Course Title: Physical Underpinnings of Biological Systems (PUBS)
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.
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
Instructors: James Fraser; jfraser+bp205a@fraserlab.com
Course Coordinator: David Mavor; David.Mavor@ucsf.edu
Microscopy Coodinator: Laura Deming; biophysics205a@gmail.com
TAs:
Rosetta Helpers:
Lecturers/Facilitators:
James Fraser, Joe DeRisi, Hiten Madhani, Michael Keiser, Sourav Bandyopadhyay, Jessica Lund, Eric Chow, Nadav Ahituv, Ryan Hernandez, Elaine Meng, Bo Huang, David Morgan, Jason Gestwicki, Kurt Thorn, Steven Altschuler, Lani Wu, Tanja Kortemme, Wendell Lim, Adam Abate, Matthew Thomson, Dave Toczyski
Important Dates:
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 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, automated microscopy, and computational biology. The model organism, Saccharomyces cerevisiae, will be used as the organismal basis. Our goal is to experimentally determine the fitness of all possible individual point mutants of ubiquitin, 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 and verified during a summer visit to the Fraser lab. The course is organized around three modules, described below. Each hands-on module will be accompanied by lectures (either “chalk talk” or with slides). The course director and/or each lecturer will assign research papers, literature reviews, or other reading material in advance. 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 be assigned a different perturbation for initial analysis. 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.
After module 1 and module 3, 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 27th and Nov 24th, the final day of class.
Activities and speakers for each week will be announced at the beginning of each module.
Module 1. Sept 29th – Oct 22nd. Ultra-deep sequencing, and chemical genomics.
Module 2. Oct 27th – Nov 5th. Ensemble vs. single observation measurements.
Module 3. Nov 10th – Nov 24th. Computational biology and evolutionary constraints.
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, 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. 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 - beginners should try Learn Python The Hard Way, people with a background in other languages should try Google’s python course. The QB3 Berkeley intensive python course provides many biological examples. Students should be comfortable with basic syntax and scripting prior to the start of instruction.
In module 1, each team will be provided an “unknown” chemical perturbation. Using deep mutational profiling, each team will measure the fitness of all possible individual point mutants of ubiquitin, an essential protein that is a key cellular integrator of stress. Upon processing the sequencing data, each team will perform comparisons against a reference dataset.
In module 2, teams will compare their data against the datasets of other teams and perform microscopy experiments to determine whether their stress response elicits a multimodal response in growth rate. This module will reinforce core concepts of ensemble vs. single observation measurements at many levels of biophysics and systems biology.
In module 3, the teams will leverage interactions with Tanja Kortemme and members of her lab to perform protein design protocols to predict sequences optimized for multiple criteria. They will test how well their deep sequencing data matches with protein design profiles generated under different constraints such as: protein stability or maintaining interactions with specific mediators of stress responses. For the final presentations, teams will explain the unique features of their Ubiquitin mutant profile by grounding their analysis in specific protein-protein interactions along branches of the cellular proteostasis network.
Student Learning Objectives
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 all of the three required presentations 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: Please see the instructor as soon as possible if you need particular accommodations, and we will work out the necessary arrangements.
##Lab work and recommended reading schedule
Week 1 – Theme: Ubiquitin and Deep Mutational Profiling
Lab work: Measure doubling times as a function of small molecule perturbation concentrations Lecturers: James Fraser (9/29,9/30), Joe DeRisi (9/29), Hiten Madhani (10/1)
Recommended reading:
Files for Computation:
Other Class Material:
Week 2 – Theme: Chemical Genetics
Lab work: Performing selection experiments under chemical stresses Lecturers: Michael Keiser (10/6), Sourav Bandyopadhyay (10/7), Eric Chow (10/8)
Recommended reading:
Other Class Material:
Week 3 – Theme: Massive Functional Profiling
Lab work: Deep sequencing library preparation Lecturers: Nadav Ahituv (10/13), Journal Club (10/14), Ryan Hernandez (10/15)
Journal Club Assignments:
Recommended reading:
Other Class Material:
Week 4 – Theme: Sequence Conservation and Statistical Mechanics
Lab Work: Computational analysis of sequencing data Lecturers: Joe DeRisi (10/20), Elaine Meng (10/21), Bo Huang (10/22)
Recommended reading:
Other Class Material:
Week 5 – Theme: Stress Response Networks
Lab Work: Comparisons of perturbations between teams Lecturers: Student Presentations (10/27), David Morgan (10/28), Jason Gestwicki (10/29)
Recommended reading:
Other Class Material:
Sub-groups:
Week 6 – Theme: Single Cell/Molecule vs. Bulk Measurements
Lab Work: Growth rates via microscopy and bulk measurements Lecturers: Kurt Thorn (11/3), Steven Altschuler and Lani Wu (11/4), Tanja Kortemme (11/5), Wendell Lim Kilobot Demo (11/5)
Recommended reading:
Other Class Material:
Week 7– Theme: Constraints on Stability and Interaction Specificity
Lab Work: Computational protein design Lecturers: Kyle Barlow (11/10), Samuel Thompson (11/12)
Recommended reading:
Other Class Material:
Week 8– Theme: Constraints in the Context of Networks
Lab Work: Comparison of computational design and selection experiments Lecturers: Adam Abate (11/17), Peter Turnbaugh (11/18), Dave Toczyski (11/19)
Other Class Material:
Recommended reading:
References:
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