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Code BIO 310
Term 201702
Title Introduction to Bioinformatics
Faculty Faculty of Engineering and Natural Sciences
Subject Mol.Bio.Genetic&Bioengin.(BIO)
SU Credit 3
ECTS Credit 6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Instructor(s) Oznur Tastan Okan -otastan@sabanciuniv.edu,
Language of Instruction English
Level of Course Undergraduate
Type of Course Click here to view.
Prerequisites
(only for SU students)
--
Mode of Delivery Formal lecture,Interactive lecture,Recitation
Planned Learning Activities Interactive,Learner centered,Communicative,Discussion based learning,Task based learning
Content

Analysis of genes and proteins. Gene finding methods; sequence patterns, Hidden Markov Models. Bioinformatics software on the net. Protein folding problem; Homology modelling and threading algolrithms. Gibbs free energy and contact potentials. Clustering of structures; Structural databases. Structural genomics.

Objective

To supply the students with the foundations in bioinformatics.

Learning Outcome

Understand and appreciate the role of bioinformatics in solving biological problems.
Use established bioinformatics databases and web servers
Demonstrate working proficiency with sequence search and alignment (local, global, pairwise multiple sequence alignment algorithms.) algorithms.
Acquire an elementary understanding of Hidden Markov Models and their applications to problems which involve sequence learning.
Gain a solid perspective of sequence, structure and function relationships in proteins.
Acquire a necessary foundation in machine learning methods for classification and their use to address biological questions.
Gain hands-on experience in the implementation of major clustering algorithms (k-means, hierarchical clustering) and their use in the analysis of biological datasets (e.g., gene expression) and be able to perform clustering analysis.

Have a grasp of gene expression analysis and perform basic expression analysis on gene expression data
Obtain a conceptual knowledge of gene set enrichment analysis and be able to analyze and interpret the results coming from omics data.
Recognize the increasing role of biological networks in analyzing biological systems

Programme Outcomes
 
Common Outcomes For All Programs
1 Understand the world, their country, their society, as well as themselves and have awareness of ethical problems, social rights, values and responsibility to the self and to others. 1
2 Understand different disciplines from natural and social sciences to mathematics and art, and develop interdisciplinary approaches in thinking and practice. 5
3 Think critically, follow innovations and developments in science and technology, demonstrate personal and organizational entrepreneurship and engage in life-long learning in various subjects. 5
4 Communicate effectively in Turkish and English by oral, written, graphical and technological means. 5
5 Take individual and team responsibility, function effectively and respectively as an individual and a member or a leader of a team; and have the skills to work effectively in multi-disciplinary teams. 4
Common Outcomes ForFaculty of Eng. & Natural Sci.
1 Possess sufficient knowledge of mathematics, science and program-specific engineering topics; use theoretical and applied knowledge of these areas in complex engineering problems. 5
2 Identify, define, formulate and solve complex engineering problems; choose and apply suitable analysis and modeling methods for this purpose. 4
3 Develop, choose and use modern techniques and tools that are needed for analysis and solution of complex problems faced in engineering applications; possess knowledge of standards used in engineering applications; use information technologies effectively. 5
4 Ability to design a complex system, process, instrument or a product under realistic constraints and conditions, with the goal of fulfilling specified needs; apply modern design techniques for this purpose. 4
5 Design and conduct experiments, collect data, analyze and interpret the results to investigate complex engineering problems or program-specific research areas. 5
6 Knowledge of business practices such as project management, risk management and change management; awareness on innovation; knowledge of sustainable development. 1
7 Knowledge of impact of engineering solutions in a global, economic, environmental, health and societal context; knowledge of contemporary issues; awareness on legal outcomes of engineering solutions; understanding of professional and ethical responsibility. 5
Computer Science and Engineering Program Outcomes Core Electives
1 Design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs and to solve a computational problem. 5
2 Demonstrate knowledge of discrete mathematics and data structures. 4
3 Demonstrate knowledge of probability and statistics, including applications appropriate to computer science and engineering. 5
Molecular Biology, Genetics and Bioengineering Program Outcomes Core Electives
1 Comprehend key concepts in biology and physiology, with emphasis on molecular genetics, biochemistry and molecular and cell biology as well as advanced mathematics and statistics. 5
2 Develop conceptual background for interfacing of biology with engineering for a professional awareness of contemporary biological research questions and the experimental and theoretical methods used to address them. 5
Materials Science and Nano Engineering Program Outcomes Area Electives
1 Applying fundamental and advanced knowledge of natural sciences as well as engineering principles to develop and design new materials and establish the relation between internal structure and physical properties using experimental, computational and theoretical tools. 1
2 Merging the existing knowledge on physical properties, design limits and fabrication methods in materials selection for a particular application or to resolve material performance related problems. 1
3 Predicting and understanding the behavior of a material under use in a specific environment knowing the internal structure or vice versa. 1
Industrial Engineering Program Outcomes Area Electives
1 Formulate and analyze problems in complex manufacturing and service systems by comprehending and applying the basic tools of industrial engineering such as modeling and optimization, stochastics, statistics. 1
2 Design and develop appropriate analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy. 1
3 Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 3
Electronics Engineering Program Outcomes Area Electives
1 Use mathematics (including derivative and integral calculations, probability and statistics), basic sciences, computer and programming, and electronics engineering knowledge to design and analyze complex electronic circuits, instruments, software and electronics systems with hardware/software. 1
2 Analyze and design communication networks and systems, signal processing algorithms or software using advanced knowledge on differential equations, linear algebra, complex variables and discrete mathematics. 1
Mechatronics Engineering Program Outcomes Area Electives
1 Familiarity with concepts in statistics and optimization, knowledge in basic differential and integral calculus, linear algebra, differential equations, complex variables, multi-variable calculus, as well as physics and computer science, and ability to use this knowledge in modeling, design and analysis of complex dynamical systems containing hardware and software components. 1
2 Ability to work in design, implementation and integration of engineering applications, such as electronic, mechanical, electromechanical, control and computer systems that contain software and hardware components, including sensors, actuators and controllers. 1
Assessment Methods and Criteria
  Percentage (%)
Final 35
Exam 30
Participation 5
Presentation 10
Homework 20
Recommended or Required Reading
Textbook

There is no required text book.

Recommended textbooks:

P. Compeau, P. Pevzner. Bioinformatics Algorithms: An Active Learning Approach. Active
Learning Publishers, 2nd Ed. Vol. 1 and Vol.2, 2015.
Supplementary website: http://bioinformaticsalgorithms.com
- J. Pevsner, Bioinformatics and Functional Genomics, 3rd Edition, 2015.
- A. Lesk, Introduction to Bioinformatics, 4th edition, Oxford University Press, 2014
(3rd edition also OK). ISBN - 978-0199651566.
- N. Jones and P. Pevzner. An Introduction to Bioinformatics Algorithms (Computational Molecular Biology), MIT Press, 2004.

Readings

Published papers.