Quantitative Biology Bootcamp

Biology is being revolutionized by new experimental techniques that have made it possible to quantitatively query the inner workings of molecules, cells and multicellular organisms in ways that were previously unimaginable. The objective of this course is to respond to this deluge of quantitative data through quantitative models and the use of biological numeracy. The course will explore the description of a broad array of topics from modern biology using the language of physics, mathematics and statistics. One style of thinking we will emphasize is to perform simple calculations that one can do with a stick in the sand.

We will draw examples from broad swaths of modern biology from our department and beyond, including cell biology (cell growth, signaling and regulation), physiology (metabolism), developmental biology (patterning of body plans, how size and number of organelles and tissues are controlled), neuroscience (action potentials and ion channel gating) and evolution (population genetics) in order to develop theoretical models that make precise predictions about biological phenomena. These predictions will be tested through the hands-on analysis of experimental data and by performing numerical simulations using Python. Quantitative and physical biology will be introduced as an exciting new tool to complement other approaches within biology such as genetics, genomics and structural biology. The course will introduce students to the enabling power of biological numeracy in scientific discovery and make it possible for them to use these tools in their own future research.
 

Logistics

  1. The Bootcamp will include first year students from MCB, Biophysics and Computational Biology, as well as a few outside visiting students. In total, we will have around 60 people! No previous math, physics or coding experience will be assumed. This means that we will go through everything carefully and hope that those that are experts at one or other topic will suspend disbelief, have fun, and help their neighbors and friends. Even for the experts, we guarantee that there will be plenty of new topics/ examples we’ll cover that you won’t be bored. The syllabus for the course can be found here. 
  2. From 8/18 to 9/1 the Bootcamp will run from 9am to 3:30pm, with study halls and experimental rotations starting at 3:30pm onwards. 
  3. All sessions will be in the first floor conference room of the IGI building. The building is locked to outsiders, but we’ll work on getting you card key access as soon as you get an ID card! If you need to get in and nobody is there you can message us on Slack or call/message us.
  4. We will code in Python using the web-based Google CoLab. This means that you don’t need fancy computers. In fact, you can use your iPad/ laptop. Just be mindful of coming with as much battery as possible, as there might not be enough ports for everybody to charge their devices at the same time. 
  5. To learn more about Python and start ahead of the course, you can explore the content and tutorials available on CodeAcademy, Coursera, and W3schools. 
  6. We are excited to offer experimental rotations this year for those who are interested in learning more about research in different fields. Students are asked to fill out the following survey to indicate their top five lab/rotation preference by Saturday 19 August. The survey can be found here. 

 

Instructors:

Hernan Garcia
Hernan Garcia
(MCB/ Physics)
Priya Moorjani
Priya Moorjani
(MCB/ Computational Biology)
 
 

Graduate Student Instructors:

Arman Karshenas
Arman Karshenas
(Biophysics)
Yasemin Kiriscioglu
Yasemin Kiriscioglu
(Physics)
 

Tentative plan for each day:

9.00-12.00     Hands-on lecture and discussion with coffee break

12.00-12:30   Lunch 

12.30-14.00   Research talk and informal session with speaker 

14.00-16.00   Hands-on lecture and discussion with coffee break

16.00-16.30   Study hall and time to work on estimates 

17.00             Optional experimental rotation 

Number Date Speaker Title
1 8/18

Rebecca Heald

Biological size control and scaling
2 8/21

Priya Moorjani

 Genomic Insights into Human History & Evolution
3 8/22

Dan Fletcher

  
4 8/23

Peter Sudmant

 Genome assembly / pangenomes / structural variation
5 8/24

Max Staller

 Transcription, evolution, theory, molecular mechanism
6 8/25

Oskar Hallatschek

Scale-dependence in ecology and evolution
7 8/28

Aaron Streets 

 
8 8/29

Carlos Bustamante

 
9 8/30

Ahmet Yildiz

Single molecule fluorescence imaging and applications to molecular motors
10 8/31

Susan Marqusee

  
Number Lab (instructor) # days and dates available Project(s) description
1 Fletcher (Liya Oster)

Monday-Thursday (one group Mon-Tue, one group Wed-Thu), Aug 21-24
6-8 students per set of two days!
 

Building a microscope: In the Fletcher Lab rotation, students will gain hands-on experience with the basic principles behind practical light microscopy and fluorescence microscopy to build a simple functioning microscope from component parts.
2 Yildiz (Joseph Slivka)

Monday-Wednesday (one group each day), Aug 21-23 

Monday-Thursday (one group each day), Aug 28-31

2 students per group

 Single molecular motor stepping: We will train students in the single molecule technique TIRF microscopy to study in vitro reconstitution of intracellular transport in real time and with super resolution. We will explain TIRF microscopy and how it is used to study biological questions, then utilize it to observe how motors move by observing their stepping pattern or by the presence of different cargo adaptors.
3 Yildiz (Joy Zhong)

Monday-Friday (one group each day), Aug 21-25

Monday-Wednesday (one group each day), Aug 28-31

2 students per group 

 Microtubule Motility assay: We will train students in the single molecule technique TIRF microscopy to study in vitro reconstitution of intracellular transport in real time and with super resolution. We will explain TIRF microscopy and how it is used to study biological questions, then utilize it to observe how motors move by observing their stepping pattern or by the presence of different cargo adaptors.
4 Bustamante (Francesca Bravo)

Monday Aug 21: 4:30-6:00pm  (group 1), and 6:00-7:30pm (group 2)

4 students per group

Pulling DNA with optical tweezers: We will use high-resolution optical tweezers to study at the single-molecule level the response of DNA to mechanical forces and to characterize protein-DNA interactions.
5 Bustamante (Robert Sosa) Tuesday Aug 22: 4:30-5:30pm (group 1), 5:30-6:30pm (group 2), and 6:30-7:30pm (group 3) Protein folding with optical tweezers: We will use high-resolution optical tweezers to follow individual trajectories of protein (un)folding in real-time.
6 Hallatschek

Monday-Wednesday (one group each day), Aug 28-30 

4 students per group

 Evolution! 
7 Garcia (Andrea Herman & Yovan Badal) 

Monday-Thursday (two groups each day apart from Thursday), Aug 21-24

Monday-Thursday (one group each day), Aug 28-31 

3 students per group

 Measuring transcription cycle in fly embryos: Students will measure the live initiation and elongation rate of RNA transcripts for a Drosophila early embryo morphogen protein on a confocal microscope using a fluorescent reporter construct.  
8 Garcia (Brandon Schlomann) 

Tuesday, Friday, Thursday (one group each day), Aug 22,25, and 31

3 students per group

 Single-cell immune response in living fly larvae: We will use light sheet fluorescence microscopy to peer directly into the immune systems of live fly larvae and measure patterns of single-cell gene expression during bacterial infection. We will explore how mathematical models can be used to connect static measurements of gene expression distributions to the underlying single-cell dynamics, providing evidence for or against mechanisms such as transcriptional bursting and the formation of spatial microenvironments. 
Number Date Topics Slides Python Code Suggested Readings Notes and Videos Extra Problems 
1 8/18 Order of Magnitude Biology 

Bootcamp Introduction (slides)

A Feeling For Numbers in Biology (slides)

Bootcamp Introduction:

  1. Cohen2004: Great article by Joel Cohen arguing how math and theory can become a microscope for biology.
  2. Phillips2015: Article by Rob Phillips arguing for models to precede experiments, and not to be relegated to the last figure of papers.
  3. Gunawardena2014: Jeremy Gunawardena reminds us that models in biology are not supposed to be accurate descriptions of nature, but just accurate descriptions of our “pathetic thinking”.

A Feeling for the Numbers in Biology:

  1. Sender2016: A revised estimate of the number (and types) of human and bacterial cells in our body.
  2. Stouthamer1973: A theoretical survey of the ATP requirements for making a new bacterial cell.
  3. Schmidt2016: Survey of protein copy number in E. coli using mass spectrometry. A great paper to think about the cell census and it’s reproducibility.
  4. Baker1998: Tania Baker’s take on the amazing fidelity of DNA polymerase.
  5. Lynch2015: Mike Lynch’s great paper on the bioenergetic cost of a gene including a survey of the energetic demands of a number of cell types.

Biological Numeracy Exam:

  1. Introduction to Biological Numeracy Exam and A feeling for numbers in biology (video)
  2. Which is bigger, mRNA or the protein it codes for? (video, notes)
  3. Who is more powerful on a per kilogram basis, Barbara McClintock or the Sun?Sun vs human power (video, notes)
  4. How many genes are there in E. coli? (video, notes)
  5. What's the most abundant cell type in humans? (video, notes)
  6. How many sugar transporters are needed to make a bacterium? (video, notes)
  7. What is the rate of ATP consumption per µm3 of cytoplasm? (video, notes)
  8. How long does it take to exhaust the ATP pool in the absence of ATP synthase? (video, notes)
  9. What length book would DNA polymerase (including proofreadring) copy without an error? (video, notes)

Human Impacts Exam:

  1. Introduction to Human Impacts Exam (video)
  2. What is the ratio of mass of cows relative to humans? (video, notes)
  
2 8/21 Stuff(t): Bacterial Growth

Bacterial growth and scaling (slides) 

Basic Python and bacteria growth simulation (Jupyter notebook) 

Bacterial growth using colony images (Jupyter notebook) 

  
  1. Neidhardt1999: Bacterial Growth: Constant Obsession with dN/dt. Neidhardt’s ode to the equation that shaped his scientific life.
  1. Biological Time Scales and Exponential growth (notes)
  1. Logistic growth (Jupyter notebook) 
  2. Bacteria growth using colony images (Jupyter notebook)
3 8/22 Statistical Thinking: Hypothesis Testing, uncertainty and p-values

Hypothesis testing and understanding uncertainty (slides) 

Mendel peas experiment and Cholera in London (Jupyter notebook) 

  
  1. Efron1979: Introduction to Bootstrap    resampling
  2. Luria-Delbrück experiment: An example of using Bootstrap resampling to characterize uncertainty
  3. John Snow and Cholera: John Snow's investigation in London.
  4. Mendel and peas: Mendel's original paper published in 1865
  5. Data 8 textbook: Introduction to Data Science at UC Berkeley.

 

    
4 8/23 Null Hypotheses and the Great Probability Distributions of Biology: The Constitutive Promoter

Null Hypotheses + Constitutive Promoter

(slides)

 

mRNA production and degradation (colab notebook)

 

mRNA synthesis with the Euler method (colab notebook)

 
  • Neidhardt1999: Bacterial Growth: Constant Obsession with dN/dt. Neidhardt’s ode to the equation that shaped his scientific life.
  • Monod1949: One of Monod’s paper revealing how bacterial growth dynamics inform our understanding of physiology and gene regulation.
  • Golding2005: Groundbreaking measurement of gene expression dynamics in single E. coli cells using the MS2 system.
  • Zenklusen2008: Great smFISH experiment showing that, while some genes follow a Poisson distribution of mRNA molecules, some don’t.
  • Jones2014: Predicting transcriptional noise from DNA architecture in simple repression.
  • Clarke1946: Examining the distribution of flying bombs hitting London during WWII using the Poisson distribution.
  1. Mean expression dynamics of the constitutive promoter (notes)
  2. Noise dynamics of the constitutive promoter (notes)
  
5 8/24 Diffusion as Biology's Null Hypothesis for Dynamics

Diffusion (slides)

Diffusion via coin flips (colab notebook)

  
  1. Droz1962: Classic measurement of the movement of aminoacid molecules down an axon.
  2. Hochbaum2014: Optical measurement of electrical signal propagation through an unmyelinated neuron.
  3. English2011: Tracking individual GFP molecules as they diffuse inside E. coli
  4. Cui2007: Measuring active transport in neurons by tracking quantum dots.
  1. Introduction to diffusion (video, notes)
  2. Estimate: Measuring diffusion constants by tracking single molecules (video, notes)
  3. Estimate: Ion channel currents (video, notes)
  4. Solving the diffusion equation by coin flips (video, notes)
  5. Solving diffusion using master equations (video, notes)
  
6 8/25 Statistical Thinking: Correlations, Associations and more p-value

Statistical Thinking (slides)

Covid Regression (Jupyter notebook)

Covid Association (Jupyter notebook)

 

      
7 8/28

Gradient Formation and the French Flag Model

Dynamical Systems and Mutual Repression

French flag model (slides)

  
  1. Testing the French Flag model (Driever1988 and Liu2013)
  2. Measuring Bicoid degradation (Drocco2011)
  3. Measuring Bicoid diffusion (Abu-Arish2011)
  4. Measuring bicoid localization (Little2011)
  5. Measuring Bicoid translation (Petkova2014) 

 

    
8 8/29

Statistical Mechanics and Regulatory Biology

Regulatory Biology (slides)

Image analysis (colab notebook)

  
  1. A first exposure to statistical mechanics for life scientists: applications to binding (Garcia 2015)
  2. Garcia2011: Quantitative dissection of the simple repression input-output function
  3. Brewster2014: The transcription factor titration effect dictates level of gene expressionAnd notes:
  4. Lecture Notes: Simple Repression
  
 
9 8/30 Defiance is the Secret of Life, Part I          
10 8/31 Defiance is the Secret of Life, Part II          
11 9/1 Presentations