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# 11 - 20 of 132 results for: all courses

## BIO 141:Biostatistics (STATS 141)

Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-AQR

## BIO 183:Theoretical Population Genetics (BIO 283)

Models in population genetics and evolution. Selection, random drift, gene linkage, migration, and inbreeding, and their influence on the evolution of gene frequencies and chromosome structure. Models are related to DNA sequence evolution. Prerequisites: calculus and linear algebra, or consent of instructor.
Last offered: Winter 2015 | UG Reqs: WAY-AQR

## BIOE 42:Physical Biology of Cells

Principles of transport, continuum mechanics, and fluids, with applications to cell biology. Topics include random walks, diffusion, Langevin dynamics, transport theory, low Reynolds number flow, and beam theory, with applications including quantitative models of protein trafficking in the cell, mechanics of the cell cytoskeleton, the effects of molecular noise in development, the electromagnetics of nerve impulses, and an introduction to cardiovascular fluid flow. Prerequisites: MATH 41, 42; CHEM 31A, B (or 31X); strongly recommended: CS 106A, PHYSICS 41, CME 100 or MATH 51, and CME 106; or instructor approval. 4 units, Spr (Huang, K)
Terms: Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA
Instructors: Huang, K. (PI)

## 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 41, BIO 42; or consent of instructor.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR

## BIOE 103:Systems Physiology and Design

Physiology of intact human tissues, organs, and organ systems in health and disease, and bioengineering tools used (or needed) to probe and model these physiological systems. Topics: Clinical physiology, network physiology and system design/plasticity, diseases and interventions (major syndromes, simulation, and treatment, instrumentation for intervention, stimulation, diagnosis, and prevention), and new technologies including tissue engineering and optogenetics.  Discussions of pathology of these systems in a clinical-case based format, with a view towards identifying unmet clinical needs.  Learning computational skills that not only enable simulation of these systems but also apply more broadly to biomedical data analysis. Prerequisites: CME 102; PHYSICS 41; BIO 41, 42
Terms: Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

## BIOE 103B:Systems Physiology and Design

*ONLINE Offering of BIOE103. This pilot class, BIOE103B, is an entirely online offering with the same content, learning goals, and prerequisites as BIOE103. Students attend class by watching videos and completing assignments remotely. Students may attend recitation and office hours in person, but cannot attend the BIOE103 in-person lecture due to room capacity restraints.* Physiology of intact human tissues, organs, and organ systems in health and disease, and bioengineering tools used (or needed) to probe and model these physiological systems. Topics: Clinical physiology, network physiology and system design/plasticity, diseases and interventions (major syndromes, simulation, and treatment, instrumentation for intervention, stimulation, diagnosis, and prevention), and new technologies including tissue engineering and optogenetics. Discussions of pathology of these systems in a clinical-case based format, with a view towards identifying unmet clinical needs. Learning computational skills that not only enable simulation of these systems but also apply more broadly to biomedical data analysis. Prerequisites: MATH 41, 42; CME 102; PHY 41; BIO 41, 42; strongly recommended PHY 43; or instructor approval.
Terms: Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

## BIOHOPK 174H:Experimental Design and Probability (BIOHOPK 274H)

(Graduate students register for 274H.) Variability is an integral part of biology. Introduction to probability and its use in designing experiments to address biological problems. Focus is on analysis of variance, when and how to use it, why it works, and how to interpret the results. Design of complex, but practical, asymmetrical experiments and environmental impact studies, and regression and analysis of covariance. Computer-based data analysis. Prerequisite: Biology core or consent of instructor.
Terms: Win, Spr | Units: 3 | UG Reqs: GER: DB-NatSci, GER:DB-Math, WAY-AQR, WAY-FR
Instructors: Watanabe, J. (PI)

## BIOHOPK 177H:Dynamics and Management of Marine Populations (BIOHOPK 277H)

(Graduate students register for 277H.) Course examines the ecological factors and processes that control natural and harvested marine populations. Course emphasizes mathematical models as tools to assess the dynamics of populations and to derive projections of their demographic fate under different management scenarios. Course objectives will be met by a combination of theoretical lectures, assigned readings and class discussions, case study analysis and interactive computer sessions.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR, WAY-FR | Repeatable for credit
Instructors: De Leo, G. (PI)

## CEE 29N:Managing Natural Disaster Risk

Natural disasters arise from the interaction of natural processes, such as earthquakes or floods, with human development that suffers safety-related and economic losses. We cannot predict exactly when those disasters will occur, or prevent them entirely, but we have a number of engineering and policy options that can reduce the impacts of such events.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR
Instructors: Baker, J. (PI)
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