Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
SYSTEMS BIOLOGY AND BIOINFORMATICS 23564019 2 + 0 2.0 7.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals The aim of the systems biology and bioinformatics program is to learn and implement novel methods that can be generalized to a defined class of problems—to focus on the acquisition, representation, retrieval, and analysis of biological data and knowledge.
Course Content The description of information theory and bioinfirmatics, Information tools, to learn the computer technology- courses of software and hardware, to learn the network structures and functions, the usage of data prosessing in Bioinformatics, to identify the online bioinformatic sites, the bioinformatic practises in molecular biology and genetic, to identify the online bibliographic databases, to use the bioinformatic application tools in online or ready softwares, to know the bioinformatic applications in the field of proteomics.
Learning Outcomes 1) Makes use of bioinformatic tools on the internet
2) Reviews and produces information by bioinformatic tools and related databases
3) Contributes to the development of novel research and diagnostics tools in oncology by bioinformatics

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week System biology and bioinformatics Lecture

 Presentation (Including Preparation Time)
2. Week The concepts of genome, chromosome, gene, locus, variation and allele Lecture

 Presentation (Including Preparation Time)
3. Week Curation of the genome and the transcriptome data Lecture

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
4. Week Research and diagnosis methods and techniques that are used in genomics Lecture

 Presentation (Including Preparation Time)
5. Week Evaluation methods used to process the genomic data Lecture

 Homework Presentation (Including Preparation Time)
6. Week Relating the tools and methods of bioinformatics during tumor development and progress to cellular pathways, genes and their products involved Lecture

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
7. Week Midterm exam Problem Solving
8. Week Relating the tools and methods of bioinformatics during tumor development and progress to gene expression pathways Lecture

 Homework Presentation (Including Preparation Time)
9. Week Examples of application of online genome and variation databases for the research of cancer etiology Lecture

 Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
10. Week Application of online genome and variation databases in oncology for the development of diagnostic methods Lecture

 Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
11. Week Analysis of polymorphic structures like SNPs, CNVs and indels with bioinformatic tools as susceptibility loci in cancer Lecture

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
12. Week Searching driver and passenger mutations in genome and variation databases. Lecture

 Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
13. Week Analysis of epigenetic factors during tumor development and progress with bioinformatic tools Lecture

 Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
14. Week Sample applications Lecture

 Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
15. Week General evaluation Lecture

 Homework Presentation (Including Preparation Time)
16. Week Final Exam


Sources Used in This Course
Recommended Sources
Cancer system biology: https://www.nature.com/articles/nrclinonc.2014.6
http://atlasgeneticsoncology.org/
http://www.ncbi.nlm.nih.gov/omim
http://www.nlm.nih.gov/
https://www.encodeproject.org
https://www.ensembl.org/index.html
Kanser genom atlası:https://cancergenome.nih.gov
Supratim Choudhuri Michael Kotewicz-Bioinformatics for beginners genes, genomes, molecular evolution, databases and analytical tools-Elsevier AP, Academic Press (2014)

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 2
Work Hour outside Classroom (Preparation, strengthening) 14 5
Homework 2 5
Presentation (Including Preparation Time) 1 3
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 12 6
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 5
Final Exam 1 2
Time to prepare for Final Exam 1 5
1 1
1 1
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • ECTS credits and course workload