In this preliminary module, you will learn some basic skills to analyze biological processes using Python.

• 0.1 – Tutorial – Welcome to Python (William Raymond)
• 0.2 – Tutorial – Getting Started with Python Tutorial (Dr. Luis Aguilera)
• 0.3 – Invited Seminar – How to Hang with a Modeler (Dr. Carlos Lopez)
• 0.4 – Tutorial – Systems Biology in Python, PySB Tutorial (the Lopez Lab)
• 0.5 – Tutorial – Basic Image Processing in Matlab Tutorial (Dr. Brian Munsky)

In this module, you will learn how to perform some basic image processing tasks in Python.

• 1.1 Lecture – Single-Cell Labeling Techniques (Dr. Linda Forero-Quintero)
• 1.2 Lecture – Single-Cell Microscopy Techniques (Dr. Doug Shepherd)
• 1.3 Tutorial – Single-Cell Segmentation (Dr. Zachary Fox)
• 1.4 Invited Seminar – Measuring and Controlling Gene Expression in Single Bacterial (Dr. Mary Dunlop)
• 1.5 Invited Seminar – Quantitative analysis receptor spatiotemporal organization and signaling (Dr. Khuloud Jaqaman)
• 1.6 Tutorial – Single Particle Tracking in Python (Dr. Luis Aguilera)

In this module you will learn how to do some basic statistical analyses to quantify single-cell measurements.

• 2.1 – Lecture – Basic Machine Learning Tools for Supervised and Unsupervised Analyses of Single-Cell Data (William Raymond)
• 2.2 – Tutorial – Basic Statistical Analyses in Python (Dr. Huy Vo)
• 2.3 – Invited Seminar – Inferring Cellular Regulation from (too much) Data (Dr. Doug Shepherd)
• 2.4 – Invited Seminar – Applications of Image Translation Networks for Laser-Scanning Microscopy (Dr. Jesse Wilson)
• 2.5 – Tutorial – Basics of Clustering and Machine Learning for Single-Cell Data (William Raymond)

In this module, you will learn how to formulate and run a stochastic simulation (Gillespie) algorithm to generate trajectories for single-cell gene regulation processes.

• 2.1 – Lecture – Learn how to describe single-cell reaction processes in terms of the stoichiometry vectors and propensity functions (Michael May)
• 2.2 – Tutorial – Write your Own Stochastic Simulation Algorithm in Python (Lissa Weber)
• 2.3 – Invited Seminar – Whole Cell Stochastic Models (Dr. Zan Luthey-Schulten)
• 2.4 – Invited Seminar – The Roles and Consequences of Randomness in Biological Systems (Dr. Linda Petzold)
• 2.5 – Tutorial – Totally Asymmetric Simple Exclusion Process Models for Single mRNA Translation (William Raymond)

In this module, you learn how to formulate and solve the chemical master equation for the direct analysis of probability distributions for single-cell gene regulation processes.

• 4.1 – Lecture – Introduction to the Chemical Master Equation, and the Finite State Projection Algorithm (Michael May)
• 4.2 – Tutorial – Solving the Chemical Master Equation using the Finite State Projection Approach (Michael May)
• 4.3 – Invited Seminar – Designing Optimal Microscopy Experiments to Harvest Single-Cell Fluctuation Information while Rejecting Imaging Distortion Effects (Dr. Brian Munsky)
• 4.4 – Invited Seminar – Stochastic dynamics of gene regulatory networks: numerical techniques to bridge models and experiments (Dr. Elizabeth Read)
• 4.5 – Tutorial – Computing the Likelihoods of Single-cell Data using a Stochastic Model (Dr. Zachary Fox)

In the final module of the course, you will learn about the application of Bayesian analysis and MCMC approaches to quantify uncertainty of models given experimental data.

• 5.1 – Tutorial – Bayesian Analysis and Markov Chain Monte Carlo (MCMC) Algorithms (Dr. Steve Presse)
• 5.2 – Invited Seminar – Resolving a single molecule theorist’s greatest anxiety: the model ambiguity problem (Dr. Steve Presse)
• 5.3 – Invited Seminar – Combing quantitative experiments with predictive modeling to understand cell biology (Dr. Gregor Neuert)
• 5.4 – Tutorial – MCMC Methods for Quantification of Parameter Uncertainties (Dr. Huy Vo)

The 2021 UQ-BIO Summer School featured five discussion panels that addressed different career concerns most often faced by graduate students. Four of these were recorded, and links to these are posted below.

• Applying for and choosing the right graduate program (e.g., what to look for, who to contact, how to evaluate different options)
• Working in an interdisciplinary research environment (e.g., collaborating across disciplines, communicating research results, academic integrity in research, rigor and reproducibility).
• There is more to life and science than academics (e.g., students groups, meeting colleagues at seminars or conferences, internships, important non-technical classes to consider, non-academic careers)
• Navigating the treacherous waters of graduate school (e.g., setting goals and focus, time management, work life balance, choosing a good project or team, self-advocacy, finding a good mentor)
• Justice, Equity, Diversity and Inclusion in STEM (e.g., recognizing, avoiding or leaving a toxic workplace, identifying and addressing bias, conflict resolution, finding supportive communities in grad school, asking for help, recognizing and addressing imposter syndrome)