This work was performed under the auspices of the U.S. Department of Energy by University of California, Lawrence Livermore National Laboratory under Contract W-7405-Eng-48. Microbial Systems Group Biosciences & Biotechnology Division Lawrence Livermore National Laboratory Eivind Almaas Cellular Metabolic Network Modeling NetSci Conference 2007 New York Hall of Science UCRL-PRES

Microbes are ubiquitous 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 kbar … Important for human health Periodontal disease (risk of spont. abortions, heart problems) Stomach cancer Obesity … !! Gypsum crust Bison hot spring Roadside puddle Eliat salt pond Yellowstone Nat’l Park Next to road, PA

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

Metabolic Network Structure H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature 407, 651 (2000). Organisms from all 3 domains of life are scale-free networks. ArchaeaBacteriaEukaryotes Nodes : chemicals (substrates) Links : chem. reaction

Metabolic network representations

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

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

Whole-cell level metabolic dynamics (fluxes)

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 … Flux Balance Analysis (FBA)

Constraints & Optimization for growth R1 R2 R3 R4 R5 R6 T1 T2 T3 M1 M2M3 M4M5 M1 ext M5 ext M3 ext J.S. Edwards & B.O. Palsson, Proc. Natl. Acad. Sci. USA 97, 5528 (2000) R.U. Ibarra, J.S. Edwards & B.O. Palsson, Nature 420, 186 (2002) D. Segre, D. Vitkup & G.M. Church, Proc. Natl. Acad. Sci. USA 99, (2002) Flux Balance Analysis M1 M2 … M5 R1R2 … RN S11 S21 S12 S22 ….. V1 V2... = 0 Stoichiometric matrix Flux vector

Simple network example Optimization goal Optimal growth curve J.S. Edwards et al, Biotechn. Bioeng. 77, 27 (2002) optimal growth line

R.U. Ibarra, J.S. Edwards & B.O. Palsson, Nature 420, 186 (2002) Experimental confirmation: E. coli on glycerol Adaptive growth of E. coli with glycerol & O 2 : 60-day experiment Three independent populations: E1 & T=30ºC; T=37ºC Initially sub-optimal performance

How does network structure affect flux organization?

Statistical properties of optimal fluxes SUCC: Succinate uptake GLU : Glutamate uptake Central Metabolism, Emmerling et. al, J Bacteriol 184, 152 (2002) E. Almaas, B. Kovacs, T. Vicsec, Z. Oltvai and A.-L. Barabási, Nature 427, 839 (2004).

Mass predominantly flows along un-branched pathways! 2 Evaluate single metabolite use pattern by calculating: Two possible extremes: (a) All fluxes approx equal (b) One flux dominates Single metabolite use patterns E. Almaas, B. Kovacs, T. Vicsec, Z. Oltvai and A.-L. Barabási, Nature 427, 839 (2004).

Carbon source: Glutamate Carbon source: Succinate The metabolite high-flux pathways are connected, creating a High Flux Backbone Metabolic super-highways E. Almaas, B. Kovacs, T. Vicsec, Z. Oltvai and A.-L. Barabási, Nature 427, 839 (2004).

How does microbial metabolism adapt to its environment?

Metabolic plasticity 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) Flux plasticity Structural plasticity

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) 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 Metabolic plasticity E. Almaas, Z. N. Oltvai, A.-L. Barabási, PLoS Comput. Biol. 1(7):e68 (2005)

The metabolic core E. Almaas, Z. N. Oltvai, A.-L. Barabási, PLoS Comput. Biol. 1(7):e68 (2005) 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 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 The metabolic core is essential E. Almaas, Z. N. Oltvai, A.-L. Barabási, PLoS Comput. Biol. 1(7):e68 (2005) Correlation in mRNA expressions

Genetic interactions mediated by metabolic network

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 Epistatic interactions & cellular metabolism Hypothesis: Damage inflicted on metabolic function by a gene deletion may be alleviated through further gene impairments. Consequence: New paradigm for gene essentiality! A.E. Motter, N. Gulbahce, E. Almaas, A.-L. Barabási, Submitted. 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 E. coli experiments

knockouts 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 mechanism 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.

University of Notre Dame: A.-L. Barabási Z. Deszo B. Kovacs P.J. Macdonald Northwestern University A. Motter 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 Collaborators