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| MBTZ5033 | Computational Biology | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of BIOTECHNOLOGY (INTERDISCIPLINARY) | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Cihan İNAN | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | To achieve that Learn the fundamental of computational biology, Process the biological data on computer, Comprehend the relation between biology and computational world, Use the open-access databases and software tools |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Learn the fundamental of computational biology | 2,6 | 1,3, | | PO - 2 : | Process the biological data on computer | 2,6 | 1,3, | | PO - 3 : | Comprehend the relation between biology and computational world | 2,6 | 1,3, | | PO - 4 : | Use the open-access databases and software tools | 2,6 | 1,3, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| The algorithmic and machine learning foundations of computational biology, combining theory with practice; the principles of algorithm design for biological datasets, and analyze influential problems and techniques; analyze real datasets from large-scale studies in genomics and proteomics including genomes (biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly), networks (gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution) and evolution (comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution) |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Fundamentals of computational biology | | | Week 2 | The principles of algorithm design for biological datasets | | | Week 3 | Analyze influential problems and techniques | | | Week 4 | Analyze real datasets from large-scale studies in genomics | | | Week 5 | Gene finding, RNA folding, sequence aligntment, genome assembly | | | Week 6 | Networks (gene expression analysis, regulatory motifs, graphical algorithms, network motifs) | | | Week 7 | Biological databases | | | Week 8 | Mid-term exam | | | Week 9 | Comparative genomics | | | Week 10 | Phylogenetics | | | Week 11 | Genome editing | | | Week 12 | Online tools for computational biology | | | Week 13 | Online tools for computational biology | | | Week 14 | Online tools for computational biology | | | Week 15 | Student presentations | | | Week 16 | Final Exam | | | |
| 1 | Computational Biology: Unix/Linux, Data Processing and Programming By: Robbe Wunschiers ISBN: 354021142X | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | 8. hafta | | 30 | | Presentation | 15 | 15. hafta | | 20 | | End-of-term exam | 16 | 16. hafta | | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 15 | 45 | | Sınıf dışı çalışma | 4 | 15 | 60 | | Arasınav için hazırlık | 4 | 7 | 28 | | Arasınav | 1 | 1 | 1 | | Ödev | 1.5 | 15 | 22.5 | | Dönem sonu sınavı için hazırlık | 3 | 15 | 45 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 202.5 |
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