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? 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?

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

5. 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.

6. 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.

7. Carbon-skeleton based manual visualisation of iGT196 Key Red hexagon – high flux precursorBlue square - EAA Red circle – low flux precursor Blue circle – biomass component Grey triangle – inferred reaction Thomas et al., (2009) BMC Systems Biology 3:24. 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).

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

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

10. Getting the model to ‘fire’ 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 Objective function - Is biomass production always suitable? - Dual objectives? What to do when it doesn’t work? - Iterative step-wise model building - Need tools to ‘debug the bug’ 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? Tools for simple linear programming - COBRA – version 2 (Feb 2011 – look at this on Friday? ) - SurreyFBA - Scrumpy - Sympheny (if rich)

11. Getting more from FBA Transcriptomic data Proteomics data Metabolomic 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). Constraining internal fluxes - Flux splits (NDH1 versus NDH2) - Dual objective functions - Thermodynamics - Allosteric regulation Understanding the output - solution space of the optimisation - FVA - how to reduce this further? Integration of ‘omics data Dynamic FBA - time dependence - consider kinetics and concentrations - integrated FBA (iFBA)

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

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

14. 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.

15. 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.

17. Gavin ThomasBuchnera/aphidsFBA Andrej KierzekStreptomycesFBA and kinetic Johnjoe McFaddenMycobacterium Isaac Perez CastilloKings College LondonE. coliMetabolic 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, FranceE. colimetabolic regulatory networks Oliver EbenhoehAberdeenE. coli model building tools Nathan PriceIllinois, USAE. coli and TB Probabilistic regulation of metabolism Rainer BreitlingGroningenStreptomyces and parasitesMetabolic engineering (with Erico Takano) Jeremy ZuckerBroad, USABioPAX Buchnera TB, E-Flux.