MOLECULAR BIOLOGY AND GENETICS
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
MBG4059	Computational Methods in Bioinformatics	Spring	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi ELIZABETH HEMOND
Course Objectives:	The goal of this course is to provide an understanding of the fundamental computational methods used in bioinformatics and the set of algorithms that have important applications both inside and outside of the bioinformatics field.

Learning Outcomes

The students who have succeeded in this course;
1. Recognize the fundamental models of computation useful in modeling nucleic acid and protein sequences.
2. Design and implement algorithms useful for analyzing various molecular biology data.
3. Discuss Genetic Algorithm and its applications in bioinformatics.
4. Discuss Greedy Algorithms and its applications in bioinformatics.
5. Discuss Gibbs sampling and its applications in bioinformatics.
6. Recognize Expectation Maximization and its applications in bioinformatics.
7. Recognize Hidden Markov models and its applications in bioinformatics.
8. Define Bayesian networks and its applications in bioinformatics.
9. Define graphs and its applications in bioinformatics.

Course Content

This course will provide a broad and thorough background in computational methods and algorithms that are widely used in bioinformatics applications. Various existing methods will be critically described and the strengths and limitations of each will be discussed.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	A brief introduction to computational complexity and algorithm design techniques
2)	Exact sequence search algorithms
3)	Rabin-Karp algorithm, pattern matching, suffix trees
4)	Elements of dynamic programming, Manhattan tourist problem, k-band algorithm
5)	Approximate string matching, divide and conquer algorithms
6)	Branch and bound search
7)	Genetic Algorithm
8)	Greedy Algorithms
9)	Gibbs sampling
10)	Expectation Maximization
11)	Hidden Markov models
12)	Bayesian networks
13)	Graphs
14)	Review

Sources

Course Notes / Textbooks:	Haftalık ders notları iletilecektir. Weekly course notes will be provided.
References:	An Introduction to Bioinformatics Algorithms (Computational Molecular Biology), Neil Jones and Pavel Pevzner, MIT Press, 2004.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	2	% 10
Project	1	% 15
Midterms	1	% 25
Final	1	% 50
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 35
PERCENTAGE OF FINAL WORK		% 65
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	6	84
Presentations / Seminar	5	4	20
Midterms	1	2	2
Final	1	2	2
Total Workload			150

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Utilize the wealth of information stored in computer databases to answer basic biological questions and solve problems such as diagnosis and treatment of diseases.	3
2)	Acquire an ability to compile and analyze biological information, clearly present and discuss the conclusions, the inferred knowledge and the arguments behind them both in oral and written format.	5
3)	Develop critical, creative and analytical thinking skills.	5
4)	Develop effective communication skills and have competence in scientific speaking, reading and writing abilities in English and Turkish.	3
5)	Gain knowledge of different techniques and methods used in genetics and acquire the relevant laboratory skills.	5
6)	Detect biological problems, learn to make hypothesis and solve the hypothesis by using variety of experimental and observational methods.	5
7)	Gain knowledge of methods for collecting quantitative and qualitative data and obtain the related skills.	3
8)	Conduct research through paying attention to ethics, human values and rights. Pay special attention to confidentiality of information while working with human subjects.	5
9)	Obtain basic concepts used in theory and practices of molecular biology and genetics and establish associations between them.	5
10)	Search and use literature to improve himself/herself and follow recent developments in science and technology.	5
11)	Be aware of the national and international problems in the field and search for solutions.	4