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Course Description

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.

References:

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).

References:

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.

References:

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.

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