Aim
The aim of the course is to give the students a fundamental understanding of: 1) how high-throughput biological data can be analyzed and 2) how mathematical modeling of biological systems can be used to gain novel biological insight. The students will be presented with a number of examples from analysis of transcriptome data from different studies of yeast, mouse and human tissues. They will further get hands-on experience with analysis of raw data from transcriptome experiments, and will be introduced to how proteome and metabolome data can be analyzed using similar statistical techniques. The students will be presented with methods for reconstruction of metabolic network models, analysis and use of these for simulation of biological functions in living cells. The students will further learn how to perform simulation of kinetic models and analyze reaction networks in terms of flux control. Finally the students will learn about integrated data analysis through a number of different examples. The overall objective is that by passing this course the students should be able to work independently in the field of systems biology.
Learning outcomes
A student that has passed the course is expected to be able to:
- Describe the concepts of genome sequencing and genome analysis
- Perform comparative genome analysis
- Describe the concepts of transcriptome analysis
- Perform normalization of the next generation RNA sequencing data and detect differentially expressed genes, principal component analysis, singular value decomposition, and clustering of transcriptome data
- Describe the concepts of proteome analysis
- Describe the concepts of integrated data analysis using protein interaction maps
- Perform metabolic network reconstruction based on biochemical and genomic information
- Perform simulation based on linear programming of network metabolic models
- Describe the concepts of regulatory networks in living cells
- Describe the principles of kinetic models
- Perform simulation with simple kinetic models
- Perform metabolic control analysis of simple reaction network models
- Describe 2-3 examples of how high-throughput analysis has contributed to biology
Course content
The course gives an in-depth description of how genomics have impacted systems biology and paved the way for high-throughput biological studies. There is special focus on transcriptome analysis using high throughput RNA sequencing and how these data are analyzed using different statistical methods such as SVD and clustering. Concepts of proteomics will be presented and how interaction networks can be used for integrated analysis of high-throughput experimental data. The course will further give insight into how metabolic networks can be reconstructed from biochemical and genomic information. Topological analysis of large genome-scale metabolic models (GEM) will be performed. Simulation of simple metabolic network models will be performed. The course will also introduce kinetic models and demonstrate the use of these for analysis of different types of biological systems, e.g. protein aggregation and cell death and analysis of flux control in reaction series in metabolic networks. Throughout the course there will be given examples from studies of yeast, nutritional studies using mouse models, and from analysis of clinical data.
Examination
Four hour written exam which will take place on January 15, 2015. Reports from exercises have to be approved for passing the course.
General information
The course will be given in English. The course is mandatory for Chalmers students following the Biotechnology MSc program.
Teaching Materials/Literature
In the course there will be used chapters from other textbooks and different research papers will together with slides and exercises be provided to the students during the course.
It is expected that the students read the specified material for passing the exam and the uploaded slides are primarily meant as supportive information. |