Quanitative Biology Bootcamp 2022

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.
  2. From 8/17 to 8/19 the Bootcamp will run from 9am to 3:30pm, with orientation activities from your respective graduate programs starting at 3:30pm onwards. From 8/22 to 8/26 we will go until 4:30pm.
  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. The first day we all meet, everybody will give a 90 second lightning talk. The idea is to use that time to tell us about who you are and what you’re interested in both inside and outside the lab. We include some of our favorite examples on Slack. Please, send a single slide in PDF format (no PowerPoint, Keynote, Google Slides or anything else) to Hernan () by 8pm on Tuesday 8/16. The slides will be combined in about one document on Hernan’s computer. We have a lot of people, so note that we will be ruthless about timing! ;)
  6. Every afternoon, there will be time for study hall. During this time the two of us and a few TAs will be available to help you with anything you want to go over again. In addition, this will be the time to work on your “estimate” in groups of three. The idea is to put a five minute presentation on some in-field or out-of-field estimate. For in-field, some examples from past students include estimating the speed of actin polymerization based on the movement of listeria inside cells, computing the energy budget of a cell as it goes through the cell cycle, etc. For out-of-field, some past favorites include MythBuster about Rapunzel’s story and whether you can really cry a river after a breakup, etc. The final talks will be on the last day, followed by a party!

 

Instructors:

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

Graduate Student Instructors:

Andrea Herman
Andrea Herman
(Physics)
Arman Karshenas
Arman Karshenas
(Biophysics)
Brandon Schlomann
Brandon Schlomann
(MCB)
Nick Gravina
Nick Gravina
(Physics)
Yasemin Kiriscioglu
Yasemin Kiriscioglu
(Physics)
Dushyant (Yovan) Badal
Dushyant (Yovan) Badal
(Physics)
Jesseon Chang
Jesseon Chang
(MCB)
Sarah Johnson
Sarah Johnson
(Computational Biology)
Elise Kerdoncuff
Elise Kerdoncuff
(Postdoc, PhD - Population Genetics)
Nathan Cramer
Nathan Cramer
(MCB)
 

Tentative plan for each day:

9.00-12.30     Hands-on lecture and discussion

12.30-14.00   Lunch talk: Topics on Quantitative Biology @Cal

14.00-15.30   Hands-on lecture and discussion

15.30-16.30   Study Hall

Number Date Topics Slides Python Code Suggested Readings Notes and Videos
1 8/17 Order of Magnitude Biology 

Bootcamp Introduction (slides)

Biological Numeracy and Human Impacts  Exams (slides)

 

 

 

 

Bootcamp Introduction:

  1. Parsegian1997: Adrian Parsegian and Bob Austin debate in a very graphical and interesting way on the role of physics in biology. 
  2. Cohen2004: Great article by Joel Cohen arguing how math and theory can become a microscope for biology.
  3. Luria1943: The Luria-Delbruck experiment, one of the greatest examples of using math as a microscope to uncover molecular mechanism in biology.
  4. Phillips2019: A concrete example of predictive understanding in the context of transcriptional regulation in bacteria.

Biological Numeracy Exam:

  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 8/18 Scales and Scaling in Biology: Physical Limits to Cell Growth
Physical Limits to Bacterial Growth (slides)
Bacteria Growth simulation (Colab notebook)
  1. Neidhardt1999: Bacterial Growth: Constant Obsession with dN/dt. Neidhardt's ode to the equation that shaped his scientific life.
  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. VanOijen2006: Single molecule measurements of DNA replication.
  6. Belliveau2021: A more detailed analysis of the fundamental physical limits to bacterial growth.
  1. Biological Time Scales and Exponential growth (notes)
  2. Limits to Bacterial Growth (notes)
3 8/19 Thinking Big About Data
Hypothesis Testing (slides) Hypothesis Testing (Colab notebook)

1. Antedisciplinary: Perspective by Sean Eddy

2. Efron1979: Introduction to Bootstrap    resampling

3. Luria-Delbrück experiment: An example of using Bootstrap resampling to characterize uncertainty

4. John Snow and Cholera: John Snow's investigation in London.

5. Mendel and peas: Mendel's original       paper published in 1865

6. Data 8 textbook: Introduction to Data     Science at UC Berkeley.

  
4 8/22 Diffusion as Biology’s Null Model for Dynamics
Diffusion as Biology's Null Model for Dynamics (slides)

Diffusion by coin flips (Colab notebook)

Diffusion by chemical master equations (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)
5 8/23 Thinking About Big Data
Association (slides) Disease association (Colab notebook)

1. Gusella1983: Discovery of gene related huntington's disease

2. Hall1990: BRCA1 mapping

3. RischMerikangas1996: Considerations for Association vs. Linkage studies

4. Klein2005: Age-related Macular degeneration association study

5. Host COVID Initiative 2022: Genome-wide study for host genetic susceptibility analysis

Multiple hypothesis testing (notes)
6 8/24 Life is Flux: The Equations of Biological Dynamics

Dynamics of the Constitutive Promoter (slides)

Mean expression dynamics of the constitutive promoter (Colab notebook)

Noise dynamics of the constitutive promoter (Colab notebook)
  1. Neidhardt1999: Bacterial Growth: Constant Obsession with dN/dt. Neidhardt's ode to the equation that shaped his scientific life.
  2. Monod1949: One of Monod's paper revealing how bacterial growth dynamics inform our understanding of physiology and gene regulation.
  3. Golding2005: Groundbreaking measurement of gene expression dynamics in single E. coli cells using the MS2 system.
  4. Liu2021: Measurement of the rate of RNA polymerase elongation in flies using the MS2 and PP7 systems.
  5. Zenklusen2008: Great smFISH experiment showing that, while some genes follow a Poisson distribution of mRNA molecules, some don't.
  6. Jones2014: Predicting transcriptional noise from DNA architecture in simple repression.
  7. Mohapatra2016: Using filament length distributions to uncover mechanisms of polymerization.
  1. Mean expression dynamics of the constitutive promoter (notes)
  2. Noise dynamics of the constitutive promtoer (notes)
7 8/25 Finding patterns in Big Data

Dimensionality Reduction (slides)

 Dimensionality Reduction (Colab notebook)

1. Novembre 2008: Genes mirror geography

2. Tabula-Muris2021: Gene expression clusters by tissues

3. 1000 Genomes Project: Support for Out of Africa migration

 Principal Component Analysis (notes)
8 8/26

Dynamical Systems and Genetic Switches

Course survey

Dynamical systems and Genetic Switches (slides)

Course-wrap (slides)

 The mutual repression genetic switch (Colab notebook)
  1. Garcia2007: During lecture, we very casually talked about binding and cooperativity. This paper, specifically targeted to life scientists, provides an introduction to binding problems.
  2. Imai1990: Comparing different models of cooperativity in Oxygen binding to hemoglobin.
  3. Gardner2000: A synthetic genetic switch in bacteria.
  4. Laso2006: A great example of dynamical systems thinking in hematopoiesis.
 The genetic switch (notes)