• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Apply the techniques used to collect high-throughput omics data, which consists of the experimental part of systems biology

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
   2. Process and analyze genome-scale biological data using statistical methods and data mining methods.
   3. Develop an awareness of continuous learning in relation with modern technology

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Term paper
2. Apply major statistics methods and data mining methods on the biological data by using a programming platform

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
   2. Process and analyze genome-scale biological data using statistical methods and data mining methods.
   3. Formulate, code, solve and analyze problems on biomolecular interactions by using an engineering viewpoint.
   4. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Term paper
3. List the biological foundations of cellular networks

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
   2. Apply modelling approaches to cellular networks.
   3. Formulate, code, solve and analyze problems on biomolecular interactions by using an engineering viewpoint.

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Term paper

Contents
Week 1:	- Fundamentals of Systems Biology: Basic Concepts (systems biology, molecular biology) - From DNA to Proteins : chromosomes, genes, gene expression, protein synthesis, folding
Week 2:	Cellular Networks: Metabolic reaction networks, Gene-regulatory networks, Signalling networks
Week 3:	Cellular Networks and Graph Theory, Use of Cytoscape Quiz
Week 4:	Experimental techniques of genome sciences I: Genomics, Transcriptomics Homework
Week 5:	Experimental techniques of genome sciences II: Proteomics, Metabolomics, Fluxomics, Interactomics Quiz
Week 6:	Normalization of Raw Omics Data, Methods and Challenges Homework
Week 7:	Statistical Significance Tests I: Parametric and nonparametric significance tests, ROC Curves Quiz
Week 8:	Statistical Significance Tests II: Use of MATLAB for Gene Expression Omnibus database, Gene Ontology (GO) Analysis Homework
Week 9:	- Similarity Tests : Parametric and nonparametric similarity tests - Clustering techniques Quiz
Week 10:	Unsupervised Dimension-reduction techniques I: Principal Component Analysis Basics Homework
Week 11:	Unsupervised Dimension-reduction techniques II: Principal Component Analysis, Independent Component Analysis, and application to genome-scale data Quiz
Week 12:	Supervised Dimension-reduction techniques: Fisher Discriminant Analysis, Support Vector Machines Homework Project Assignment
Week 13:	Cell metabolism and modeling techniques I: Enzyme kinetics, Dynamic metabolic models, Lin-log kinetic approximation Quiz
Week 14:	Cell metabolism and modeling techniques II: Flux Balance Analysis, Flux Variability Analysis Homework
Week 15*:	-
Week 16*:	Project Presentations
Textbooks and materials:	1. Edda Klipp, Wolfram Liebermeister, Cristoph Wierling, Axel Kowald, Hans Lehrach, Ralf Herwig. “Systems Biology”, Wiley-Blackwell, 2009.
Recommended readings:	1. Brian P. Ingalls, "Mathematical Modeling in Systems Biology: An Introduction", The MIT Press, 2013
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:		0
Other in-term studies:		0
Project:	12-16	25
Homework:	4,6,8,10,12,14	40
Quiz:	3, 5, 7, 9, 11, 13	35
Final exam:		0
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	5	14
Practice, Recitation:	0	0
Homework:	5	9
Term project:	8	3
Term project presentation:	1	1
Quiz:	0.5	6
Own study for mid-term exam:	0	0
Mid-term:	0	0
Personal studies for final exam:	0	0
Final exam:	0	0
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)