• Explores computational and experimental approaches to analyzing complex biological networks and systems. Includes genomics, transcriptomics, proteomics, metabolomics and microscopy. Stresses the practical considerations required when designing and performing experiments. Also focuses on selection and implementation of appropriate computational tools for processing, visualizing, and integrating different types of experimental data, including supervised and unsupervised machine learning methods, and multi-omics modelling. Students use statistical methods to test hypotheses and assess the validity of conclusions. In problem sets, students read current literature, develop their skills in Python and R, and interpret quantitative results in a biological manner. In the second half of term, students work in groups to complete a project in which they apply the computational approaches covered.

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