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Constraint-based modelling of bacterial metabolic networks – where are we in 2011?

Constraint-based modelling of bacterial metabolic networks – where are we in 2011?. What I aim to do in 30 minutes... Give you a brief intro into our system of study. Recap the things we talked about in York in York in 2009. Think about what we could do in Edinburgh in 2011 ?.

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Constraint-based modelling of bacterial metabolic networks – where are we in 2011?

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  1. Constraint-based modelling of bacterial metabolic networks – where are we in 2011? • What I aim to do in 30 minutes... • Give you a brief intro into our system of study. • Recap the things we talked about in York in York in 2009. • Think about what we could do in Edinburgh in 2011?

  2. A bit about our system (see Sandy’s talk on Friday) The pea aphid, Acyrthosiphon pisum. • About 5000 different species • Major crop pests. • Restricted diet of phloem sap. • All contain an obligate primary symbiont.

  3. The γ-proteobacterium Buchnera aphidicola sp. APS is the primary symbiont of the pea aphid • Located in specialised insect cells called bacteriocytes in their body cavity. • They are surrounded by an aphid-derived bacteriocycte membrane. • TEM of bacteriocyte cytoplasm, showing coccoid Buchnera. • The Buchnera are unculturable so not tractable to traditional microbiological methods. • Vertically transmitted to aphid offspring via the ovary. • The function of the symbiosis is nutritional. • Phloem sap poor in essential amino acids [EAAs] (His, Iso, Leu, Lys, Met, Phe, Thr, Trp and Val). • There is experimental evidence that EAAs are provided by the symbiont.

  4. The BuchneraAPS genome • Small - 0.64 Mb • 607 genes (569 protein coding genes) that are a subset of the E. coli K-12 genome. • Almost 90% of the genes have known functions in E. coli K-12. • Specific retention of pathways for biosynthesis of EAAs. • Virtually no transcription regulation.

  5. Carbon-skeleton based manual visualisation of iGT196 196 gene products 240 compounds (39% of iJR904) 263 reactions (27% of iJR904) 35% of reactions for EAA biosynthesis. • Neworkvisualisation • Initially used Cytoscape (picture only really). • Now use CellDesigner to draw model that can export SBML for Cobra (or Surrey FBA). Key Red hexagon – high flux precursor Blue square - EAA Red circle – low flux precursor Blue circle – biomass component Grey triangle – inferred reaction Thomas et al., (2009) BMC Systems Biology 3:24.

  6. Building a whole genome model (of a bacterium) Taken from Durot, Bourguignon and Schachter (2009) FEMS Microbiology Reviews 33:164-190.

  7. Modelling construction Orthology mapping to known model KASS • Assigning transporters • making specific GPRs is difficult • need more experimental data. PRIAMS EFICAz Assignment of E.C. Numbers? Input for a Cyc-type reconstruction Manual curation in CellDesigner • Problems with • using E.C. Numbers • Better ontology? • more coverage • Network visualisation • best tools? • overlay fluxes? • Model exchange • SBML – strict enough? • BioPAX • MIRIAM Value of automated methods?

  8. Getting the model to ‘fire’ • Tools for simple linear programming • COBRA – version 2 (Feb 2011 – look at this on Friday? ) • SurreyFBA • Scrumpy • Sympheny (if rich) • Sanity checking • critical so that don’t get nonsense out • check for production of all biomass components • check major fluxes are in the ‘right’ direction • check network ‘quality’ – FBA aims to minimise total number of fluxes • how valid is it to reverse a reaction? • Biomass reaction • Base it on E. coli or figure it out yourself • Different biomass reactions for different growth conditions • Cofactor constraints Maintenance energy - growth and non growth related - ATP yield in respiration - Redox balancing • What to do when it doesn’t work? • Iterative step-wise model building • Need tools to ‘debug the bug’ • Objective function • Is biomass production always suitable? • Dual objectives?

  9. Getting more from FBA • Understanding the output • solution space of the optimisation • FVA • how to reduce this further? Integration of ‘omics data Transcriptomic data • What does it mean for enzyme fluxes? • How to use it to constrain the model? • - regulatory FBA – Boolean filter • mixed integer linear programming (MILP). Proteomics data Metabolomic data • Dynamic FBA • time dependence • consider kinetics and concentrations • integrated FBA (iFBA) • Constraining internal fluxes • Flux splits (NDH1 versus NDH2) • Dual objective functions • Thermodynamics • Allosteric regulation

  10. Applications of a working model Feist definitions... Metabolic engineering Model-directed discovery Interpretation of phenotypic screens Analysis of network properties Studies of evolutionary processes Using iAF1260 Lycopene L-valine L-threonine MOMA OptKnock Informing on the biological function of metabolism. Orphan enzymes and transporters. Compare KO strains and/or Biolog data to model predictions - Improves model. Evolution of reduced networks Pan genomes. Network analysis – how much value? What are the inputs and output? Buchnera has some high-value waste products. Missing biology?

  11. What kind of systems have we been building or analysing? B. aphidicola Thomas Wood Zucker M. tuberculosis Macdonald Ebenhoeh Price Pérez-Castillo McFadden E. coli K-12 iAF1260 Kierzek Cornish Bowden Brietling De Jong Streptomyces sp. Westerhoff Fell Poolman Velasco A. thaliana S. cerevisiae

  12. What next for an E. coli model? 48 % of all CDS included in iAF1260. Not much more to add! ....but still some reactions not assigned to genes Pan-genome models probably more useful – define the core metabolic network for the species – removes K-12 specific components.

  13. What I’d like to get out of this meeting • How can I usefully use my transcriptomic and proteomic data to add to the FBA (not just with Boolean on and off)? • I want to decouple growth from amino acid overproduction. How can I use dual objective functions? • How can model my transporters more effectively? Both at the level of functional annotation and kinetics. • Where are we with kinetic models? Can we usefully integrate them into our FBA modelling? • What’s new in terms of methods and software that I can use to improve my analysis.

  14. Gavin Thomas Buchnera/aphids FBA AndrejKierzekStreptomycesFBA and kinetic Johnjoe McFadden Mycobacterium Isaac Perez Castillo Kings College London E. coli Metabolic optimisation principle Sergio Bordel Velasco Chalmers, Sweden (Nielson lab) Metabolic models in industrial microbiology . Overlaying transcription. Random sampling of flux distributions . Hans Westerhoff Athel Cornish Bowden Mark Poolman David Fell Thomas Forth Hidde de JongGrenoble, France E. coli metabolic regulatory networks Oliver Ebenhoeh Aberdeen E. coli model building tools Nathan Price Illinois, USA E. coli and TB Probabilistic regulation of metabolism Rainer Breitling Groningen Streptomyces and parasites Metabolic engineering (with Erico Takano) Jeremy Zucker Broad, USA BioPAXBuchnera TB, E-Flux.

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