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CoS 518: Computational and Integrative Systems Biology

This course introduces the modern genomics and systems biology practices to students of a wide background. The course aims to introduce biological concepts and methods to computer scientists as well as core mathematical and programming skills to life sciences graduates. In doing so, an integration of these disciplines will be possible with the aim of advancing the research frontier helping to solve the most challenging health and environmental problems of our times.

Contents:

  • Programming and Database Management: programming skills, basic computing concepts, program design, abstraction and modularity, Python / Java / PERL, relational databases and SQL
  • Statistical Inference: probability theory, information theory, basic descriptive statistics, Bayesian and frequentist inference, descriptive analysis of large data sets, practical experience with R
  • Statistical Genetics: genetic epidemiology, segregation analysis and path analysis, parametric and non-parametric linkage analysis, QTL analysis, linkage disequilibrium analyses, phylogeny and cladistics
  • Genes and Genomes: modes of inheritance, chromosomal, somatic and mitochondrial disorders, complex trait disorders, linkage analysis for single gene and complex traits, linkage disequilibrium, animal models, physical mapping, the Human Genome Project, high-throughput sequencing, DNA and protein databases, principles of homology and motif identification
  • Functional genomics: transcriptomics, proteomics, metabolomics, metabonomics, RNAi screens, functional studies, network analysis
  • Bioinformatics: mathematical methods in biology, advanced tools for the analysis of biological data, DNA sequence analysis and annotation, DNA alignment algorithms and DNA/protein homology, identification and delineation of protein families, evolutionary processes, phylogenetic analysis of protein sequences and residue conservation
  • Structural Biology: methods and analysis of protein structure, secondary and tertiary protein structure prediction, analysing structure-function relationships
  • Systems Biology: analysis of whole genomes (eukaryotic and microbial genetics), pathways and signalling networks, transcriptional profiling, gene expression analysis, proteomics, metabolomics
  • Modelling Biological Systems: statistical and dynamical modelling, numerical methods, model selection and parameter inference, sensitivity analysis, stochastic processes
  • Ethics: Ethical issues in contemporary genetics
  • enes and Genomes: modes of inheritance, chromosomal, somatic and mitochondrial disorders, complex trait disorders, linkage analysis for single gene and complex traits, linkage disequilibrium, animal models, physical mapping, the Human Genome Project, high-throughput sequencing, DNA and protein databases, principles of homology and motif identification
  • Functional genomics: transcriptomics, proteomics, metabolomics, metabonomics, RNAi screens, functional studies, network analysis
  • Bioinformatics: mathematical methods in biology, advanced tools for the analysis of biological data, DNA sequence analysis and annotation, DNA alignment algorithms and DNA/protein homology, identification and delineation of protein families, evolutionary processes, phylogenetic analysis of protein sequences and residue conservation
  • tructural Biology: methods and analysis of protein structure, secondary and tertiary protein structure prediction, analysing structure-function relationships
  • Systems Biology: analysis of whole genomes (eukaryotic and microbial genetics), pathways and signalling networks, transcriptional profiling, gene expression analysis, proteomics, metabolomics
  • Modelling Biological Systems: statistical and dynamical modelling, numerical methods, model selection and parameter inference, sensitivity analysis, stochastic processes
  • Ethics: Ethical issues in contemporary genetics
gender equality

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