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This course is divided into two 3-week cycles. During the first cycle, students will be developing a 2-page original research proposal, which may be used for NSF or other fellowship applications. In the second cycle, students will work in small teams and will be mentored by faculty to develop an original research project for oral presentation. Skills emphasized include: 1) reading for breadth and depth; 2) developing compelling, creative arguments; 3) communicating with the spoken and written word; 4) working in teams. Important features of the course include peer assessment, interactive joint classes, and substantial face-to-face discussion with faculty drawn from across the Biosciences programs. Shortened autumn quarter class; class meets during weeks 1 through 8 of the quarter.

Basics of ordinary differential equation modeling of signal transduction motifs, small circuits of regulatory proteins and genes that serve as building blocks of complex regulatory circuits. Morning session covers numerical modeling experiments. Afternoon session explores theory underpinning that day's modeling session. Modeling done using Mathematica, Standard Edition provided to enrolled students.

Introduction to foundations of rigorous, reproducible research in experimental biology and clinical research. Provides conceptual framework for linking hypotheses to experimental design, quantitative measurement, statistical analysis and assessment of uncertainty. Course combines lecture presentation and discussion of core concepts from statistics and reproducibility with hands-on exposure to best practices for reproducible workflows spanning design, data collection, annotation, analysis and presentation of results. Brief discussion of social, legal, and ethical issues with reproducibility in scientific practice, along with NIH grant requirements. Course provides foundations for future learning in these areas. Examples drawn from multiple areas of experimental biology and clinical research. Target audience: Students in BIOS 200 (Foundations in Experimental Biology), in Biosciences graduate programs or T32 training programs. Prerequisites: None

A variety of stem cells harbor different properties appropriate for various types of research. We will cover the molecular characteristics of totipotent, pluripotent, multipotent, and unipotent stem cells. This knowledge will form the foundation for us to explore the use of stem cells in developmental biology and translation research. As an application, we will focus on genome editing technologies and in vitro models of cardiovascular disease.

"Open Source Prototyping" is a hands-on course that equips students with the skills and knowledge to use open-source design tools and rapid prototyping technologies, such as 3D printing and CNC. Students will learn how to translate their ideas into real-world objects, understanding the full process from ideation to realization. Key topics include navigation of leading design software, 3D printing technologies, and in-depth understanding of materials science. The course emphasizes open-source principles, and their applications in additive manufacturing. It features a wide range of applications, including medical devices, lab equipment, and experimental apparatuses, providing a comprehensive look at prototyping potential.

RNA sequencing (RNAseq) is a powerful and increasingly popular tool that is used to investigate a variety of biological questions across the tree of life. However, while commercially available solutions have made the bench work associated with RNAseq easier than ever, the planning and analysis of an RNAseq experiment require considerable bioinformatics knowledge. In this course, students will learn how to design and analyze both single cell and bulk RNAseq experiments with topics including: quality control, mapping, read counting, identification of differentially expressed genes, gene set enrichment analysis, clustering and annotation, pseudotime, and advanced topics. Computer lab workshops will be incorporated to supplement lecture material and allow students to work on their own data.

An overview of principles and fundamentals for writing competitive fellowships (e.g. NIH F31, F32) and career development awards (e.g. NIH K Awards). Topics include: developing specific aims and career development plans; using the review criteria to inform writing; timelines and resources. Participants develop proposals through guided exercises with an emphasis on in-class peer review and focused faculty feedback.

This course introduces different technologies for harvesting and storing solar energy, the most abundant source of renewable energy on earth. This course will cover ways to generate electricity (solar cells) and molecular fuels (biofuels and solar fuels) and the key solutions for short and long-term solar energy storage, including batteries, supercapacitors, electrolyzers, and fuel cells. For each topic, we will cover the existing and emerging technologies, how they work, what role they fill in the energy transition, and how they need to be improved. Students will give a final presentation where they cover a technology or proposed technology of their choosing.

This mini-course is designed to equip graduate students and postdocs with essential skills for ensuring reproducibility in computational research. Through practical exercises and interactive sessions, participants will learn best practices, tools, and techniques for doing open and reproducible research. Topics covered include version control, containerization, data management, workflows, and documentation strategies. This course empowers students to overcome challenges associated with reproducibility, fostering rigorous scientific inquiry, and enhancing the credibility and impact of their computational work, while also exploring the primary causes and consequences of irreproducibility in research. Participants will gain valuable insights and practical experience in achieving computational reproducibility across various domains, including biology. Prerequisites: Basic familiarity with programming (e.g., Python, R); Basic knowledge of Unix/Linux Bash