This course starts by reviewing basic principles of gene regulation and how information is encoded in the huge part of genome - it's so called "non-coding" part. Bioinformatics plays a huge role in exploring how gene regulation works. We will introduce mathematical modelling of cellular pathways and address the interplay between feedback and feed-forward loops of regulation. We will see how a range of modelling techniques can help us to:
- find master regulators
- understand disease mechanisms
- build models for use in precision medicine
- design novel systems for biotechnology
Starting from techniques for the computational prediction of promoters and distant enhancers, we will deal with the analysis of the structure of regulatory regions by identifying binding sites for transcription factors. A complete annotation of a newly sequenced genome has to address the identification of these regions in addition to the classical task of finding genes in terms of coding regions. Regulatory regions determine where, under which conditions and when genes become active. Thus, they are a fundamental part of the definition of the function of a gene. We will have a chance to introduce and acquire *hands-on* practice in next generation sequencing (NGS) techniques for genome-wide epigenetic analyses such as ChIP-seq.
Then we will approach the analysis and modelling of biological systems from several practical angles. Systems biology and modelling are approched here from the interaction and network-based perspectives. We will introduce several pathway databases, such as Reactome, KEGG, TRANSPATH, TRANSFAC, ConsensusPathDB and use these information resources to perform pathway analysis. Next, we will use modelling of biological systems to look at different mathematical modelling strategies, such as Boolean networks and ordinary differential equation systems (ODEs). We will describe computational tools, like Cytoscape and CellDesigner, for the set-up and development of model prototypes and show further tools, such as Copasi, BioUML and PyBioS that can be used for parameter-fitting and sensitivity analysis.
We will follow-on by looking at methods for reconstructing gene regulatory networks from gene expression data. The application of such methods can reveal key nodes in networks as potential biomarkers or drug targets. Dynamic systems modelling will be used to check the consistency of target and biomarker predictions. We will show real examples of application of these methods for identification of disease related biomarkers, drug discovery and personalized medicine.
Special attention will be given to the application of the above mentioned methods in designing novel biological systems, in the growing field of Synthetic Biology.
Participants will learn about several techniques of finding promoters and enhancers and the principles on which these methods are based on. In the hands-on exercises, several analytical tools will be introduced and the results critically evaluated to assess their reliability. The course sessions will consist of lectures that lay out the conceptual framework as needed, and hands-on exercises, which will provide the practical insight on the use of the methods, gradually, in order to produce skills that can be used with a relatively high degree of independence. Participants will learn how to set-up some of the programs, and use publicly available servers for more complex analytical jobs, in an informed fashion, so that they fully understand the output generated and how their quality can be assessed. Participants will also learn the novel principles of organization of gene regulatory regions, which will help them to interpret their results of genomic and transcriptomic studies.
The course will provide sufficient skills for the participants to address problems using open source software and freely accessible data resources. It will also be a chance to use commercially licensed resources such as TRANSFAC or the geneXplain integrated analytic platform for specific purposes.