Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BENG543COMPUTATIONAL BIOLOGY3+0+037,513.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program BIOENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course 1. Defining how to perform sequence and structure analysis.
2. Describing why computational biology is an essential area in life sciences.
Course Content This course will provide up-to-date information about the theories and methodologies in computational biology. Upon completion of the course, the students should be able to perform various computational analyses on biological data. The topics include basic structure and sequence analysis, omics, methods of sequence comparison and multiple alignment, distances of the trees and sequences.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator Instructor Dr. Ömer Faruk Bay omerfaruk.bay@agu.edu.tr
Name of Lecturers Instructor Dr. ÖMER FARUK BAY
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources

Course Category
Mathematics and Basic Sciences %30
Engineering %30
Engineering Design %0
Social Sciences %0
Education %0
Science %20
Health %20
Field %0

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Ödev 1 % 50
Final examination 1 % 50
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
F2F Dersi 14 3 42
Ev Ödevi 1 3 3
Sınıf İçi Aktivitesi 12 2 24
Teslim İçin Hazırlık 6 6 36
Soru Çözümü 12 2 24
Yazılım Deneyimi 14 2 28
Teslim 1 3 3
Yüz Yüze Ders 14 3 42
Final Sınavı 1 2 2
Total Work Load   Number of ECTS Credits 7,5 204

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Identify basics of computational biology
2 Apply the learned methodologies to real problems.
3 Use various tools for sequence analysis.
4 Describe how to find solutions for computational omics problems.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Course Schedule and Login to Cocalc Platform.
2 Unix coding introduction.
3 Scripting with shell, data manipulation
4 Introduction to Python Programming
5 Functions, modules and object-oriented programming concepts
6 Introduction to data structures and data manipulation
7 Basic data visualisation and working with files
8 Fall Breaks
9 Active learning week
10 Data visualisation, working with files and data manipulation
11 Introduction to metabolic networks
12 Building a small metabolic network
13 Importing and analysing an existing metabolic model
14 Simulations and analysis
15 Recap

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 5 2 4 3 3 4 4 1 5 4 1 4
C2 5 2 4 3 3 4 4 1 5 4 1 4
C3 5 2 4 3 3 4 4 1 5 4 1 4
C4 5 2 4 3 3 4 4 1 5 4 1 4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77947&lang=en
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