Biophysics 205A: Physical Underpinnings of Biological Systems
Fall 2018 Syllabus
Location: Genentech Hall Teaching Lab - Room 227
Course Days/Hours: Monday, Tuesday, Wednesday 1pm-5pm
Final presentations: November 5, 1 pm
Instructor: Martin Kampmann
PUBS fellow / Course Coordinator: Robert Newberry
Co-Instructors: Sy Redding
TAs: Jean Costello, George Hartoularos, Snow Naing, Maureen Pittman, Nish Reddy
Lecturers/Facilitators: Martin Kampmann, Jason Gestwicki, Hiten Madhani, Charles Joseph, Eric Chow, DeLaine Larson, Sy Redding, Stephen Floor, Bill DeGrado, Tanja Kortemme, Lisa McConlogue
Teams:
- Last, but not yeast (TA: Jean): Bryan, Laura, Calla, Jack
- LAMMDA (TA: Snow): Laurel, Matt, Aji, Maru
- The Degenerates (TA: George): Daniel, Elissa, Wren, Christina
- Synucleus (TA: Nish): Elizabeth, Hayarpi, Garrett
- Sleeping Giants (TA: Maureen): Miriam, Nick, Stephanie
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
Background
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.
Suggested Reading
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.
Course Description
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.
Course Goals
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
- Bioinformatics
- Computer programming
- High-content microscopy
- Image processing
- Biophysical computation
Class Policies
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 ([email protected]); 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.
Schedule
Week 1 – Orientation, perturbation selection, growth optimization
Monday, September 17
Lecture:
by Martin Kampmann
Lecture:
by Robert Newberry
Labwork: Choose team names, select chemical perturbants, plan growth experiments
Group Presentation: Compound Choice
Tuesday, September 18
Computation: Set up cluster access
Server Basics, by Jean Costello:
Protocol Talk (Bryan): Growth Experiments
Tech Talk:
by Nish Reddy
Labwork: Preculture for growth experiment; prepare media
7:30pm: Induce expression
Wednesday, September 19
8:30am: Collect growth data
Lecture:
by Martin Kampmann
Labwork: Analyze growth data, repeat growth experiment
Computation: aSynuclein Barcode Analysis [pickle dictionary, plasmid sequence (FASTA), plasmid sequence (SnapGene), plasmid map (PNG)]
7:30pm: Induce expression
Thursday, September 20
8:30am: Collect growth data
Week 2 – Library selection
Monday, September 24
Lecture: Proteostasis, by Jason Gestwicki
Journal Club (Laurel): Outeiro and Lindquist. Yeast Cells Provide Insight into Alpha-Synuclein Biology and Pathobiology. Science 2003, 302, 1772-1775.
Journal Club (Daniel): Hillenmeyer, et al. The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes. Science 2008, 320, 362-365.
1 minute presentations from each group on growth experiment results
Protocol Talk (Elizabeth): Selection experiments
Labwork: Prepare media for selection experiments
Computation: a-Synuclein barcode association
by Snow Naing
7:30pm: Induce expression, replicate 1
Tuesday, September 25
8:30am: Collect miniprep samples, replicate 1 timepoint 1
Lecture: Everything you wanted know about yeast, but were afraid to ask, by Hiten Madhani
Journal Club (Miriam): Olzscha, et al. Amyloid-like aggregates sequester numerous metastable proteins with essential cellular functions. Cell 2011, 144, 67-78.
Journal Club (Laura): Khurana, et al. Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways. Cell Syst. 2017, 4, 157-170.
1 minute presentations from each group on barcode analysis
Labwork: Growth selection for replicate 1; Preculture for replicate 2
Computation: a-Synuclein barcode association
7:30pm: Collect miniprep sample, replicate 1 timepoint 2
7:30pm: Induce expression, replicate 2
Wednesday, September 26
8:30am: Collect miniprep sample, replicate 2 timepoint 1
Lecture: DNA Synthesis Technologies & Advances in Library Generation, by Charles Joseph
Journal Club (Matt): Starr, et al. Alternative evolutionary histories in the sequence space of an ancient protein. Nature 2017, 549, 409-413.
Journal Club (Hayarpi): Chong, et al. Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis. Cell 2015, 161, 1413-1424.
Labwork: Growth selection for replicate 2
Computation: a-Synuclein barcode association
7:30pm: Collect miniprep sample, replicate 2 timepoint 2
Week 3 – Microscopy, molecular biology
Monday, October 1
Lecture: Next-Generation Sequencing, by Eric Chow
Protocol Talk (Nick): Yeast miniprep
Labwork: DNA miniprep
Team presentations: a-Synuclein barcodes
Tuesday, October 2
8:30am: Induce expression
Lecture:
by DeLaine Larson
Protocol Talk (Calla): Microscopy
Protocol Talk (Aji): PCR Overview
Protocol Talk (Wren): PCR Day 1, PCR Day 2
Labwork: PCR Day 1; imaging
Wednesday, October 3
Lecture: Image Analysis, by Sy Redding
Labwork: PCR Day 2, sample pooling and submission
Computation: Begin image analysis
Week 4 – Biophysical computation, sequence analysis
Monday, October 8
Journal Club (Garrett): Toth-Petroczy, et al. Structured States of Disordered Proteins from Genomic Sequences. Cell 2016, 167, 158-170.
Journal Club (Stephanie): Vavouri, et al. Intrinsic protein disorder and interaction promiscuity are widely associated with dosage sensitivity. Cell 2009, 138, 198-208.
Computation: Barcode counting
Tuesday, October 9
Lecture: Biological Phase Separation, by Stephen Floor
Journal Club (Jack): Galvagnion, et al. Lipid vesicles trigger α-synuclein aggregation by stimulating primary nucleation. Nat. Chem. Biol. 2015, 11, 229-234.
Journal Club (Maru): Dettmer, et al. Loss of native α-synuclein multimerization by strategically mutating its amphipathic helix causes abnormal vesicle interactions in neuronal cells. Hum. Mol. Genet. 2017, 26, 3466-3481.
Tech Talk:
by Maureen Pittman Multiple Hypotheses ResourceComputation: Microscopy analysis, Fitness calculations
Wednesday, October 10
Lecture: Structural Biology of Amyloids, by Bill DeGrado
Followed by: Group presentations on progress update and next steps
Regrouping into focused subgroups
Week 5 – Subgroups
Monday, October 15
Lecture: Protein Biophysics, by Tanja Kortemme
Journal Club (Christina): Guerrero-Ferreira, et al. Cryo-EM structure of alpha-synuclein fibrils. eLife 2018, 7, e36402.
Journal Club (Elissa): Bousset, et al. Structural and functional characterization of two alpha-synuclein strains. Nat. Commun. 2013, 4, 2575.
Work in subgroups
Tuesday, October 16
Work in subgroups
Wednesday, October 17
Lecture: Alpha-Synuclein Therapeutics: Navigating the Energy Landscape, by Lisa McConlogue
Work in subgroups
3 pm: Subgroup presentations
Week 6 – Data analysis, presentation preparation
Monday, October 29
Robert: Presentation of background slides that will be shown before the presentations next week
Work in teams on presentations
Practice presentations around 3:30 pm (faculty will visit each team for this)
Tuesday, October 30
Work in teams on finalizing presentations
Wednesday, October 31
Work in teams on finalizing presentations
Practice presentations starting at 3 pm (each group: 10 min presentation, 10 min discussion)
Final Presentations
Monday, November 5, 3 pm