Biophysics 205A: Physical Underpinnings of Biological Systems
Fall 2017 Syllabus
Location: Genentech Hall Teaching Lab - Room 227
Course Days/Hours: Monday, Tuesday, Wednesday 1pm-5pm
Final presentations: October 26, 3 pm
Instructor: Martin Kampmann
PUBS fellow / Course Coordinator: Robert Newberry
Co-Instructors: Sy Redding, Seemay Chou, Hani Goodarzi
TAs: Taylor Arhar, Alison Maxwell, Daniel Schwarz, Douglas Wassarman, Taia Wu
Lecturers/Facilitators: Hiten Madhani, Jason Gestwicki, Robert Edwards, Charles Joseph, Eric Chow, DeLaine Larson, Sy Redding, Bill DeGrado, Tanja Kortemme
The alpha-team: Yessica, Jenna, Nish, Adam (TA: Alison) - compound: MG132
Rub-a-dub-dub 4 geeks in PUBS: Andrew, Taylor, Jean, Kyle (TA: Doug) - compound: tunicamycin
Yeast mode: Erik, Matt, Gracie, Lakshmi (TA: Taia) - compound: menadione
Buds: Jared, Snow, Christa (TA: Dan) - compound: spermidine
Aged Neuron Merge: Daniel, Maureen, George (TA: Taylor) - compound: geldanamycin
Course Credit: 4 units
Course Format: 12 hours of lab per week
Prerequisites: All incoming first year iPQB graduate students are required to enroll in this course.
Although TAs, instructors, and your fellow students will be happy to help out, it is important to be familiar with basic scripting and the principles of python PRIOR to starting the class. This will be covered in bootcamp.
Grading: Letter grade
Textbook: None. Lab protocols and course materials will be available in class or online
Current initiatives in biomedical research seek detailed understanding of the complex molecular basis for both normal physiology and disease pathology with the aim of developing targeted therapies uniquely suited to individual patients. This effort toward “precision medicine” involves a variety of discovery- and hypothesis-driven studies of the molecular, cellular, genetic, systems, and environmental contributions to biology and disease. Comprehensive, unbiased explorations of the cell and its components are key to this effort, as they illuminate the specific lesions underlying disease processes. Targeted interventions in cancer represent the greatest success story of precision medicine to date; many patients can be screened for disease-driving mutations that can be treated with specific molecules. Nevertheless, much remains to be unraveled about the physical, chemical, and biological basis for many diseases, particular those affecting the brain. Moreover, it has become clear that genetic factors are incomplete for describing many normal and aberrant processes, indicating an important contribution from environmental factors, whether they derive from the particular palette of components specific to different cell types, the unique tissue microenvironment, or agents encountered by the organism writ large.
An excellent example of this challenge concerns the protein α-synuclein. An abundant protein expressed predominantly in neurons, α-synuclein gained notoriety in 1997 when genetic and pathological studies implicated it in Parkinson’s disease, which affects at least 500,000 people in the United States alone. In Parkinson’s disease, α-synuclein misfolds and aggregates into toxic protein assemblies that can cause neuronal death. How α-synuclein contributes to neurodegeneration remains unclear and controversial, owing in part to an incomplete understanding of its biophysical properties and cellular interactions. Though mutations to α-synuclein can cause Parkinson’s disease, most Parkinson’s cases have little or no genetic basis. In addition, though expressed widely throughout the brain, α-synuclein pathology impacts only a small subset of neurons, indicating a strong contribution from cellular context. How this protein interacts with its cellular environment is therefore of significant interest for understanding the etiology of Parkinson’s disease.
In this course, we will examine how environmental factors affect α-synuclein misfolding and toxicity with the goal of clarifying how α-synuclein contributes to neurodegeneration.
Bendor, J. T.; Logan, T. P.; Edwards, R. H. The function of α-synuclein. Neuron 2013, 79, 1044–1066.
Downey, A., Think Python. Green Tea Press: Needham, MA, 2012.
Fowler, D. M.; Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 2014, 11, 801–807.
Lashuel, H. A.; Overk, C. R.; Oueslati, A.; Masliah, E. The many faces of α-synuclein: from structure and toxicity to therapeutic target. Nat. Rev. Neurosci. 2013, 14, 38–48.
Sherman, F. Getting started with yeast. Methods Enzymol. 2002, 350, 3–41.
The centerpiece of this course is an interdisciplinary research project that will be completed in teams. Students will perform the experiment, collect and analyze the data, and draw conclusions. Though extensive support will be provided by faculty and instructional staff, students will be encouraged to explore and execute their own ideas. The results thereby obtained will be integrated into a manuscript for peer-reviewed publication. Course content will be delivered through a combination of lectures from guest faculty, technique-focused talks from instructional staff, and literature reviews by students. Students will also present oral progress reports and give a final oral presentation of their findings for a wide audience.
The goal of the course is to provide an immersive, hands-on experience in the context of genuine research questions. As articulated by Vale and colleagues (http://www.sciencemag.org/content/338/6114/1542.long), there are tremendous advantages when graduate students work "pursuing a research question with unknown answers and uncertain outcomes, students and faculty combine their wits and skills to design experiments, evaluate progress, and troubleshoot along the way". These advantages are likely to be common accross all learning levels (http://blogs.kqed.org/mindshift/2014/09/can-project-based-learning-close-gaps-in-science-education/). In our course, teams may use whatever literature, software, and resources that are available publicly, and are encouraged to write their own scripts and software where necessary.
This course will introduce students to approaches and methodologies for interrogating biological systems in high throughput, which will require the integration of experiment and computation. In addition to fundamental techniques in modern molecular biology and bioinformatics, students will learn to interpret and leverage large datasets, draw original conclusions, and present findings in written and oral formats.
The "official" language of the class is python (https://www.python.org) - beginners should try Learn Python The Hard Way (http://learnpythonthehardway.org/book/), people with a background in other languages should try Google's python course (https://developers.google.com/edu/python/). The QB3 Berkeley intensive python course (http://intro-prog-bioinfo-2014.wikispaces.com/) provides many biological examples. Students should be comfortable with basic syntax and scripting prior to the start of instruction.
Spreadsheet with a listing of multiple Python resources: https://docs.google.com/spreadsheets/d/1BjKsN0B1hqd4dJW5slZ5KPuToCjSMRyA7Bl8MwWrbS4/edit#gid=0
Student Learning Objectives
- Laboratory safety
- Scientific documentation
- Experimental design
- Yeast manipulation
- Molecular biology techniques
- Library preparation
- Deep sequencing
- Computer programming
- High-content microscopy
- Image processing
- Biophysical computation
Ethics: This course is more than a training experience; it is an active research project whose results will be published to the broader scientific community. The community must be able to understand our work, replicate it, and have confidence in its findings. We must therefore ensure the integrity of the information we disseminate. To do so, it is essential that students perform and document their experiments and analyses as faithfully as possible. Mistakes and oversights are normal and to be expected, but they must not be ignored, concealed, or disguised. In addition, to merit authorship, students must contribute to three aspects of the project: intellectual conception or interpretation of the methods or data, technical execution of the experiments and/or analyses, and documentation or dissemination of the results. We fully expect that by actively participating in the course and working toward the course objectives, all students will merit authorship.
Respect: This course is built around an open research project performed in teams. Successful completion of the course objectives will require that students work together effectively, so please respect the time and effort of your classmates and instructors. Moreover, as part of the research process, we will consider and debate a variety of ideas and approaches; however, we must not allow our position on a particular idea or argument to compromise our respect for its author. We therefore expect course participants to give all instructors and students, regardless of academic or personal background, their complete professional respect; anything less will not be tolerated.
Absences: The instructor must be notified by the second week of classes for any planned absences, or in advance of class due to illness. Active participation in the laboratory is essential and students are required to attend normal class hours. Occasional attendance outside of regular class hours will also be necessary, as indicated by the syllabus. Attendance during the final presentation is absolutely mandatory, except in cases of doctor-excused medical illness. Any class material or lecture that is missed will be the responsibility of the student. Unexcused absences may affect the final course grade. Written evaluations of each team and its members will be provided to the Graduate Tracking System for inclusion into the graduate record, and provided to oral committee members and thesis committee members.
Accommodations for students with disabilities: The Graduate Division embraces all students, including students with documented disabilities. UCSF is committed to providing all students equal access to all of its programs, services, and activities. Student Disability Services (SDS) is the campus office that works with students who have disabilities to determine and coordinate reasonable accommodations. Students who have, or think they may have, a disability are invited to contact SDS (StudentDisability@ucsf.edu); or 415-476-6595) for a confidential discussion and to review the process for requesting accommodations in classroom and clinical settings. More information is available online at http://sds.ucsf.edu. Accommodations are never retroactive; therefore students are encouraged to register with Student Disability Services (http://sds.ucsf.edu/) as soon as they begin their programs. UCSF encourages students to engage in support seeking behavior via all of the resources available through Student Life, for consistent support and access to their programs.
Week 1 – Orientation, perturbation selection, growth optimization
Monday, September 18
Lecture: Introduction and Course Goals, by Martin Kampmann
Lecture: Introduction to a-Synuclein, by Robert Newberry
Labwork: Choose team names, select chemical perturbants, plan growth experiments
Group Presentation: Compound Choice
Computation: Set up cluster access
Tuesday, September 19
Tech Talk: Organizing Experiments, by Taylor Arhar
Tech Talk: Aseptic Technique, by Dan Schwarz
Protocol Talk (George): Growth Experiments
Labwork: Preculture for growth experiment; prepare media
8pm: Induce expression
Wednesday, September 20
8:30am: Collect growth data
Lecture: Yeast as a Model Organism, by Hiten Madhani
Labwork: Analyze growth data - 1 minute presentation from each group on your results
Tech Talk: GitHub and Version Control, by Alison Maxwell
Tech Talk: Yeast Plasmids and Expression, by Doug Wassarman
Computation: Barcode association
Week 2 – Library selection
Monday, September 25
Lecture: Proteostasis, by Jason Gestwicki
Team presentations: Barcode Analysis
Lecture: Chemical Genetics, by Martin Kampmann
Journal Club (Yessica): Outeiro and Lindquist. Yeast Cells Provide Insight into Alpha-Synuclein Biology and Pathobiology. Science 2003, 302, 1772-1775.
Journal Club (Andrew): Hillenmeyer, et al. The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes. Science 2008, 320, 362-365.
Protocol Talk (Erik): Selection experiments
Labwork: Prepare media for selection experiments
8pm: Induce expression, replicate 1
Tuesday, September 26
8:30am: Collect miniprep samples, replicate 1 timepoint 1
Lecture: Biology of α-Synuclein, by Robert Edwards
Journal Club (Jared): Olzscha, et al. Amyloid-like aggregates sequester numerous metastable proteins with essential cellular functions. Cell 2011, 144, 67-78.
Journal Club (Daniel): Khurana, et al. Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways. Cell Syst. 2017, 4, 157-170.
Labwork: Growth selection for replicate 1; Preculture for replicate 2
8pm: Collect miniprep sample, replicate 1 timepoint 2
8pm: Induce expression, replicate 2
Wednesday, September 27
8:30am: Collect miniprep sample, replicate 2 timepoint 1
Lecture: Gene synthesis, by Charles Joseph
Journal Club (Jenna): Starr, et al. Alternative evolutionary histories in the sequence space of an ancient protein. Nature 2017.
Labwork: Growth selection for replicate 2
8pm: Collect miniprep sample, replicate 2 timepoint 2
Week 3 – Microscopy, molecular biology
Monday, October 2
Lecture: Next-Generation Sequencing, by Eric Chow
Journal Club: Schlecht, et al. A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions. Nat. Commun. 2017, 8, 15586.
Protocol Talk: Miniprep
Labwork: DNA miniprep; yeast preculture for microscopy
Tuesday, October 3
8:30am: Induce expression
Lecture: Microscopes and Image Acquisition, by DeLaine Larson
Protocol Talk: Microscopy
Protocol Talk: PCR
Labwork: PCR; imaging
Wednesday, October 4
Lecture: Image Analysis, by Sy Redding
Protocol Talk: PCR purification
Tech Talk: SciKit Image
Labwork: PCR purification, sample submission
Computation: Begin image analysis
Week 4 – Biophysical computation, sequence analysis
Monday, October 9
Lecture: Structural Biology of Amyloids, by Bill DeGrado
Journal Club: Chong, et al. Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis. Cell 2015, 161, 1413-1424.
Tech Talk: SciPy
Computation: Image preparation; sequencing quality
Tuesday, October 10
Lecture: Biophysical Computation, by Tanja Kortemme
Journal Club: Bousset, et al. Structural and functional characterization of two alpha-synuclein strains. Nat. Commun. 2013, 4, 2575.
Computation: Barcode counts; cell identification; explore biophysical options
Wednesday, October 11
Journal Club: Flagmeier, et al. Mutations associated with familial Parkinson's disease alter the initiation and amplification steps of α-synuclein aggregation. Proc. Natl. Acad. Sci. USA 2016, 113, 10328-10333.
Computation: Fitness scores; colocalization analysis; pilot biophysical computations
Week 5 – Data Analysis
Monday, October 16
Journal Club: Toth-Petroczy, et al. Structured States of Disordered Proteins from Genomic Sequences. Cell 2016, 167, 158-170.
Group Presentation: Preliminary Data Analysis
Computation: Fitness score trends; quantify aggregates; scale up computations
Tuesday, October 17
Computation: Correlation of fitness scores and biophysical parameters
Wednesday, October 18
Computation: Correlation of imaging phenotypes and predicted biophysical properties
Week 6 – Data analysis, presentation preparation
Monday, October 23
Group Presentation: Update on Data Analysis
Tuesday, October 24
Wednesday, October 25
Thursday, October 26
3PM: Final Presentations