Modeling of complex biological systems

obligatory courses

Models and inferences in computational molecular biology, focusing on how statistics and machine learning methods are used to understand complex systems.

We examine modern challenges in modeling and understanding complex biological systems through data. High-throughput molecular measurements have necessitated development and application of statistics and machine learning, giving rise to computational biology. Microarray and sequencing technologies enable us to quantify how complex systems are responding to and influenced by experimental and external conditions. It may lead to better understanding fundamental organizational principles and functionalities of molecules and cells. Lately, there have been interesting developments in single cell analyses, spatial genomics, imaging and others that involve higher resolutions, scales, and complexities.

In this course, we study exploratory data analysis, statistical learning, and neural networks that are specifically designed for such biological studies. Good understanding of statistics and programming are prerequisites. Students will program in R and Python, read primary literature weekly, and complete data analysis projects.

An Introduction to Statistical Learning with Applications in R

by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani

The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

by Trevor Hastie, Robert Tibshirani, Jerome Friedman

Deep Learning with Python

by Francois Chollet

Students will be asked to read selected original research and review papers.

At the end of the course, the students will:

- know major developments in computational biology and computational models for select biological systems,

- be able to analyze selected biological data underlying complex systems,

- be able to read and write scientific reports.

Participation, Homeworks, Project Report, Presentation.