MATHEMATICS
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
GEN4059	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:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi ELIZABETH HEMOND
Recommended Optional Program Components:	There is none.
Course Objectives:	The goal of this course is to provide an understanding of the fundamental computational methods used in bioinformatics and set of algorithms that have important applications in bioinformatics and also have several other applications outside of bioinformatics.

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 through 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:	Relevant course notes or hand-outs will be supplied.
References:	1)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	% 25
Midterms	1	% 25
Final	1	% 40
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	7	98
Midterms	1	2	2
Final	1	2	2
Total Workload			144

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)	To have a grasp of basic mathematics, applied mathematics and theories and applications in Mathematics
2)	To be able to understand and assess mathematical proofs and construct appropriate proofs of their own and also define and analyze problems and to find solutions based on scientific methods,
3)	To be able to apply mathematics in real life with interdisciplinary approach and to discover their potentials,
4)	To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself,	4
5)	To be able to tell theoretical and technical information easily to both experts in detail and non-experts in basic and comprehensible way,
6)	To be familiar with computer programs used in the fields of mathematics and to be able to use at least one of them effectively at the European Computer Driving Licence Advanced Level,
7)	To be able to behave in accordance with social, scientific and ethical values in each step of the projects involved and to be able to introduce and apply projects in terms of civic engagement,
8)	To be able to evaluate all processes effectively and to have enough awareness about quality management by being conscious and having intellectual background in the universal sense,	4
9)	By having a way of abstract thinking, to be able to connect concrete events and to transfer solutions, to be able to design experiments, collect data, and analyze results by scientific methods and to interfere,
10)	To be able to continue lifelong learning by renewing the knowledge, the abilities and the competencies which have been developed during the program, and being conscious about lifelong learning,
11)	To be able to adapt and transfer the knowledge gained in the areas of mathematics ; such as algebra, analysis, number theory, mathematical logic, geometry and topology to the level of secondary school,
12)	To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively.