Molecular Systems Biology

Nowadays increasing numbers of complete genomic sequences are available and methods have been developed to study system wide gene expression, protein abundances and interactions and metabolite formation. Systems biology integrates the results of the different omics techniques in order to understand how they work together by using dedicated analysis and visualisation techniques (e.g. machine learning, networks). These methods allow the extraction of relevant information for elucidating the function of genes, transcripts, proteins and metabolites, to signify interactions between these molecules, and to study the underlying regulatory mechanisms. The course is modular and focuses on data generation, mining, analysis, and data integration and visualisation.

After successful completion of this course students are expected to be able to:

• Explain variation control and experimental design used in the generation of omics datasets
• Apply univariate and multivariate statistical and machine learning (ML) methods to analyse omics data
• Compare these statistical methods with each other and explain which method is applied for which type of biological question
• Interpret mass spectrometry datasets of proteins
• Construct, enhance and mine biological networks of molecular interactions from omics data
• Analyse network topology using precise mathematical definitions, R and Cytoscape
• Construct protein classes based upon network properties
• Evaluate 'omics' information with respect to the biological questions

Assumed Knowledge:

The course accepts students with a biological background. Preferably the student will have some experience with bioinformatics tools and statistics: the courses Computational Biology (SSB34306 ) and Advanced statistics (MAT20306) are highly recommended.