This course is devoted to the study of key methods of computational biology of aging, which have had a significant impact on the field in recent years. We will take a detailed look at aging clock models, survival curves, Gompertz models, dynamical models as well as key aspects of the biology of aging (including its molecular part). Students will get experience in differential gene expression or methylation analysis in the context of aging, hazard ratio models fitting and interpretation, aging clocks construction and their exploration and develop an intuition in dynamical systems modeling. The course aims to form a systematic view of the biology of aging, and offer a holistic computational methodology for the study of this problem.
Any person who is interested in Aging Biology has a basic technical or bioinformatics background.
Please check this list of prerequisites if needed. Each subject is a clickable link to an appropriate introductory course.
- Python programming
- Fundamentals of biology
- Fundamentals of statistics
- Fundamentals of linear algebra
- Fundamentals of calculus
This is an open project for everyone to improve it. If you would like to contribute to the book, please refer to the github page.
We would like to thank Skoltech for the help in creating this book and the opportunity to test the material presented in it as part of the course of the same name.
@online{kriukov2023compagingbook,
year={2023},
author={Dmitrii Kriukov and Irina Zhegalova and Simon Steshin and Dmitrii Smirnov and Ekaterina Khrameeva},
title={Computational Biology of Aging},
url={https://computationalaginglab.github.io/computational_aging_course/intro.html},
}