Computational Approaches to Biology

BIOL66021 Computational Approaches to Biology 15 Semester 1 Postgraduate Taught Level 6 Jean Marc Schwartz Unit coordinator School of Biological Sciences Division of Informatics, Imaging and Data Sciences 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 Computational methods are increasingly used in all areas of biology in order to better understand complex living systems and develop models that generate testable predictions. This unit introduces a range of computational techniques, including differential equations, machine learning, network and constraint-based analyses, which are used for a wide range of biomedical and biotechnological applications, from understanding how intracellular signalling pathways are disrupted in diseases to redesigning organisms by metabolic engineering. Computational methods are increasingly used in all areas of biology in order to better understand complex living systems and develop models that generate testable predictions. This unit introduces a range of computational techniques, including differential equations, machine learning, network and constraint-based analyses, which are used for a wide range of biomedical and biotechnological applications, from understanding how intracellular signalling pathways are disrupted in diseases to redesigning organisms by metabolic engineering. This unit aims to introduce students to a wide range of computational methods and tools required to carry out interdisciplinary research in the biological sciences. Students should:<ul><li>Understand essential mathematical concepts required for biological research.  </li><li>Understand and apply differential equations modelling of intracellular systems.</li><li>Understand and apply constraint-based modelling of metabolic systems.</li><li>Understand and apply network analysis and logical modelling of molecular systems.</li><li>Understand and apply data analysis and machine learning methods.  </li><li>Understand the applications and limitations of different modelling techniques and tools.</li><li>Understand and use RNA-sequencing data analysis methods. </li></ul> Students should: <ul> <li> Develop problem-solving skills.</li> <li> Construct models and design experiments to test biological hypotheses.</li></ul> Students should: <ul> <li>Use the Python language and develop models using the Jupyter Notebook system.</li> <li>Construct models of signalling, regulatory and metabolic systems.</li> <li>Infer computational models from biological data.</li></ul> Students should: <ul> <li> Develop computational skills.</li> <li> Develop report writing skills.</li> <li> Learn to communicate computational results.</li></ul> Analytical skills Critical appraisal of research papers. Project management To be able to meet deadlines for written and experimental work. Problem solving Planning of modelling strategies to test a specific hypothesis. Research Learning computational techniques and applying these to your planned goals. Written communication Scientific writing skills preparing a report. The unit will start with an introduction to the Python programming language. Students will be introduced to the Jupyter Notebook system, a widely used online application allowing the development of code for data analysis and numerical simulation.  The core of the unit will be structured along four main sections, each covering a particular set of techniques and applications:Section 1: Dimensionality reduction and clustering<ul><li>Dimensionality reduction: Principal Component Analysis (PCA) and interactive plotting with applications to visualising single-cell expression data.</li><li>Clustering: hierarchical k-means and mixture model clustering.</li><li>Non-linear dimensionality reduction methods: GPLVM and t-SNE for non-linear dimensionality reduction and visualisation of single-cell data.</li></ul>Section 2: Models of large cellular systems<ul><li>Network reconstruction and analysis: protein-protein interaction networks, metrics for network analysis, integration of high-throughput biological data.</li><li>Logical modelling: Boolean models, logical steady state analysis, applications to cancer systems.</li><li>Constraint-based modelling of metabolic networks.</li></ul>Section 3: Dynamic models<ul><li>Introduction to differential equations-based modelling.</li><li>Modelling gene regulatory pathways: gene transcription and cellular signalling pathways.</li></ul>Section 4: Next-generation sequencing  <ul><li>Next-generation sequencing I: from laboratory experiments to computational analysis.</li><li>Next-generation sequencing II: RNA-seq workflow. </li></ul> The unit will be delivered as a succession of lectures (1 hour) and practical sessions (2 hours), where each lecture will introduce the theory behind a method/tool, and students will apply the method/tool to solve a particular biological problem in the practical.Students will be assessed by completing four written assessments, one for each of the main sections of the unit. These assessments will consist of a series of short questions and mini-project reports, some of which will require some computer code to be written.  0 Other 100% Four online modules worth 20%, 27%, 20% and 33% respectively.  Verbal feedback will be communicated during the practical sessions.Written feedback will be communicated through annotated comments for each online assessment and report submission. MSc Bioinfor and Systems Biol MSc Bioinf and Systems Biology PGDT Taught Component Mandatory N Specific material will be provided with each lecture. Lectures 12 Practical classes & workshops 24 0 114