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1 - 10 of 32 results for: BIOS ; Currently searching autumn courses. You can expand your search to include all quarters

BIOS 200: Foundations in Experimental Biology

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.
Terms: Aut | Units: 5

BIOS 204: Modeling Cell Signaling

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.
Terms: Aut | Units: 3
Instructors: Ferrell, J. (PI)

BIOS 215: Stanford SKY Campus Happiness Retreat

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.
Terms: Aut, Spr | Units: 1 | Repeatable 3 times (up to 3 units total)

BIOS 217: Foundations of statistics and reproducible research

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
Terms: Aut | Units: 2
Instructors: Goodman, S. (PI)

BIOS 219: Early Development Strategies for Neutralizing Antibodies and Brain Permeable Small Molecules

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.
Terms: Aut, Win | Units: 1

BIOS 221: Modern Statistics for Modern Biology (STATS 256, STATS 366)

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
Terms: Aut | Units: 3

BIOS 223: Development and reporting of robust and reproducible LC-MS/MS assays

This mini-course offers a series of lectures and hands-on labs to discuss the development and reporting of reproducible quantitative LC-MS/MS molecular assays using a triple quadrupole. We will discuss chromatography and mobile phase selection, mass spectrometry parameters and selection of fragment ions, and the reporting and interpretation of published methods. Additionally we will cover the use of internal standards and sample preparation, and normalization methods for reproducible data analysis. Students will have the opportunity to work with a mass spectrometer and will design a cohesive plan for a targeted assay of an example molecule in their research.
Terms: Aut, Win | Units: 1

BIOS 229: Open Source Prototyping: Translating Ideas to Reality using Rapid Prototyping Methods

"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.
Terms: Aut | Units: 1
Instructors: Wang, B. (PI)

BIOS 230: Successful Fieldwork in Global Health Research

This two-day workshop focuses on how to successfully implement fieldwork in global health research. After this class, students will have a detailed plan for their fieldwork, including entering and exiting the field, ethical concerns, impact, safety, equitable partnerships, and preparing for the unexpected. The course builds on student-led active learning techniques, with invited guest speakers who share their fieldwork experiences. The course is open to advanced graduate students and postdocs who have developed a research question and design for global health research. Students are recommended but not required to take the course ¿Practical Approaches to Global Health Research¿ ( MED226/ INTLPOL290/ EPI237) beforehand.
Terms: Aut | Units: 1

BIOS 235: Foundations of Computer Science: What the Tutorial Didn't Tell You

The course provides non-computer-science students with a comprehensive understanding of computer science and software engineering principles for efficient code in modern scientific computing. Students gain theoretical knowledge and practical skills to advance programming proficiency and develop robust software systems. Upon completion, students have foundational knowledge in computer science, capable of writing better code for scientific computing. They gain expertise in selecting tools, designing modular software systems, and following best coding practices. They understand the importance of testing and version control in software development, ready to tackle advanced programming challenges in scientific applications. Prerequisites: Familiarity with a high-level programming language. More info and sign up at: https://forms.gle/qJSL1PBdokaUTLV6A
Terms: Aut | Units: 2
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