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 biological networks can be reconstructed based on biological information. The students will be presented with a number of examples from analysis of transcriptome data from different studies of yeast, filamentous fungi, 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 integrated data analysis. The overall objective is that by passing this course the students should be able to work independently in the field of systems biology of metabolism.
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 DNA array data, and perform student t-test, ANNOVA, single value decomposition, principal component analysis 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 topological analysis of metabolic networks
Perform simulation of metabolic models
Describe the concepts of regulatory networks in living cells
Describe the principles of dynamic models
Perform simulation with simple kinetic models
Describe 2-3 examples of how high-throughput analysis has contributed to biology
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 and how these data are analyzed using different statistical methods such as t-test, ANNOVA, SVD, PCA 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 detailed insight into how metabolic networks can be reconstructed from biochemical and genomic information. Topological analysis of large genome-scale metabolic models (GSMM) will be performed. Simulation of GSMM will be performed and the use of these for functional studies of living cells will be presented. Further use of GSMM for integrated data analysis will be presented. Throughout the course there will be given examples from studies of yeast and filamentous fungi, nutritional studies using mouse models, and from analysis of clinical data.
Four hour written, open book exam, which will take place on December 16, 2010. Reports from exercises have to be approved for passing the course.
The course will be given in English. The course is mandatory for Chalmers students following the Biotechnology MSc program. Part of the course will be co-run with the course BIO421/BIN882 Generation and Organisation of Data for Systems Biology at University of Gothenburg.
The following two text books will be used in the course:
A. Malcolm Campbell & L. J. Heyer (2007) Discovering Genomics, Proteomics, & Bioinformatics, 2. Edition, Pearson Benjamin Cummings, San Fransisco
B. O. Palsson (2006) Systems Biology. Properties of Reconstructed Networks, Cambridge University Press, New York
Besides these two text books there will be used different research papers and exercises that will be provided to the students during the course.
Professor Jens Nielsen
Systems Biology, Department of Chemical and Biological Engineering, Chalmers
Tel: +46 31 772 3804