Courses

This program supports trainees from seven graduate degree programs and augments their PhD programs with training in Data Driven Biology.The DDB curriculum will feature three experiential courses. The goal of these courses will be to equip students with the fundamental skillsets necessary to process, analyze, and treat large datasets characteristic of modern quantitative bioscience.

Over the course of two years, trainees will be expected to take BIOE 211 (Quantitative Experiments), BIOE 212 (Great Experiments), and a course in Machine Learning/Spatial Analysis (i.e. BIOE 250AC or ECE 594N), in addition to BIOE 101 (Responsible Conduct of Research training). Trainees are also expected to enroll in the one-unit seminars, BIOE 225 and BIOE 230, during their traineeship and to enroll in BIOE 299: Independent Study for each quarter they engage in a research exploration or rotation.

BIOE 211: Quantitative Experiments 

This course is built around experimental design, data analysis, and quantitative modeling of biological processes and phenomena. Topics including experimental design considerations and a priori assumptions, probability, dimensional reduction, hypothesis testing, statistical analysis, and quantitative modeling through ordinary and partial differential equations. Case studies of recent and classic research papers in Bioengineering are used to illustrate key course topics through class discussions. 

BIOE 212: Great Experiments 

   Introduces students to seminal experiments that introduced pioneering biological engineering methods and experimental analysis. Students learn the principles of sound experimental design to test a hypothesis, become familiar with techniques using bacterial and stem cell model systems, as well as imaging and analysis methods. 

BIOE 250AC: Machine Learning / Artificial Intelligence for Cell Biology and Quantitative Image Analysis

This course will engage students in developing and testing algorithms for automated definition and extraction of quantitative parameters linking multi-modal data sets. For example, mining image and video data with multi-Omics data matched at discrete timepoints to correlate functional outputs such as morphological changes, cell contractility, calcium dynamics, and/or biomechanics with gene and protein expression patterns. The data sets used will be those created in the Great Experiments course and during the winter rotations.

BIOE 201: Responsible Conduct of Research 

The responsible conduct of research (RCR) is essential to good science. Examples of goals of RCR education and training are to: Develop, foster, and maintain a culture of integrity in science; discourage and prevent unethical conduct; empower researchers to hold themselves and others accountable to high ethical standards; increase knowledge of, and sensitivity to, ethical issues surrounding the conduct of research by researchers with diverse backgrounds; improve the ability to make responsible choices when faced with ethical dilemmas involving research; provide an appreciation for the range of accepted scientific practices for conducting research; inform scientists and research trainees about the regulations, policies, and statutes.

Formerly BIOE 101