• 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. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Acquire knowledge for research methods which are required to develop novel application methods
   3. Apply mathematical analysis and modeling methods for bioengineering design and production processes.

   Method of assessment

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

   Contribution to Program Outcomes

   1. Convert biological, chemical, physical and mathematical principles into novel applications for the benefit of society,
   2. Apply mathematical analysis and modeling methods for bioengineering design and production processes.

   Method of assessment

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

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Convert biological, chemical, physical and mathematical principles into novel applications for the benefit of society,

   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 1
Week 4:	Experimental techniques of genome sciences I: Genomics, Transcriptomics Homework 1
Week 5:	Experimental techniques of genome sciences II: Proteomics, Metabolomics, Fluxomics, Interactomics
Week 6:	Normalization of Raw Omics Data, Methods and Challenges Homework 2 Quiz 2
Week 7:	Statistical Significance Tests I: Parametric and nonparametric significance tests, ROC Curves
Week 8:	Statistical Significance Tests II: Use of R for Gene Expression Omnibus database, Gene Ontology (GO) Analysis Homework 3
Week 9:	- Similarity Tests : Parametric and nonparametric similarity tests - Clustering techniques Quiz 3
Week 10:	Unsupervised Dimension-reduction techniques I: Principal Component Analysis Basics Homework 4
Week 11:	Unsupervised Dimension-reduction techniques II: Principal Component Analysis, Independent Component Analysis, and application to genome-scale data
Week 12:	Supervised Dimension-reduction techniques: Fisher Discriminant Analysis, Support Vector Machines Homework 5 Assignment of the Course Project Quiz 4
Week 13:	Transcriptome data and protein-protein interaction networks: Integrative approaches
Week 14:	Transcriptome data and protein-protein interaction networks: subnetwork discovery approaches Homework 6 MidTerm
Week 15*:	-
Week 16*:	Project presentations
Textbooks and materials:	Edda Klipp, Wolfram Liebermeister, Cristoph Wierling, Axel Kowald, Hans Lehrach, Ralf Herwig. “Systems Biology”, Wiley-Blackwell, 2nd edition, 2016.
Recommended readings:	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:	14	20
Other in-term studies:		0
Project:	12-16	20
Homework:	4,6,8,10,12,14	35
Quiz:	3,5,7,9,11,13	25
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:	2	14
Practice, Recitation:	0	0
Homework:	6	6
Term project:	10	1
Term project presentation:	1	1
Quiz:	1	4
Own study for mid-term exam:	8	1
Mid-term:	1	1
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)