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? 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. 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. The Buchnera APS 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. 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. Nework visualisation • 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 Red circle – low flux precursor Grey triangle – inferred reaction Thomas et al., (2009) BMC Systems Biology 3:24. Blue square - EAA Blue circle – biomass component Building a whole genome model (of a bacterium) Taken from Durot, Bourguignon and Schachter (2009) FEMS Microbiology Reviews 33:164-190. Modelling construction Orthology mapping to known model KASS PRIAMS Assigning transporters - making specific GPRs is difficult - need more experimental data. EFICAz Assignment of E.C. Numbers? Problems with using E.C. Numbers -Better ontology? - more coverage Input for a Cyc-type reconstruction Manual curation in CellDesigner Value of automated methods? Network visualisation - best tools? - overlay fluxes? Model exchange - SBML – strict enough? - BioPAX - MIRIAM Getting the model to ‘fire’ Tools for simple linear programming - COBRA – version 2 (Feb 2011 – look at this on Friday? ) - SurreyFBA - Scrumpy Sanity checking - Sympheny (if rich) - critical so that don’t get nonsense out - check for production of all biomass Biomass reaction components - Base it on E. coli or figure it out yourself - check major fluxes are in the ‘right’ - Different biomass reactions for different direction growth conditions - check network ‘quality’ – FBA aims to - Cofactor constraints minimise total number of fluxes - how valid is it to reverse a reaction? 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 Objective function - Need tools to ‘debug the bug’ - Is biomass production always suitable? - Dual objectives? 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? Proteomics data How to use it to constrain the model? - regulatory FBA – Boolean filter - mixed integer linear programming (MILP). Metabolomic data Constraining internal fluxes - Flux splits (NDH1 versus NDH2) - Dual objective functions - Thermodynamics - Allosteric regulation Dynamic FBA - time dependence - consider kinetics and concentrations - integrated FBA (iFBA) Applications of a working model Feist definitions... Metabolic engineering Using iAF1260 Lycopene L-valine L-threonine MOMA OptKnock Model-directed discovery Informing on the biological function of metabolism. Orphan enzymes and transporters. Interpretation of phenotypic screens Analysis of network properties Compare KO strains and/or Biolog data to model predictions Network analysis – how much value? Evolution of reduced networks What are the inputs and output? Pan genomes. - Improves model. Buchnera has some highvalue waste products. Missing biology? Studies of evolutionary processes What kind of systems have we been building or analysing? M. tuberculosis B. aphidicola Thomas Zucker Wood Macdonald Price McFadden Ebenhoeh Pérez-Castillo Kierzek E. coli K-12 iAF1260 Brietling Cornish Bowden De Jong Streptomyces sp. Fell Poolman Westerhoff Velasco A. thaliana S. cerevisiae 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. 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. Gavin Thomas Buchnera/aphids FBA Andrej Kierzek Johnjoe McFadden Streptomyces Mycobacterium FBA and kinetic 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 Jong Oliver Ebenhoeh Nathan Price metabolism Grenoble, France Aberdeen Illinois, USA E. coli metabolic regulatory networks E. coli model building tools E. coli and TB Probabilistic regulation of Rainer Breitling Groningen engineering (with Erico Takano) Streptomyces and parasites Jeremy Zucker BioPAX Buchnera TB, E-Flux. Broad, USA Metabolic