Computational regulatory genomics 1000-5D25OGR

The turbulent development of molecular biology resulted in a growing demand for the application of mathematics and computer science tools, especially methods from such fields as algorithmics, machine learning, probabilistic methods and statistics. The seminar topics focus on the computational analysis of molecular data, with particular emphasis on methods to better understand the regulation of gene expression. Many presentations concern current research projects in which seminar participants are involved.

Our recent interests include the following topics:

- Predicting the activity of regulatory sequences. Despite the vast amount of experimental data available on genomic regulatory regions such as enhancers and promoters, predicting the effect caused by even a small mutation is challenging. To better understand the principles of regulatory regions, we are developing convolutional neural networks to predict the activity of any DNA sequence in different cell types. (Methods: statistical data analysis, machine learning.)

- Reconstructing gene regulatory networks. The interactions between chromatin, transcription factors and genes form a complex network of interactions, allowing gene expression to change across time and space. These interactions can be reconstructed using single cell sequencing data. (Methods: statistical data analysis, probabilistic methods.)

- Spatial organization of genomes. The three-dimensional architecture of the genome in the cell nucleus allows regulatory elements to be brought closer in space to the genes they regulate, despite the considerable distance measured along the genome sequence. We are trying to predict the formation of such contacts and determine their function. (Methods: statistical data analysis, machine learning.)

- Predicting the structure and function of proteins. Proteins are chains of amino acids that perform their function when folded into a tertiary structure. It turns out that the chains of some proteins have a non-trivial topology after folding, forming knots. We predict their formation and possible effect of the knotting on the function of these proteins. (Methods: molecular dynamics simulations, knot theory, machine learning.)