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

Learning outcomes

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

1. Download transcriptome datasets from related online databases, and analyze them via basic statistical tests

   Contribution to Program Outcomes

   1. Identify structure-function relationships in cells and organisms
   2. Develop an awareness of continuous learning in relation with modern technology

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Grasp (i) the techniques used to collect high-throughput omics data, which consists of the experimental part of systems biology, (ii) the details of major cellular network types

   Contribution to Program Outcomes

   1. Obtain basic knowledge of Biology
   2. Develop an awareness of continuous learning in relation with modern technology

   Method of assessment

   1. Written exam
   2. Homework assignment
3. 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. Identify structure-function relationships in cells and organisms
   2. Work effectively in multi-disciplinary research teams
   3. Develop an awareness of continuous learning in relation with modern technology
   4. Find out new methods to improve his/her knowledge

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Term paper
4. Analyze cellular metabolism by integratively using stoichiometry-based models with genome-wide high-throughput omics data

   Contribution to Program Outcomes

   1. Develop an understanding of matter and energy in organisms
   2. Identify structure-function relationships in cells and organisms
   3. Define the relationship among life forms and their environments and ecosystems
   4. Develop an awareness of continuous learning in relation with modern technology

   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 Homework 1
Week 2:	Cellular Networks: Metabolic reaction networks, Gene-regulatory networks, Signalling networks Quizz I
Week 3:	Cellular Networks and Graph Theory Homework 2
Week 4:	Experimental techniques of genome sciences: DNA chips for transcriptomics, Proteomics, Metabolomics, Fluxomics, Interactomics Homework 3, Quizz 2
Week 5:	Programming and Statistics in MATLAB: application to biological data Homework 4
Week 6:	Statistical Significance Tests: Parametric and nonparametric significance tests Quizz 3
Week 7:	Statistical Significance Tests: Use of MATLAB for Gene Expression Omnibus database, Gene Ontology (GO) Analysis Homework 5
Week 8:	Similarity Tests : Parametric and nonparametric similarity tests, Clustering techniques Homework 6, Quizz 4
Week 9:	Unsupervised Dimension-reduction techniques: Principal Component Analysis, Independent Component Analysis Homework 7
Week 10:	Supervised Dimension-reduction techniques: Fisher Discriminant Analysis, Support Vector Machines Homework 8, Quizz 5
Week 11:	Cell metabolism and modeling techniques: Flux Balance Analysis (FBA) to calculate metabolic fluxes, Basics of Optimization and Linear Programming Homework 9
Week 12:	Cell metabolism and modeling techniques: FBA, Flux Variability Analysis (FVA), Genome-scale metabolic models, Quadratic programming (Minimization of Metabolic Adjustment, MOMA) Homework 10, Quizz 6
Week 13:	Application examples of Systems Biology on Cell Metabolism
Week 14:	Application examples of Systems Biology on Cellular Signalling
Week 15*:	General Overview
Week 16*:	Term Project Presentations
Textbooks and materials:	1.Edda Klipp, Ralf Herwig, Axel Kowald, Cristoph Wierling. “Systems Biology in Practice: Concepts, Implementation and Application”, John Wiley and Sons, New York, 2005. 2.Bernhard O. Palsson. “Systems Biology: Properties of Reconstructed Networks” Cambridge University Press, 2006
Recommended readings:	1.Gregory N. Stephanopoulos, Aristos A. Aristidou, Jens Nielsen. “Metabolic Engineering: Principles and Methodologies”, Academic Press, San Diego, 1998 2. P. Venkataraman, “Applied Optimization with MATLAB Programming”, John Wiley and Sons, New York, 2009 3. Pierre Baldi, Soren Brunak. “Bioinformatics: The Machine Learning Approach”, The MIT Press, Cambridge, 2001 4. Stormy Attaway, "MATLAB- A practical introduction to programming and problem solving", Elsevier B-H, USA, 2012
	* 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:	11	25
Homework:	1,3,4,5,7,8,9,10,11,12	40
Quiz:	2,4,6,8,10,12	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:	2	14
Practice, Recitation:	0	0
Homework:	2	6
Term project:	5	3
Term project presentation:	2	1
Quiz:	0.2	10
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)