COS 514: Advanced Topics in Computational and Mathematical Biology
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Course Title |
Advanced Topics in Computational and Mathematical Biology |
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Course Code |
COS 514 |
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Course Type |
Elective |
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Level |
PhD |
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Instructor’s Name |
Prof. George K. Christophides (Lead Instructor) |
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ECTS |
5 |
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Lectures / week |
90 min/week for 7 weeks |
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Laboratories / week |
90 min/week for 7 weeks |
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Course Purpose and Objectives |
The course deals with advanced topics in modern computational biology focusing on “omics” technologies, computational analysis tools of biological data and mathematical modelling. Its purpose is to equip students that embark on PhD studies in areas related to biology with the basic knowledge and awareness of advanced concepts and techniques in specific topics, which would allow them to progress in their studies. It aims at introducing, on the one hand, students from diverse but not Biology BSc and MSc backgrounds to biological concepts, data and methods and, on the other hand, students from Biology BSc and MSc backgrounds to core mathematical and programming skills. |
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Learning Outcomes |
By the end of the course, the students will receive hands-on knowledge on the state-of-the-art computational and mathematical modelling techniques. Specifically, they are expected to have a: - Good understanding and application of computational biology concepts relevant to this module and commonly used algorithms in bioinformatics, modelling and statistics;
- Be able to write basic scripts and pipelines for automating and repeating analyses that make use of the taught techniques;
- Be aware of the use of computers in studying mathematical functions and carrying out statistical tests;
- Basic grasp of computer programming and biological data management;
- Capacity to assess biological inferences that rest on computational, mathematical and statistical arguments;
- Understanding of how sound conclusions about the underlying processes using their knowledge of mathematics and statistics.
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Prerequisites |
None |
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Background Requirements |
None |
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Course Content |
Week 1: Advanced concepts of genes, genetics and genomics: 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. Week 2: Bioinformatics: advanced tools for the analysis of biological data, DNA sequence analysis and annotation, DNA and protein alignment
algorithms and DNA/protein homology, identification and delineation of protein families, evolutionary processes, phylogenetic analysis of proteinsequences and residue conservation.
Week 3: Systems Biology: analysis of genomes (eukaryotic and microbial genetics), pathways and signalling networks, genome wide association studies, microbiomics, transcriptomics, proteomics and metabolomics. Week 4: Biostatistics: probability theory, information theory, basic descriptive statistics, Bayesian and frequentist inference, descriptiveanalysis of large data sets and practical experience with R.
Week 5: Programming and Database Management: programming skills, basic computing concepts, program design, abstraction and modularity, Python / Perl / Javascript, relational databases and SQL. Week 6: Mathematical modelling: statistical and dynamical modelling, numerical methods, model selection and parameter inference, sensitivityanalysis and stochastic processes.
Week 7: Epidemiological and population dynamics modelling: concepts of Practical 1 and essay write-up (weeks 1-4): The topics of the practical may vary from year-to-year and will focus on the analysis of genomes and population genomics data, RNA sequencing data and microbiome data.
Practical 2 and essay write-up (weeks 4-7): The topics of the practical may vary from year-to-year and will focus on mathematical and population dynamics modelling focusing on infectious disease epidemiology. Guest lectures: a series of guest lectures from computational biologists on topics related to but extending knowledge beyond the lectures and
practicals, including multi-omics phylogenetic and network analyses, medical informatics, computational structural biology, in silico drug
discovery, and ethical issues in contemporary genetics.
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Teaching Methodology |
- 7 x 3-hour lectures/laboratories |
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Bibliography |
- Course notes, research articles |
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Assessment |
Assessment will be done through marking of the two assignments developed and completed during the course in the form of essays/manuscripts. The final mark will be the average of individual marks for these essays. Formative assessment provided during the formal presentation of the results from these assignments will not count against the final mark and will aim to guide students in improving their scientific presentation skills on the subject. |
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Language |
English |
