Bioinformatics is a rapidly developing field of knowledge. Each of the past Young Scientists Schools concerned topics urgent at those times. To preserve this tradition, the SBB-2019 will be dedicated to bioinformatics and systems biology in such areas as:
- Genomics and transcriptomic
- Evolutionary bioinformatics
- Structural biology: bioinformatics and molecular dynamics
- Systems biology: analysis of molecular-genetic systems and gene network modeling
- Biomedicine
- Genetics, genomics, bioinformatics and biotechnology of plants
Genomics and evolutionary bioinformatics
Analysis of genome data needs integration of databases on next generation sequencing, gene expression and gene functional annotation. We consider examples of whole-genome sequencing data processing using tools and databases from UCSC Genome Browser, ENCODE project, NCBI and Ensembl. We will discuss here problems of NGS data analysis including ChIP-seq, ChIP-exo, ChIA-PET methods.
Also, a computer assisted study of genomes of primates, ancient and modern humans can reveal insight to evolution processes. Computer models of molecular evolution and phylogenetically connected clusters of orthologous proteins, programming languages in biophysics and high performance computing simulation of evolutionary processes will be discussed on the section.
Structural biology and molecular dynamics
Structural Bioinformatics studies the structure of biological molecules and compounds, such as the structure of proteins, RNA structure, even the structure of DNA. There are two main approaches – molecular dynamics, which simulated the movement of molecules under the action of physical fields and statistical, containing a variety of methods, allowing on the basis of comparing the sequences of amino acids or nucleotides represented in databases to predict the structure of the molecule under study. In addition to predicting the structure of a single molecule this section will address the problem of studying of mechanisms of small molecule interaction with protein, prediction by computational methods of interaction of transcription factors (proteins that control the expression of genes) with DNA and others.
Molecular-genetic systems analysis
Currently, the design of efficient algorithms and systems to solve the inverse problem of mathematical modeling continues to be a challenge due to large volume and heterogeneity of biomedical data, as well as high computational complexity of biomedical applications. Variability in biological processes is observed on multiple scales and stems from different origins. Biological systems have evolved means to buffer unpredictable variation. In spite of intensive studies over the past decade, the mechanism of canalization and biological robustness remains controversial.
