David Gilbert (Brunel U, London) [intermediate/advanced, 6 hours]
Biomodel Engineering for Systems and Synthetic Biology – from Uniscale to Multiscale
The use of models of biochemical networks is a central component for both Systems and Synthetic Biology. Constructing, analysing and applying these models for prediction (Systems Biology) or design (Synthetic Biology), is a major challenge that can benefit from the application of methods originating in computer science and software engineering. This course gives a general introduction to a general modelling framework and shows how it can be applied to analysing existing biological systems and designing novel systems. A particularly challenging aspect is modeling biological systems which are characterized by important features at multiple spatial and/or temporal scales. We will show how to develop approaches to support the modelling of large and complex biological systems by the use of a novel integrative combination of hierarchy and colour in Petri nets, which promises to be particularly helpful in investigating spatial aspects of biochemical network behaviour, such as communication at the intra- and inter-cellular levels.
More information available here.
- R Breitling, R Donaldson, D Gilbert, M Heiner, BioModel Engineering - From Structure to Behavior, Trans. on Computational Systems Biology XII, Special Issue on Modeling Methodologies, Springer LNCS/LNBI, Vol. 5945, 1-12 (2010).
- D Gilbert: BioModel Engineering,
The MultiScale challenge, Invited talk, 11th Annual Congress of SocBin – Society for Bioinformatics in the Nordic Countries, Helsinki, Finland, May 10, 2011.
- D Gilbert, M Heiner, R Breitling, R Orton, Computational modelling of kinase signalling cascades, in R Seger (Ed.): MAP Kinase Signaling Protocols, 2nd Edition; Methods in Molecular Biology, Vol. 661, Part 4, Chapter 22, 369-384 (2010).
- M Heiner, R Donaldson, D Gilbert, Petri Nets for Systems Biology, in MS Iyengar (Ed.): Symbolic Systems Biology: Theory and Methods, Jones & Bartlett Learning, Chapter 3, 61-97 (2010).
- F Liu, M Heiner, Coloured Petri nets to model and simulate biological systems, Proc. Workshop BioPPN 2010, satellite event of Petri Nets 2010, 70-84 (2010).
- W Marwan, C Rohr, M Heiner, Petri nets in Snoopy: A unifying framework for the graphical display, computational modelling, and simulation of bacterial regulatory networks, in Jv Helden, A Toussaint, D Thieffry (Eds): Methods in Molecular Biology - Bacterial Molecular Networks, Humana Press, Chapter 21.
- C Rohr, W Marwan, M Heiner, Snoopy - a unifying Petri net framework to investigate biomolecular networks, Bioinformatics 26(7): 974-975 (2010).
- Z Wu, Q Gao, D Gilbert, Target driven biochemical network reconstruction based on Petri nets and simulated annealing, Proc. CMSB 2010, ACM digital library, 33-42 (2010).
Joachim Selbig (University of Potsdam & Max Planck Institute of Molecular Plant Physiology), [intermediate/advanced, 6 hours]
Integrative 'Omics' Data and Network Analysis
We will focus on specific aspects of metabolite profile and network analysis: the evaluation of the interactions between metabolites, the uncovering of the connection between metabolism and the phenotype (e.g. as measured by the biomass or morphological properties) and the establishment of relationships between gene expression and metabolite profiles. The latter is to date the most difficult task because the number of observations is often much smaller than the number of investigated genes. For the same reason, the uncovering of relationships between gene expression and physiological properties is difficult (Steinfath et al. 2008, Jozefczuk et al. 2010, Larhlimi et al. 2011, Basler et al. 2012, Girbig et al. 2012). To date, more than 100,000 different metabolites of broad biochemical complexity have been discovered in the plant kingdom and typical non-plant eukaryotic organisms are estimated to contain 4,000 to 20,000 metabolites (Fernie et al. 2004). The high number of metabolites, together with their biochemical complexity and a wide dynamic range of abundances, hampers a comprehensive analysis. The technical and analytical challenges in metabolome analysis have been recently reviewed in detail (Goodacre et al. 2004).
- Basler, G., Grimbs, S., Ebenhöh, O., Selbig, J., Nikoloski, Z. Evolutionary significance of metabolic network properties. Journal of the Royal Society Interface 9:1168-1176, 2012
- Fernie, A.R., Trethewey, R.N., Krotzky, A.J., Willmitzer, L. Metabolite profiling: From diagnostics to systems biology. Nature Reviews Molecular Cell Biology 5:763-769, 2004
- Girbig, D., Grimbs, S., Selbig, J. Systematic analysis of stability patterns in plant primary metabolism. PLoS ONE 7:e34686, 2012
- Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G., Kell, D.B. Metabolomics by numbers: Acquiring and understanding global metabolite data. Trends in Biotechnology 22:245-252, 2004
- Jozefczuk, S., Klie, S., Catchpole, G., Szymanski, J., Cuadros-Inostroza, A., Steinhauser, D., Selbig, J., Willmitzer, L. Metabolomic and transcriptomic stress response of Escherichia coli. Molecular Systems Biology 11:364, 2010
- Larhlimi, A., Blachon, S., Selbig, J., Nikoloski, Z. Robustness of metabolic networks: A review of existing definitions. Biosystems 1006:1-8, 2011
- Steinfath, M., Groth, D., Lisec, J., Selbig, J. Metabolite profile analysis: From raw data to regression and classification. Physiologia Plantarum 132:150-161, 2008
Wing-Kin Sung (National U Singapore), [introductory/intermediate, 6 hours]
Extracting Information from Next Generation Sequencing Data
During the last few years, next generation sequencing (NGS) becomes a popular research tool. People identified more and more NGS applications. At the same time, the throughput of NGS is improving exponentially. It becomes a challenging bioinformatics problem on how to process and analyze NGS data.
This course has three parts. The first part studies methods on processing NGS data. We will discuss techniques to reduce the processing time and how to reduce the NGS datasize. The second and third parts study two specific applications of NGS, namely genome assembly and binding site analysis.
- Li, H. and Durbin, R. Fast and accurate long-read alignment with burrowswheeler transform. Bioinformatics 26(5):589-595, 2010
- Langmead, B., Trapnell, C., Pop, M., and Salzberg, S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10(3):R25, 2009
- Hsi-Yang Fritz, M., Leinonen, R., Cochrane, G., and Birney, E. Efficient storage of high throughput DNA sequencing data using reference-based compression. Genome Research 21(5):734-740, 2011
- Zerbino, D.R. and Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Research 18(5):821-829, 2008
- Simpson, J.T., Wong, K., Jackman, S.D., Schein, J.E., Jones, S.J.M., and Birol, I. ABySS: A Parallel Assembler for Short Read Sequence Data. Genome Research 19:1117-1123, 2009
- Ariyaratne, P.N. and Sung, W.-K. PEAssembler: De Novo Assembler Using Short Paired-End Reads. Bioinformatics 27(2):167-174, 2011
- Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein, B.E., Nusbaum, C., Myers, R.M., Brown, M., Li, W., and Liu, X.S. Model-Based Analysis of ChIP-Seq (MACS). Genome Biology 9:R137, 2008
- Xu, H., Handoko, L., Wei, X., Ye, C., Sheng, J., Wei, C.-L., Lin, F., and Sung, W.-K. A Signal-Noise Model for Significance Analysis of ChIP-seq with Negative Control. Bioinformatics 26(9):1199-1204, 2010
- Zhang, Z., Chang, C.W., Goh, W.L., Sung, W.-K., and Cheung, E. CENTDIST: Discovery of Co-Associated Factors by Motif Distribution. Nucleic Acids Research 39(Suppl 2):W391–W399, 2011
- Zhang, Z., Chang, C.W., Hugo, W., Cheung, E., and Sung, W.-K. Simultaneously Learning DNA Motif along with Its Position and Sequence Rank Preferences through EM Algorithm, in Proceedings of RECOMB 12, LNCS 7262: 355-370, Springer, 2012
Michael Zhang (U Texas Dallas), [intermediate, 6 hours]
From Computational -Omics to Systems Biology
These lectures will introduce typical computational biology problems in genomics and epigenomics. They will describe basic ideas and computational approaches to study transcriptional and post-transcriptional gene regulatory networks in molecular systems biology.
- Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D. and Futcher, B. Comprehensive Identification of Cell Cycle Regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9:3273-3297, 1998
- Zhang, M.Q. Computational prediction of eukaryotic protein-coding genes. Nature Reviews Genetics 3:698-709, 2002
- Zhang, M.Q. Prediction, Annotation and Analysis of Human Promoters. Cold Spring Harbor Symposium Quantitative Biology 68:217-225, Cold Spring Harbor Press, 2003
- Zhang, M.Q. Computational analyses of eukaryotic promoters. Review proceeding of Otto Warburg International Summer School and Workshop on Networks and Regulation, Peter F Arndt & Martine Vingron, eds. BMC Bioinformatics 8(Suppl 6):S2, 2007
- Wang, Z., Zang, C., Rosenfeld, J., Schones, D.E., Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Peng, W., Zhang, M.Q., Zhao, K. Genome-wide correlation analysis of histone acetylation and methylation in human T cells. Nature Genetics 40:897-903, 2008
- Zhang, C., Zhang, Z., Castle, J., Sun, S., Johnson, J., Krainer, A.R., Zhang, M.Q. Defining the regulatory network of the tissue-specific splicing factors Fox-1 and Fox-2. Genes & Development 22:2550-2563. Erratum in: Genes & Development 22:2902, 2008
- Harris, R.A., Wang, T., Coarfa, C., Nagarajan, R.P., Hong, C., Downey, S.L., Johnson, B.E., Fouse, S.D., Delaney, A., Zhao, Y., Olshen, A., Ballinger, T., Zhou, X., Forsberg, K.J., Gu, J., Echipare, L., O'Geen, H., Lister, R., Pelizzola, M., Xi, Y., Epstein, C.B., Bernstein, B.E., Hawkins, R.D., Ren, B., Chung, W.Y., Gu, H., Bock, C., Gnirke, A., Zhang, M.Q., Haussler, D., Ecker, J.R., Li, W., Farnham, P.J., Waterland, R.A., Meissner, A., Marra, M.A., Hirst, M., Milosavljevic, A., Costello, J.F. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nature Biotechnology 28:1097-105, 2010
- Hu, Z., Qian, M., Zhang, M.Q. Novel Markov model of induced pluripotency predicts gene expression changes in reprogramming. BMC Systems Biology 5(Suppl 2):S8, 2011
- Ma, W., Trusina, A., El-Samad, H., Lim, W.A., Tang, C. Defining network topologies that can achieve biochemical adaptation. Cell 138:760-773, 2009
- François, P., Siggia, E.D. Predicting embryonic patterning using mutual entropy fitness and in silico evolution. Development 137:2385-2395, 2010
- de Bono, M., Bargmann, C.I. Natural variation in a neuropeptide Y receptor homolog modifies social behavior and food response in C. elegans. Cell 94:679-689, 1998
- Rankin, C.H. From gene to identified neuron to behavior in Caenorhabditis elegans. Nature Reviews Genetics 3:622:630, 2002