multi-cellular systems, furthermore carry out real biology experiments over the web. Specific topics include: (Morphogen) gradients; reaction-diffusion systems (Turing patterns); visco-elastic aspects and forces in tissues; morphogenesis; coordinated gene expression, genetic oscillators and synchrony; genetic networks; self-organization, noise, robustness, and evolvability; game theory; emergent behavior; criticality; symmetries; scaling; fractals; agent based modeling. The course is geared towards a broadly interested graduate and advanced undergraduates audience such as from bio / applied physics, computer science, developmental and systems biology, and bio / tissue / mechanical / electrical engineering. Prerequisites: Previous knowledge in one programming language - ideally Matlab - is recommended; undergraduate students benefit from
BIOE 42, or equivalent.

Terms: Spr
| Units: 2-3

Instructors:
Riedel-Kruse, I. (PI)
;
Kim, H. (TA)

## BIOE 212: Introduction to Biomedical Informatics Research Methodology (BIOMEDIN 212, CS 272, GENE 212)

Capstone Biomedical Informatics (BMI) 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 211 or 214 or 217. Preference to BMI graduate students. Consent of instructor required.

Terms: Spr
| Units: 3

Instructors:
Altman, R. (PI)
;
Boyce, H. (TA)

## BIOE 213: Stochastic and Nonlinear Dynamics (APPPHYS 223, BIO 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: Spr
| Units: 3

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

Topics: 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, protein threading techniques, homology modeling, 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, machine learning (clustering and classification), and natural language text processing. Prerequisite:
CS 106B; recommended:
CS161; consent of instructor for 3 units.

Terms: Aut
| Units: 3-4

## BIOE 215: Physics-Based Simulation of Biological Structure

Modeling, simulation, analysis, and measurement of biological systems. Computational tools for determining the behavior of biological structures- from molecules to organisms. Numerical solutions of algebraic and differential equations governing biological processes. Simulation laboratory examples in biology, engineering, and computer science. Limited enrollment. Prerequisites: basic biology, mechanics (F=ma), ODEs, and proficiency in C or C++ programming.

Last offered: Spring 2007

## BIOE 217: Translational Bioinformatics (BIOMEDIN 217, CS 275)

Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of
CS 106A and familiarity with biology and statistics.

Terms: Win
| Units: 4

Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
...
more instructors for BIOE 217 »

Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
;
Gloudemans, M. (TA)
;
McInnes, G. (TA)
;
Shenoy, A. (TA)

Filter Results: