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Informatics challenges in systems biology

Informatics challenges in systems biology. Douglas Kell School of Chemistry, University of Manchester, MANCHESTER M60 1QD, U.K. dbk@man.ac.uk http://dbk.ch.umist.ac.uk/ http://www.mib.ac.uk. “Biology is an informational science” Lee Hood, Director, Institute for Systems Biology, Seattle.

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Informatics challenges in systems biology

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  1. Informatics challenges in systems biology Douglas Kell School of Chemistry, University of Manchester, MANCHESTER M60 1QD, U.K.dbk@man.ac.uk http://dbk.ch.umist.ac.uk/ http://www.mib.ac.uk

  2. “Biology is an informational science”Lee Hood, Director, Institute for Systems Biology, Seattle.

  3. HYPOTHESIS/ ANALYSIS/ DEDUCTION SYNTHESIS/ INDUCTION dB The cycle of knowledge ‘KNOWLEDGE/ RULES OBSERVATIONS / DATA

  4. Timeline • Pre-genomics  Post-genomics/ functional genomics  Systems Biology • Organismal  Cellular  Molecular  Systems

  5. Manchester Centre for Integrative Systems Biology

  6. The Manchester Interdisciplinary Biocentre (2005-) Postdoc and studentship positions available MIB

  7. The MIB model - a major international research centre - a partner to the disciplines - parallel to schools/faculties - one of a number of expected IRCs Chemistry Chem. Eng. Maths/ Computing Biology MIB Medicine/ Pharmacy http://www.mib.ac.uk Physics Engineering Mat. Sci. MIB

  8. Summary of NF-kB – 3 steps • NF-B is a nuclear transcription factor and is held inactive in the cytoplasm of non-stimulated cell by three IB isoforms • During cell stimulation, the IKK complex is activated, leading to phosphorylation and ubiquitination (and removal) of the IB proteins. • Free NF-B translocates to the Nucleus, activating genes including IB. IB& - are synthesised at a steady rate, allowing for complex temporal control of NF-B activation involving negative feedback (2) (1) (3)

  9. The model has 64 unidirectional reactions & 26 variables Violet red circles = IB-NF-B cytoplasmic reactions; Blue Arrows and circles = Nuclear Transport; Magenta Arrows and Pink circles = IB mRNA synthesis (including transcription, translation and degradation); Black Arrows and white circles = IB-NF-B nuclear reactions; Light Green Arrows and circles = IB Phosphorylation and Degradation reactions; Brown Arrows and brown circles = Bimolecular IKK- IB and tri-molecular IKK- IB-NF-B; Yellow Arrows and circles = IKK slow adaptation coefficient

  10. Main areas of informatics challenges • Data and model storage and standards – including new kinds of data • Forward modelling • Inverse modelling • Data mining

  11. Data and model storage and standards – including new kinds of data • Existing data standards/models and dB are not vertically integrated; MaxD (transcriptome), PEDRo (proteome), ArMet (metabolome) • All relate to variables, not parameters (which are more important for SB) • Models – SBML (http://sbml.org) • Create, edit, visualise, disseminate, analyse, compare with each other and with experimental data, mine • Potentially large and novel datasets e.g. of cellular images

  12. “Real” oscillations of GFP-NFkBn observed microscopically with labelled IkBa and NFkB Nelson et al 2004 Science 306, 704-8 NB we measure individual cells, not ensembles

  13. The timing and amount of oscillations depend strongly on the type of stimulation (various amounts and times of TNFa, different individual cells) Nelson et al 2004

  14. Forward modelling Running a forward ODE model is comparatively easy e.g. with Gepasi (Mendes & Kell, Bioinformatics (1998) 14, 869-883). PDEs are much harder computationally

  15. Gepasi is free to download

  16. After pre-equilibration for 2000s, IKK is ‘added’ at 0.1 mM IKK NFkBn

  17. Largest sensitivity values for each reaction (D100%)(Ihekwaba et al., Systems Biology 1, 93-103 (2004)

  18. Mathematical challenge is the Inverse Problem – work out the system that gave THIS time series

  19. …and all 3 (or 23) together

  20. Data Mining

  21. Serum: > 1000 metabolite peaks

  22. Disease diagnosis by GC-tof- MS – look for metabolites that discriminate ‘cases’ from ‘controls’ IF 403 < 0.035 AND (IF 415 < 0.001 OR IF 427 > 0.001) THEN disease IF 403 > 0.015 AND (IF 415 <0.012) THEN disease

  23. Conclusion • (Systems) Biology is an informational science • At least 4 main areas where informatics is central • Many new and interesting challenges for integrating data and models to achieve a true systems understanding • Huge potential scientific (and indeed commercial) pay-offs

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