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Cellular Metabolic Network Modeling

UCRL -PRES-231343. NetSci Conference 2007 New York Hall of Science. Cellular Metabolic Network Modeling. Microbes are ubiquitous. Bison hot spring. Roadside puddle. Gypsum crust. Yellowstone Nat’l Park. Next to road, PA. Eliat salt pond. Observations

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Cellular Metabolic Network Modeling

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  1. UCRL-PRES-231343 NetSci Conference 2007 New York Hall of Science Cellular Metabolic Network Modeling

  2. Microbes are ubiquitous Bison hot spring Roadside puddle Gypsum crust Yellowstone Nat’l Park Next to road, PA Eliat salt pond • Observations • Total biomass on earth dominated by microbes • Microbes co-exist as “communities” in a range of environments spanning the soil and the ocean; critically affect C and N cycling; potential source of biofuels • Even found in extreme environments, such as hypersaline ponds, hot springs, permafrost, acidity of pH<1, pressure of >1 kbar … • Important for human health • Periodontal disease (risk of spont. abortions, heart problems) • Stomach cancer • Obesity … !!

  3. Cells are chemical factories Micro-organisms: The good, the bad & the ugly Escherichia coli Helicobacter pylori Saccharomyces cerevisiae

  4. Archaea Bacteria Eukaryotes Organisms from all 3 domains of life are scale-free networks. Metabolic Network Structure Nodes: chemicals (substrates) Links: chem. reaction H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature407, 651 (2000).

  5. Metabolic network representations

  6. Effect of network representations E. Almaas, J. Exp. Biol. 210, 1548 (2007)

  7. Effect of network representations E. Almaas, J. Exp. Biol. 210, 1548 (2007)

  8. Whole-cell level metabolic dynamics (fluxes)

  9. Flux Balance Analysis (FBA) • FBA input: • List of metabolic reactions • Reaction stoichiometry • Impose mass balance • Impose steady state • Optimization goal • FBA ignores: • Fluctuations and transients • Enzyme efficiencies • Metabolite concentrations / toxicity • Regulatory effects • Cellular localization • …

  10. M3ext T3 M2 M3 R3 R1 M1ext M1 T1 R4 R5 M4 M5 R2 Stoichiometric matrix M5ext R6 Flux vector T2 … R1 R2 RN M1 S11 S12 V1 M2 S21 S22 V2 = 0 ….. … ... M5 Flux Balance Analysis Constraints & Optimization for growth J.S. Edwards & B.O. Palsson, Proc. Natl. Acad. Sci. USA 97, 5528 (2000) R.U. Ibarra, J.S. Edwards & B.O. Palsson, Nature420, 186 (2002) D. Segre, D. Vitkup & G.M. Church, Proc. Natl. Acad. Sci. USA99, 15112 (2002)

  11. 3 1 1 2 6 4 1 2 6 3 4 5 7 3 4 5 7 2 1 0 2 3 optimal growth line Simple network example Optimal growth curve Optimization goal J.S. Edwards et al, Biotechn. Bioeng. 77, 27 (2002)

  12. Experimental confirmation: E. coli on glycerol • Adaptive growth of E. coli with glycerol & O2: • 60-day experiment • Three independent populations: • E1 & E2 @ T=30ºC; E3 @ T=37ºC • Initially sub-optimal performance R.U. Ibarra, J.S. Edwards & B.O. Palsson, Nature420, 186 (2002)

  13. How does network structure affect flux organization?

  14. SUCC: Succinate uptake GLU : Glutamate uptake Central Metabolism, Emmerling et. al, J Bacteriol184, 152 (2002) Statistical properties of optimal fluxes E. Almaas, B. Kovacs, T. Vicsec, Z. Oltvai and A.-L. Barabási, Nature427, 839 (2004).

  15. Mass predominantly flows along un-branched pathways! 2 Single metabolite use patterns Evaluate single metabolite use pattern by calculating: Two possible extremes: (a) All fluxes approx equal (b) One flux dominates E. Almaas, B. Kovacs, T. Vicsec, Z. Oltvai and A.-L. Barabási, Nature427, 839 (2004).

  16. Carbon source: Glutamate Carbon source: Succinate Metabolic super-highways The metabolite high-flux pathways are connected, creating a High Flux Backbone E. Almaas, B. Kovacs, T. Vicsec, Z. Oltvai and A.-L. Barabási, Nature427, 839 (2004).

  17. How does microbial metabolism adapt to its environment?

  18. Sample 30,000 different optimal conditions randomly and uniformly • Metabolic network adapts to environmental changes using: (a) Flux plasticity (changes in flux rates) (b) Structural plasticity (reaction [de-] activation) Structural plasticity Flux plasticity Metabolic plasticity

  19. Sample 30,000 different optimal conditions randomly and uniformly • Metabolic network adapts to environmental changes using: (a) Flux plasticity (changes in flux rates) (b) Structural plasticity (reaction [de-] activation) Metabolic plasticity • There exists a group of reactions NOT subject to structural plasticity: the metabolic core • These reactions must play a key role in maintaining the metabolism’s overall functional integrity E. Almaas, Z. N. Oltvai, A.-L. Barabási, PLoS Comput. Biol. 1(7):e68 (2005)

  20. A connected set of reactions that are ALWAYS active  not random effect The larger the network, the smaller the core  a collective network effect The metabolic core E. Almaas, Z. N. Oltvai, A.-L. Barabási, PLoS Comput. Biol. 1(7):e68 (2005)

  21. The metabolic core is essential • The core is highly essential: 75% lethal (only 20% in non-core) for E. coli. • 84% lethal (16% non-core) for S. cerevisiae. • The core is highly evolutionary conserved: • 72% of core enzymes (48% of non-core) for E. coli. • The mRNA core activity is highly correlated in E. coli Correlation in mRNA expressions E. Almaas, Z. N. Oltvai, A.-L. Barabási, PLoS Comput. Biol. 1(7):e68 (2005)

  22. Genetic interactions mediated by metabolic network

  23. Experimental data supports hypothesis: - No satisfactory explanation existed previously! - Comparison of wild-type E. coli (sub-optimal) growth with growth in mutants. - Multiple examples of suboptimal recovery.  suboptimal wild-type growth rate  single-knockout mutant Epistatic interactions & cellular metabolism Epistasis: • Nonlinear gene - gene interactions • Partly responsible for inherent complexity and non-linearity in genome – phenotype relationship • Non-local gene effects are mediated by network of metabolic interactions Hypothesis: Damage inflicted on metabolic function by a gene deletion may be alleviated through further gene impairments. Consequence: New paradigm for gene essentiality! E. coli experiments A.E. Motter, N. Gulbahce, E. Almaas, A.-L. Barabási, Submitted.

  24. Epistatic mechanism Results: Gene knockouts can improve function Computational predictions in E. coli: Two types of metabolic recovery from gene knockouts on minimal medium with glucose: (a) Suboptimal recovery (b) Synthetic viability • Epistatic interaction mechanism: • Gene-knockout  flux rerouting • Choose genes for knockout that align mutant flux distribution with optimal A.E. Motter, N. Gulbahce, E. Almaas, A.-L. Barabási, Submitted.

  25. Collaborators • Los Alamos Nat’l Lab • N. Gulbahce • University of Pittsburgh • Z. Oltvai • Virginia Tech • R. Kulkarni • Kent State University • R. Jin • Trinity University • A. Holder • Network Biology Group (LLNL) • Eivind Almaas • Joya Deri • Cheol-Min Ghim • Sungmin Lee • Ali Navid • University of Notre Dame: • A.-L. Barabási • Z. Deszo • B. Kovacs • P.J. Macdonald • Northwestern University • A. Motter

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