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Syllabus ( BENG 458 )


   Basic information
Course title: Bioinformatics and Systems Biology Laboratory
Course code: BENG 458
Lecturer: Assoc. Prof. Dr. Tunahan ÇAKIR
ECTS credits: 5
GTU credits: 3 ()
Year, Semester: 4/1, Fall and Spring
Level of course: First Cycle (Undergraduate)
Type of course: Elective
Language of instruction: English
Mode of delivery: Face to face , Group study , Lab work
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: The course aims to provide the students with hands-on experience on the computational systems biology and bioinformatics field.
   Learning outcomes Up

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

  1. analyze biological data using computational tools

    Contribution to Program Outcomes

    1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
    2. Combine, Interpret, and analyze different subfields of bioengineering
    3. Work effectively in multi-disciplinary research teams

    Method of assessment

    1. Written exam
    2. Laboratory exercise/exam
  2. identify associated molecular pathways and interactions or retrieve such data from databases for a given gene/protein list

    Contribution to Program Outcomes

    1. Conduct and develop bioengineering applications for relevant sectors such as health and agricultural industry.
    2. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.
    3. Work effectively in multi-disciplinary research teams

    Method of assessment

    1. Written exam
    2. Laboratory exercise/exam
  3. construct mathematical models from biological data

    Contribution to Program Outcomes

    1. Apply mathematical analysis and modeling methods for bioengineering design and production processes.
    2. Work effectively in multi-disciplinary research teams

    Method of assessment

    1. Written exam
    2. Laboratory exercise/exam
   Contents Up
Week 1: Introduction to Biological Data Analysis
Week 2: Gene Expression Omnibus and analysis of transcriptome data
Week 3: Gene Ontology & Pathway enrichment analysis: g:profiler and EnrichR
Week 4: Clustering (heatmap) and Principal Component Analysis using iGeak or ClustVis
Week 5: Analysis of TCGA (The Cancer Genome Atlas) data using TCGABioLinks
Week 6: Analysis of Metabolome Data using MetaboAnalyst
Week 7: Analysis of single-cell RNA sequencing data using ALONA
Week 8: Overview of data based approaches
MidTerm Exam
Week 9: Protein-protein interaction networks and STRING database
Week 10: Using Cytoscape to draw and analyze biological networks
Week 11: Gene-regulatory networks and DREM (Dynamic Regulatory Events Miner)
Week 12: Kinetic modelling using COPASI
Week 13: BioMet Toolbox and genome-scale metabolic models
Week 14: Overview of network and modeling approaches
Week 15*: -
Week 16*: Final Exam
Textbooks and materials:
Recommended readings: 1. Edda Klipp, Ralf Herwig, Axel Kowald, Cristoph Wierling. “Systems Biology: A Textbook”, Wiley-Blackwell, 2016.
2. Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists,Eds. Natasa Przulj, Cambridge University Press, 2019.
  * 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: 8 15
Other in-term studies: 2-13 70
Project: 0
Homework: 0
Quiz: 0
Final exam: 16 15
  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: 4 14
Practice, Recitation: 0 0
Homework: 0 0
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 8 1
Mid-term: 1 1
Personal studies for final exam: 10 1
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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