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Syllabus ( MBG 428 )


   Basic information
Course title: Systems Biology
Course code: MBG 428
Lecturer: Prof. Dr. Tunahan ÇAKIR
ECTS credits: 5
GTU credits: 3 (3-0-0)
Year, Semester: 1/2, Spring
Level of course: First Cycle (Undergraduate)
Type of course: Area Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: The Systems Biology course has two main goals: (a) provide the students with the fundamentals and applications of systems biology approach, which is based on the bioinformatic analysis of genome sciences and related experimental techniques; and (b) provide the students with the bioinformatic and statistical methods that are used for the computational analysis of genomics data, with a special focus on cellular metabolism.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Download transcriptome datasets from related online databases, and analyze them via basic statistical tests

    Contribution to Program Outcomes

    1. To be able to define the structure-function relationship at the molecular level in cells and organisms.
    2. To be able to work individually, make independent decisions and participate actively in multidisciplinary group studies.
    3. To be able to follow current scientific and technological innovations with the awareness of continuous learning and to apply them in the field.

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Grasp (i) the techniques used to collect high-throughput omics data, which consists of the experimental part of systems biology, (ii) the details of major cellular network types

    Contribution to Program Outcomes

    1. To be able to define the structure-function relationship at the molecular level in cells and organisms.
    2. To be able to follow current scientific and technological innovations with the awareness of continuous learning and to apply them in the field.

    Method of assessment

    1. Written exam
    2. Homework assignment
  3. Develop the skills to apply major statistics methods and data mining methods on the biological data by using a programming platform

    Contribution to Program Outcomes

    1. To be able to work individually, make independent decisions and participate actively in multidisciplinary group studies.
    2. To be able to follow current scientific and technological innovations with the awareness of continuous learning and to apply them in the field.
    3. To be able to drive hypotheses using existing knowledge, designing and conducting experiment for problem solving and make correct interpretation of the results obtained from the experiment.

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
   Contents Up
Week 1: - Fundamentals of Systems Biology: Basic Concepts (systems biology, molecular biology)
- From DNA to Proteins : chromosomes, genes, gene expression, protein synthesis, folding
Week 2: Cellular Networks: Metabolic reaction networks, Gene-regulatory networks, Signalling networks
Week 3: Cellular Networks and Graph Theory
Week 4: Experimental techniques for Genomics and Next Generation Sequencing
Week 5: Experimental techniques for Transcriptomics, Proteomics, Metabolomics, Fluxomics, Interactomics
Week 6: Programming and Statistics in R: application to biological data
Week 7: Statistical Significance Tests: Parametric and nonparametric significance tests
Week 8: Statistical Significance Tests: Use of R for Gene Expression Omnibus database, Gene Ontology (GO) Analysis
Week 9: Similarity Tests : Parametric and nonparametric similarity tests, Clustering techniques
Week 10: Unsupervised Dimension-reduction techniques: Principal Component Analysis, Independent Component Analysis
Week 11: Supervised Dimension-reduction techniques: Fisher Discriminant Analysis, Support Vector Machines
Week 12: Network Inference: Creating correlation networks from transcriptome data
Week 13: Mapping transcriptome data on protein-protein interaction networks
Week 14: MidTerm Exam
Application examples of Systems Biology
Week 15*: -
Week 16*: Term Project Presentations
Textbooks and materials: 1.Edda Klipp, Ralf Herwig, Axel Kowald, Cristoph Wierling. “Systems Biology in Practice: Concepts, Implementation and Application”, John Wiley and Sons, New York, 2005.

2.Bernhard O. Palsson. “Systems Biology: Properties of Reconstructed Networks” Cambridge University Press, 2006
Recommended readings: 1.Gregory N. Stephanopoulos, Aristos A. Aristidou, Jens Nielsen. “Metabolic Engineering: Principles and Methodologies”, Academic Press, San Diego, 1998

2. P. Venkataraman, “Applied Optimization with MATLAB Programming”, John Wiley and Sons, New York, 2009

3. Pierre Baldi, Soren Brunak. “Bioinformatics: The Machine Learning Approach”, The MIT Press, Cambridge, 2001

4. Stormy Attaway, "MATLAB- A practical introduction to programming and problem solving", Elsevier B-H, USA, 2012
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 14 20
Other in-term studies: - 0
Project: 11 20
Homework: 4,6,8,10,12,14 35
Quiz: 3,6,9,12 25
Final exam: - 0
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 2 14
Practice, Recitation: 0 0
Homework: 6 6
Term project: 10 1
Term project presentation: 1 1
Quiz: 1 4
Own study for mid-term exam: 8 1
Mid-term: 1 1
Personal studies for final exam: 0 0
Final exam: 0 0
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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