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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.

The emerging therapeutic landscape has a new cast of characters' engineered cells, programmable nucleic acids, and multi-valent antibodies' to name a few. This lecture-based course will provide an overview of these new therapeutic modalities, the basic science guiding their development, and a discussion of new regulatory and safety challenges that emerge in these modalities. As a final project, students will produce a report spanning the preclinical and clinical development of a new therapy. Examples include CRISPR-edited cell therapies, bispecific T cell engagers, in vivo CRISPR base editors, and antisense oligo therapies.

Discover the power of the breath to reach a meditative state of mind. Combine meditation with activities that inspire connection and purpose through community building and mindful leadership. Learn through breathwork, meditation, lecture, class discussion, experiential learning, and yoga. The cornerstone of the course is evidence-based SKY Meditation technique that uses the breath to quiet the mind, supporting a deep experience of meditation and a practical approach to happiness.

The course will focus on computational approaches to ensure that all data, code, and analyses can be captured in a reproducible workflow, to be confirmed and replicated by you in the future, by other members of your team, and by reviewers and other researchers. We will cover how to satisfy FAIR principles, version control, how to create a git repository, utilize Github and how to create a reproducible dataset. Prerequisites: Basic knowledge of R. Recommended (not required): EPI 202 or 261/262, STATS 60, or MS&E 125.

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

In evolutionary theory, the standard 'fitness' considers the operation of selection over a single generation. Some researchers use a poorly-quantified term, 'evolvability', to describe the latent ability of organisms to evolve over multiple generations. Does evolvability itself evolve? Can we tease apart the concepts of short-term fitness and long-term evolvability? Can we quantitatively define a 'long-term fitness' that is as general and practical as the standard fitness? What entities (individuals? genotypes? species?) can be said to possess evolvability? This seminar will debate these questions as we study papers ranging from theoretical biology, to concepts in "evo-devo", to recent experimental work in microbiology and in silico models.

This course will provide students with an overview of current technologies related to the development of small molecules and neutralizing antibodies. Delivered via classroom instruction and a workshop, these modules will aim to increase the fundamental understanding of drug development terminology and processes, combined with real-world examples of discovery therapeutics and technologies developed for translation to clinic. Emphasis will be made on the development of small molecules that can cross the blood-brain barrier and show potential for translation in clinic. Focus will also be put on the screening of antibody targets followed by target validation, target profile development and preclinical evaluation. The workshop will focus on computational molecular docking which is an excellent tool that will help reduce the attrition rate during the drug development process and lead identification studies. Active engagement of the students during the workshop is expected based on the need to install specific software and completion of assigned tasks (computational docking). Students are expected to bring personal laptops to class on day 1 and 2 to perform docking exercises.

Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate methods (PCA and extensions), sparse representations (trees, networks, contingency tables) as well as nonparametric testing (Bootstrap, permutation and Monte Carlo methods). Hands on, use R and cover many Bioconductor packages. Prerequisite: Working knowledge of R and two core Biology courses. Note that the 155 offering is a writing intensive course for undergraduates only and requires instructor consent. (WIM). See https://web.stanford.edu/class/bios221/index.html