PUBS 2016

Biophysics 205A: Physical Underpinnings of Biological Systems

Fall 2016 Syllabus

 

Course Credit: 4 units

Course Format: 12 hours of lab per week

Location: Genentech Hall Teaching Lab - Room 227

Prerequisites: All incoming first year iPQB and CCB 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

Course Days/Hours: Monday, Tuesday, Wednesday 1pm-5pm

Final presentations: October 31, 1 pm

Instructors: Martin Kampmann, James Fraser

Course Coordinator: David Mavor

Co-Instructors: Hiten Madhani, Sy Redding, Seemay Chou, Hani Goodarzi

TAs: Daniel Asarnow, Yuliya Birman, Derek Britain, Charlotte Nelson, Pooja Suresh, Ruilin Tian

Lecturers/Facilitators: Joe DeRisi, Hiten Madhani, Jason Gestwicki, Martin Kampmann, Kurt Thorn, David Morgan, Tanja Kortemme, Eric Chow, David Kokel, James Fraser, Kyle Barlow, Justin Biel, Steve Altschuler, Lani Wu

Background

"Precision Medicine" is an emerging theme in biomedical research and patient care, and refers to the use of genome-wide information, such as DNA sequence, expression profiling, metabolic labeling/imaging, and other technologies to better inform and ultimately customize therapy. For cancer medicine, discreet genomic changes can be tied directly to particular treatments, such as immunotherapies or small molecules directed against a mutated enzyme. However, the cancer genome is not necessarily a static entity, and may be subjected to intense selective pressures resulting in highly dynamic changes that manifest as relevant phenotypes, such as drug resistance or metastatic potential. Technological revolutions, such as DNA microarrays, followed by ultra-deep sequencing, have allowed high-resolution views of the genome and dynamical views of the expression programs they exhibit.

Despite the promise for personalized care, many challenges remain. The genome, its expression, and its translation into phenotype embody a highly complex and dynamic system, whether it is a cancer cell, a yeast cell, or even a virus. Mutations that drive a phenotype may not be necessarily distinguishable from those that are mere passengers, and the molecular determinants of large-scale alterations remain largely uncharacterized.

Ultimately, the goal is the synthesis of predictive models that can reveal fundamental regulatory principles, and in the case of patients, deliver actionable information for treatment, early detection, and prevention.

Course Description

The course is a hands-on, project-based course that integrates deep mutational profiling (Fowler and Fields, Nature Methods, 2014, http://www.ncbi.nlm.nih.gov/pubmed/25075907), high-content microscopy and computational biology. The model organism, Saccharomyces cerevisiae, will be used as the organismal basis. Our goals are to experimentally determine the fitness of all possible individual point mutants of ubiquitin in the presence of different chemical perturbations, to quantify effects of the chemical perturbagens on cellular phenotypes, and to use structural modeling and bioinformatics queries of databases to formulate biological hypotheses for the mechanisms underlying our observations.

Ubiquitin is an essential protein that is a key cellular integrator of stress, under a variety of experimental perturbations. The library of these point mutants was assembled by Dan Bolon (http://profiles.umassmed.edu/profiles/display/133553) and verified during a summer visit to the Fraser lab. The course is organized around modules, described below. Each hands-on module will be accompanied by lectures (either "chalk talk" or with slides). Students will present short talks (hard limit: 5 minutes) to the class covering the assigned protocols or summarizing literature related to the class. In addition, students are expected to conduct their own literature reviews during the course of the project. Students will work in small teams, and each team will choose a different chemical perturbagen. Students are expected to remain in their teams for the duration of the course, although team-team collaboration is highly encouraged. All team members are expected to participate in each activity.

At the end of the class, each team will orally present their findings to the class and faculty, limited to 15 minutes and 15 slides maximum, with 10 minutes for discussion and questions. All members of the team are expected to speak and describe their contributions. These presentations are currently scheduled for Oct 31st, the final day of class.

Activities and speakers for each week will be announced at the beginning of each module.

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.

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.

Spreadsheet with a listing of multiple Python resources: https://docs.google.com/spreadsheets/d/1BjKsN0B1hqd4dJW5slZ5KPuToCjSMRyA7Bl8MwWrbS4/edit#gid=0

Students should be comfortable with basic syntax and scripting prior to the start of instruction.

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.

Student Learning Objectives

  • Laboratory safety
  • Appropriate methods for documenting laboratory procedures
  • Library prep and Ultra-Deep Sequencing
  • Bioinformatics and algorithms
  • Python scripting
  • Experimental design and planning
  • Yeast molecular biology
  • Introduction to high-content microscopy

 

Class Policies

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. 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. 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.

Teams

  • Graduate Mutant Ninja Pythons
    Wesley, Allison, Cynthia, Sasha
    carfilzomib
  • Jackpot!
    Peter, Jason, Taylor, Kaitlyn
    cobalt acetate
  • Baker's Beast:
    Douglas, Susanna, Keely, Greyson
    tamoxifen
  • Total Borons
    Matvei, Ian, Taia
    Ni(II)Cl2
  • Tequila Mocking Bird
    Lisa, Kevin, Gabe, Beatrice
    CB-5083
  • At Yeast We Showed Up
    Sophia, Andrew, Shizhong, Daniel
    p-fluoro-DL-phenylalanine
 

Week 1: Getting started

 

Reading:

- Fowler and Fields, 2014 (http://www.ncbi.nlm.nih.gov/pubmed/25075907)
- Finley et al., 2012 (http://www.ncbi.nlm.nih.gov/pubmed/23028185)
- Herhaus and Dikic, 2015 (http://www.ncbi.nlm.nih.gov/pubmed/26268526)

- Fred Sherman's "Getting started with Yeast" (https://instruct.uwo.ca/biology/3596a/startedyeast.pdf)

 

9/20: 

  • Martin Kampmann: Welcome, introductions, course overview
  • David Mavor: Introduction to the Biological question
  • Introduction: Chemical choice
  • Pickle project

 

Files for Computation:
- Allele_dic.pkl 
- translate.pkl
- aminotonumber.pkl

Folder
 

- Day 1 Lecture

Chemical Menu:

 

Yeast-GFP tagged strains:

 

9/21:

   Compute 101 PDF

   Experimental 101 PDF

 

Week 2: Optimizing experimental design

 

 

9/26:

 

9/27:

 

9/28: 

 

Week 3: Perturbation experiments

 

 

10/3:

 

10/4:

  • Sy Redding: Principles of Image Processing
  • Charlotte Nelson: Image Processing with scikit-image
  • Library collection day 1
  • Microscopy day 1

Skikit Image Lecture

Sample .nd2 stack

10/5:

 

Week 4: Molecular biology, Image analysis

 

 

10/10:

 

10/11:

 

10/12:

 

Week 5: Structural modeling

 

 

10/17:

 

10/18:

ddG Website

Calculated ddGs

Rosetta ddG Slides

Pymol examples

10/19:

 

Week 6: Integrative data analysis

 

 

10/24:

  • Student Journal club (Gabe): Lange et al. 2008 (http://www.ncbi.nlm.nih.gov/pubmed/18556554)
  • Student Presentations (1 representative per group, 5 min): Initial results and plans for remaining analysis
  • Second half of class: work in subgroups:
    • Biology / hypothesis finding / testing (Yuliya)
    • Image Processing (Daniel)
    • Structure/Rosetta (Jaime)
    • Visualization, clustering (Charlotte)
    • Statistics (Ruilin)
    • Dealing with sequencing errors / Hamming distance (Derek)

10/25:

 

10/26:

  • Steve Altschuler & Lani Wu: Phenotypic profiling
  • Student Journal club (Daniel): Humphris et al. 2007 (http://www.ncbi.nlm.nih.gov/pubmed/17722975)
  • Student Presentations (1 representative per subgroup): Results from the subgroups

 

October 31: Final presentations

 

For each group: 15 slides maximum, 15 minutes presentation + 10 minutes questions

Each group member needs to be involved in the presentation.