Course

BIO-369: Randomness and information in biological data

Lectures in this course (28)

Quantifying Randomness in Biological Data

Covers randomness and information in biological data, focusing on discrete random variables and their quantification.

Discrete Random Variables: Medical Testing

Explores discrete random variables, joint probabilities, and medical testing quality using Bayes' theorem.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Luria-Delbrück Experiment: Testing Evolution Hypotheses

Explores the Luria-Delbrück experiment, demonstrating how probabilities are used to test evolution hypotheses.

Quantifying Statistical Dependence

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Luria-Delbrück Experiment

Explores the implications of the Luria-Delbrück experiment on evolutionary mechanisms and the importance of probabilities in understanding biological data.

Thermal Fluctuations: Importance at Cellular Scale

Explores the impact of thermal fluctuations at the cellular scale, emphasizing randomness in biological processes and the distribution of kinetic energy in gas molecules.

Introduction to Maximum Entropy Inference

Introduces maximum entropy inference for finding optimal probability distributions with minimal extra structure.

Thermal Fluctuations in Gases

Explores thermal fluctuations in gases and the strength of various bonds, crucial in biological processes.

Maximum Entropy Modeling: Applications & Inference

Explores maximum entropy modeling applications in neuroscience and protein sequence data.

Biopolymers and Biomembranes

Explores the importance of biopolymers and biomembranes in cell mechanics and their flexibility under thermal fluctuations.

Protein Structure Prediction

Explores predicting protein structure from sequence data using maximum entropy modeling and discusses recent advancements in protein structure prediction.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Protein Abundance: Random Walks

Explores protein abundance as a random walk and the Moran model in population genetics.

Nonlinear Dimension Reduction Methods

Explores nonlinear dimension reduction methods preserving neighborhoods in lower-dimensional spaces.

Random Walks and Moran Process

Explores random walks in biological systems, protein abundances, and the Moran process in population genetics.

Biological Randomness and Data Analysis

Explores randomness in biology, covering thermal fluctuations, random walks, and data analysis techniques.

Random Walks: Moran Process and Examples

Explores random walks, the Moran process, and examples in population genetics, protein abundance modeling, and bacterial chemotaxis.

Random Walks and Moran Model in Population Genetics

Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.