Syllabus
Computational structural biology aims to model and predict the structure of bio-molecules (e.g., proteins, DNA, RNA). This fields of study has a broad impact on the understanding of gene regulation processes and biological system mechanisms. It also open new research directions in synthetic biology such as the design of new bio-molecular devices and the re-engineering of biological functions.
The topics covered will range from the classic techniques in the field (e.g. molecular dynamics and hidden Markov models) to more recent advances in analyzing and predicting RNA and protein structure (e.g. conditional random fields tree grammars).
The class is intended for grad students interested in the application of mathematical and computational methods to molecular biology and also for biologists and biochemists interested in the algorithmic foundations of the approaches used in this field. Prerequisites: Basic understanding of algorithms. No biological background required. The class will provide background for any advanced methods discussed. The evaluation will consist of paper reviews and a final collective project.
Topics
RNAs:
- Secondary structure prediction,
- Multiple alignment,
- Statistical mechanics of RNA structures,
- RNA-RNA interactions,
- RNA design,
- Mutational analysis,
- Molecular evolution,
- RNA folding kynetics.
- Secondary and tertiary structure prediction,
- Threading, side-chain packing,
- Molecular dynamics, motion planning,
- Protein folding on HP lattices,
- NMR related algorithms,
- Folding pathways.
- Amyloids and transmembrane proteins,
- Protein-protein interactions.
Image produced with RNAmovies
Course informations
Instructor: Jérôme Waldispühl
Lectures: Tuesday-Thursday 2:00pm - 3:30pm in McConnell 320.
Office hours: TBA.
Schedule and Material
TBA
References
Computational Molecular Biology: An Introduction
Peter Clote, Rolf Backofen
Wiley Series in Mathematical and Computational Biology
Credits
Notes have been released using material kindly provided by Peter Clote, Ivo Hofacker, David Mathews, Alain Laederach, Thomas Simonson and Jinbo Xu.