BIO 108: Essential Statistics for Human Biology (HUMBIO 85A)
Introduction to statistical concepts and methods that are essential to the study of questions in biology, environment, health and related areas. The course will teach and use the computer language R and Python (you learn both, choose one). Topics include distributions, probabilities, likelihood, linear models; illustrations will be based on recent research.
Terms: not given this year

Units: 4

UG Reqs: WAYAQR

Grading: Letter (ABCD/NP)
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: Win

Units: 35

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Zhu, X. (PI)
BIOE 42: Physical Biology
BIOE 42 is designed to introduce students to general engineering principles that have emerged from theory and experiments in biology. Topics covered will cover the scales from molecules to cells to organisms, including fundamental principles of entropy, diffusion, and continuum mechanics. These topics will link to several biological questions, including DNA organization, ligand binding, cytoskeletal mechanics, and the electromagnetic origin of nerve impulses. In all cases, students will learn to develop toy models that can explain quantitative measurements of the function of biological systems. Prerequisites:
MATH 19, 20, 21
CHEM 31A, B (or 31X),
PHYSICS 41; strongly recommended:
CS 106A,
CME 100 or
MATH 51, and
CME 106; or instructor approval.
Terms: Spr

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
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 highthroughput 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: WAYAQR

Grading: Letter (ABCD/NP)
Instructors:
Covert, M. (PI)
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 clinicalcase 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 82,
BIO 84.
Terms: Spr

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
BIOE 103B: Systems Physiology and Design
*ONLINE Offering of
BIOE 103. This pilot class,
BIOE103B, is an entirely online offering with the same content, learning goals, and prerequisites as
BIOE 103. Students attend class by watching videos and completing assignments remotely. Students may attend recitation and office hours in person, but cannot attend the BIOE103 inperson 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 clinicalcase 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 82,
BIO 84. strongly recommended
PHYSICS 43; or instructor approval.
Terms: Spr

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
BIOE 140: Physical Biology of Macromolecules
Principles of statistical physics, thermodynamics, and kinetics with applications to molecular biology. Topics include entropy, temperature, chemical forces, enzyme kinetics, free energy and its uses, self assembly, cooperative transitions in macromolecules, molecular machines, feedback, and accurate replication. Prerequisites:
MATH 19, 20, 21;
CHEM 31A, B (or 31X); strongly recommended:
PHYSICS 41,
CME 100 or
MATH 51, and
CME 106; or instructor approval.
Terms: Win

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Prakash, M. (PI)
BIOHOPK 14: Biologging and Biotelemetry
Biologging is a rapidly growing discipline that includes diverse fields such as consumer electronics, medicine, and marine biology. The use of animalattached digital tags is a powerful approach to study the movement and ecology of individuals over a wide range of temporal and spatial scales. This course is an introduction to biologging methods and analysis. Using whales as a model system, students will learn how use multisensor tags to study behavioral biomechanics.
Terms: Spr

Units: 3

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Goldbogen, J. (PI)
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. Computerbased data analysis. Prerequisite: Biology core or consent of instructor.
Terms: Win, Spr

Units: 3

UG Reqs: GER: DBNatSci, GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
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: WAYAQR, WAYFR

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
De Leo, G. (PI)
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