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The Systems Biology of metabolism and computational challenges

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The Systems Biology of metabolism and computational challenges

  1. 1. The Systems Biology of metabolism: Computational challenges <ul><li>The Manchester Centre for </li></ul><ul><li>Integrative Systems Biology </li></ul>Vangelis Simeonidis Universität für Bodenkultur Wien 6 May 2010
  2. 2. Systems Biology <ul><li>Molecular biology, genetics and bioinformatics have made great advances </li></ul><ul><li>Systems biology studies the interactions of the system, not just the components </li></ul><ul><li>The interactions are what give the system its immerging properties; the sum is greater than its parts </li></ul>
  3. 3. Goals <ul><li>Assemble a comprehensive set of strategies and methodologies for Systems Biology, in this case specifically metabolism </li></ul><ul><li>Test and validate strategies and methodologies in the baker’s yeast S. Cerevisiae </li></ul><ul><li>To implement the methodologies in various other systems in local, national and international collaborations </li></ul>
  4. 4. Workflow
  5. 5. Keeping data alive
  6. 6. Experimental Workflow PROTEIN PURIFICATION ENZYME KINETIC ASSAYS EFFECTOR MAP / ENZYME REGULATION ASSAYS CONTINUOUS STEADY- STATE CULTURES (TURBIDOSTAT) ABSOLUTE PROTEIN QUANTITATION ABSOLUTE METABOLITE QUANTITATION EXPERIMENT DESIGN (FBA, GROWTH RATE, MEDIA COMPONENTS, MODEL VALIDATION) PRIDE MEMO TEXT MINING SABIO-RK
  7. 7. Cell growth Protein purification Enzyme kinetics Quantitative proteomics Quantitative metabolomics SBML model Parameters (K M , K cat ) Variables (metabolite and protein concentrations) Overview
  8. 8. Challenge: Reliable reaction list
  9. 9. Consensus yeast model Nature Biotechnology 26, 1155 - 1160 (2008) A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology Markus J Herrgård, Neil Swainston et al. <ul><li>SBML – widely supported, many tools </li></ul><ul><li>Use MIRIAM standards </li></ul><ul><li>http://www.ebi.ac.uk/miriam/ </li></ul><ul><li>- Unambiguous, unique identifiers with CV terms and external database identifies (Uniprot, CHEBI) </li></ul>
  10. 10. Consensus yeast model <ul><li>http://www.comp-sys-bio.org/yeastnet/ </li></ul>iIN800 (Nielsen) Yeast 1.0 Yeast 3.0 Change Reactions 1557 1477 2216 +50% Metabolites 814 820 930 +13% Enzymes 708 832 938 +13% Compartments 5 15 16 +7%
  11. 11. The main representation for biological models is SBML
  12. 12. Challenge: Which enzymes?
  13. 13. General strategy <ul><li>Brute-force approach: </li></ul><ul><li>Study all enzymes </li></ul><ul><li>Create complete map </li></ul><ul><li>But: </li></ul><ul><li>Gaps in the network </li></ul><ul><li>Might end up with >90% of enzymes, but <10% of flux </li></ul>
  14. 14. General strategy <ul><li>Flux-centric approach: </li></ul><ul><li>Identify where the carbon flux goes </li></ul><ul><li>Prioritize pathways by ranking higher ones that carry the most flux </li></ul><ul><li>Will end up with 20-30% of enzymes, but >90% of flux </li></ul>
  15. 15. A E B C D G H I K L M N O F L 1 ≤ v 1 ≤ U 1 L 2 ≤ v 2 ≤ U 2 … .............. L n ≤ v n ≤ U n max M S . v = 0 Chasing the flux: Flux Balance Analysis
  16. 16. 1 1 1 1 1 1 1 1 X Y 1 1 1 How does FBA work? A E B C D G H I K L M 1
  17. 17. 1 1 1 1 X Y 1 1 1 How does FBA work? A E B C D L N 1 XX YY ZZ WW UU
  18. 18. Genome scale network
  19. 19. Exometabolome measurements Experiment 1 2 3 AVE C% of input flux for FBA Carbon input flux as glucose (mmoles/hr/g DW) 67.5 42.1 74.1 61.2 100.00 1.0000 qBiomass - C4H7O2N (mmoles carbon/hr/g DW) 10.3 9.4 8.7 9.5 15.46 0.0234 qCO2 (offgas) (mmoles carbon/hr/g DW) 16.0 12.3 20.9 16.4 26.78 1.6070 qEthanol (exometabolome, mmoles carbon/hr/g DW) 29.5 16.9 34.8 27.1 44.20 1.3261 qAcetate (exometabolome, mmoles carbon/hr/g DW) 0.5 0.4 0.5 0.5 0.76 0.0229 qAcetaldehyde (exometabol., mmoles carbon/hr/g DW) 0.2 0.1 0.2 0.2 0.27 0.0082 qGlycerol (exometabolome, mmoles carbon/hr/g DW) 5.9 6.2 8.1 6.7 11.00 0.2199 qTrehalose (exometabolome, mmoles carbon/hr/g DW) 0.2 0.2 0.2 0.2 0.33 0.0016
  20. 20. Results Glycolysis Pyruvate metabolism Glycine metabolism Serine metabolism Alanine metabolism Aspartate metabolism Pentose phosphate TCA cycle Fructose Manose Oxidative Phosphorylation Fatty acid biosynthesis Trehalose cycle Purine biosynthesis Phosphorylation biosynthesis biomass production 15.55% D-Glucose exchange 100.00% Glucose-6-phosphate isomerase 100.00% Glucokinase 100.00% glucose-6-phosphate isomerase 95.67% fructose bisphosphate aldolase 93.09% phosphofructokinase 93.09% glyceraldehyde-3-phosphate dehydrogenase 82.32% phosphoglycerate kinase 82.32% enolase 71.93% phosphoglycerate mutase 71.93% pyruvate kinase 71.30% pyruvate decarboxylase 67.39% alcohol dehydrogenase 41.99% Ethanol exchange 41.99% triose phosphate isomerase 36.07% CO2 exchange 32.16% glycine cleavage complex lipoamide 30.70%   .........................................................   ............   .........................................................   ............
  21. 21. Elementary flux mode analysis the smallest sub-networks that allow a metabolic reconstruction network to function (in steady state)
  22. 22. Pathways from EFM results
  23. 23. What pathways? GLC DHAP G6P F6P FDP G3P 13PG 3PG 2PG PYR PEP ACALD CO2 ETOH AKG 3PHP PSEP GLU SER GLY CO2 GLYC3P GLYC OAA ASP G1P UDPG 13BDGLCN AC MAN6P MAN1P GDPMANN DOLMANP MANNAN 14GLUN GLYCOGEN
  24. 24. Challenge: Modelling system behaviour
  25. 25. Flux Balance Analysis (FBA)  easy to solve  only stoichiometry required  no insight into substrate concentrations Stoichiometric Matrix : signifies if and how a metabolite takes part in a certain reaction A B … G r 1 r 2 …. r n a 1 b 1 … . g 1 a 2 b 2 … . g 2 … . … . … . … . a n b n … . g n Flux Vector : Each component represents the flux through the corresponding reaction v 1 v 2 … . v n v dA/dt dB/dt … . dG/dt = Steady State condition 0 0 … . 0 = R n space R m Space Ker( S ) null vector 0 L 1 ≤ v 1 ≤ U 1 L 2 ≤ v 2 ≤ U 2 … .............. L n ≤ v n ≤ U n
  26. 26. Kinetic modelling Teusink et al. glycolysis model (Eur J Biochem 267:5313, 2000) aims to characterize fully the mechanics of each enzymatic reaction
  27. 27. Teusink et al. glycolysis model (Eur J Biochem 267:5313, 2000) aims to characterize fully the mechanics of each enzymatic reaction  full detail  costly; time-consuming  unknown mechanics Kinetic modelling
  28. 28. linlog kinetics v : reaction rate x : internal metabolite concentration ε x : elasticity v 0 , x 0 : reference state good approximation of MM kinetics
  29. 29. Goodness of fit <ul><li>in most cases linlog is very good approximation </li></ul><ul><li>even when not so good, the approximation remains valid for at least a region around the reference point </li></ul>linlog: Teusink: o
  30. 30. How to estimate without experimental data? FBA solution Stoichiometric considerations
  31. 31. Metabolite concentrations with changes in ethanol linlog (with correct elasticities): Teusink: o linlog (with estimated elasticities): <ul><li>Good fit in most cases </li></ul><ul><li>Can easily incorporate experimental information to improve the fit </li></ul>
  32. 32. Scaling up to genome-scale <ul><li>Reactions exerting most control over biomass production </li></ul>Reaction C J glucosamine-6-phosphate deaminase 0.532 glutamine-fructose-6-phosphate transaminase 0.441 glutamine synthetase 0.358 H2O transport via diffusion 0.212 inorganic diphosphatase -0.193 glycerol-3-phosphate dehydrogenase (NAD) 0.189 L-asparaginase -0.146 adenylate kinase (GTP) -0.142 glucose transport (uniport) -0.132 ribonucleoside-triphosphate reductase (UTP) -0.104
  33. 33. Challenge: Constraint-based, genome-scale modelling
  34. 34. Constraint-based modelling Teusink et al. glycolysis model (Eur J Biochem 267:5313, 2000) based on stoichiometry and steady-state assumption maximise
  35. 35. Flux Balance Analysis (FBA) Stoichiometric Matrix : signifies if and how a metabolite takes part in a certain reaction A B … G r 1 r 2 …. r n a 1 b 1 … . g 1 a 2 b 2 … . g 2 … . … . … . … . a n b n … . g n Flux Vector : Each component represents the flux through the corresponding reaction v 1 v 2 … . v n v dA/dt dB/dt … . dG/dt = Steady State condition 0 0 … . 0 = L 1 ≤ v 1 ≤ U 1 L 2 ≤ v 2 ≤ U 2 … .............. L n ≤ v n ≤ U n S . v = 0
  36. 36. Some of the problems with FBA  no substrate concentrations  not always realistic  solution degeneracy
  37. 37. FBA and metabolite concentrations linlog (with correct elasticities): Teusink: o linlog (with estimated elasticities): <ul><li>Good fit in most cases </li></ul><ul><li>Can easily incorporate experimental information to improve the fit </li></ul>
  38. 38. In general an FBA problem can have more than one optimal solution. FBA and solution degeneracy
  39. 39. FBA and unrealistic solutions
  40. 40. Computational hypotheses <ul><li>Test different conditions </li></ul><ul><li>Test different evolutionary pressures </li></ul><ul><li>Test the effect of unknown “costs” </li></ul>
  41. 41. Hypoxic conditions <ul><li>Under anaerobic conditions, yeast ferments </li></ul><ul><li>We tested if fermentation is also a response to relative O 2 limitation </li></ul><ul><li>O 2 transport was given an upper bound and FBA solved for increasing uptakes of glucose </li></ul>
  42. 42. Hypoxic conditions <ul><li>NOT a switch from respiration to fermentation </li></ul><ul><li>fermentation activated on top of respiration to compensate for increase in glucose </li></ul>
  43. 43. Resource preservation <ul><li>Minimisation of the number of active reactions </li></ul><ul><li>Resource preservation as the objective of optimisation, instead of optimal growth </li></ul><ul><li>A value for biomass production was chosen and fixed, then a new formulation solved </li></ul>
  44. 44. Resource preservation <ul><li>For low levels of glucose uptake respiration was chosen </li></ul><ul><li>For higher levels of uptake the model switched to fermentation to conserve resources </li></ul>
  45. 45. Energy (ATP) cost <ul><li>More respiration requires the synthesis of more mitochondria and/or a number of enzymes involved in the TCA cycle, respiratory chain, ATP synthesis </li></ul><ul><li>An energy cost related to mitochondria synthesis is added to the FBA formulation </li></ul>
  46. 46. Energy (ATP) cost <ul><li>Sensitivity analysis revealed the existence of a bifurcation point </li></ul><ul><li>Above this there was a sharp switch to fermentation </li></ul>
  47. 47. One last challenge: Getting the message across
  48. 48. Systems Biology
  49. 49. Conclusions <ul><li>I outlined some computational challenges in the study of metabolism </li></ul><ul><li>Solving such problems improves our biological understanding, and gives us the systematic tools necessary </li></ul><ul><li>Results get us closer and closer to simulating experimental observations </li></ul><ul><li>Such crucial improvements are a necessary stepping stone for the creation of realistic genome-scale models </li></ul><ul><li>We can guide experimental design to verify predictions </li></ul>
  50. 50. Acknowledgments <ul><li>The MCISB team </li></ul><ul><li>financial support from BBSRC/EPSRC via “The Manchester Centre for Integrative Systems Biology” grant (BB/C008219/1) </li></ul>

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