Computational Biology

Computational Biology (for first degree students)

Modern biomedicine, shaped by novel, complex experimental methods, is
 generating massive data that can no longer be analyzed by traditional
 computational tools. Progress in biomedicine now substantially depends 
 on  advanced computational methods, turning bioinformatics into a key area 
 of  biomedical research and technology. On the other hand, biomedicine 
 motivates  novel problem areas for computational scientists.

  The course gives an overview of modern developments in biomedicine, 
 “omics”  (genomics/transcriptome/proteomics/metabolomics) data types, and
 computational approaches to analysis and integration of these data. Computational  applications can be technological in nature, e.g. development of  methods for  the analysis of deep sequencing data or high-density oligonucleotide microarray as used for genome-wide association studies, or specifically  addressing concrete biomolecular questions, drug design, systems biology and population genetics oriented computer modeling.

Major topics covered are:

Functional genomics: an overview of highthroughput omics data:
DNA micrioarrays: expression, tiling, SNP
next generation sequencing (NGS): DNA-seq, RNA-seq, ChIP-seq;
mass-spectroscopy: metabolomics and proteomics
Microarray experiments: design, normalization, statistical issues
Genome assembly from short reads
Analysis of transcriptome data: mapping of reads, detection of transcribed isoforms, denovo transcriptome assembly
Analysis of  whole genome data (RNA/DNA editing, epigenetics patterns, transcription factor binding sites): motif discovery, signal peaks across genome
Haplotype reconstruction, and haplotype frequency estimation
Proteomics and metabolomics: isotope patterns, protein-protein interactions and protein modifications
Downstream  analysis of highthroughput biological data: multivariate and factor analysis, ANOVA and regression methods, clustering, discrimination
Integration of physiological/clinical measurements and omics  data
Systems biology approaches: modeling genetics, genomics, and biochemical networks
Autoregulation and multistability in the biological systems: bifurcations and chaos in biochemical processes
Mutations and adaptive evolution

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