Cellular Metabolic Network Modeling
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Transcript of Cellular Metabolic Network Modeling
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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 GroupBiosciences & Biotechnology Division
Lawrence Livermore National Laboratory
Eivind Almaas
Cellular Metabolic Network Modeling
NetSci Conference 2007New York Hall of Science
UCRL-PRES-231343
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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, pressure of >1 kbar …
Important for human health• Periodontal disease (risk of spont. abortions, heart problems)
• Stomach cancer
• Obesity … !!
Gypsum crustBison hot spring
Roadside puddle
Eliat salt pondYellowstone Nat’l Park
Next to road, PA
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Micro-organisms: The good, the bad & the ugly
Saccharomyces cerevisiae
Helicobacter pylori
Escherichia coli
Cells are chemical factories
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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.
Archaea Bacteria Eukaryotes
Nodes: chemicals (substrates)
Links: chem. reaction
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Metabolic network representations
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Effect of network representations
E. Almaas, J. Exp. Biol. 210, 1548 (2007)
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Effect of network representations
E. Almaas, J. Exp. Biol. 210, 1548 (2007)
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Whole-cell levelmetabolic dynamics
(fluxes)
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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)
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Constraints & Optimization for growth
R1
R2
R3
R4
R5
R6
T1
T2
T3
M1
M2 M3
M4 M5
M1ext
M5ext
M3ext
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, 15112 (2002)
Flux Balance Analysis
M1M2…
M5
R1 R2 … RNS11S21
S12S22
…..
V1V2
...
= 0
Stoichiometricmatrix Flux vector
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Simple network example
1 2 6
3 4 5 7
1 1 2 6 4
3 4 5 7
2
3
b
b
b
b
Optimization goal
Optimal growth curve
J.S. Edwards et al, Biotechn. Bioeng. 77, 27 (2002)
1
2
3
0
optimal growth line
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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 & O2:• 60-day experiment• Three independent populations: E1 & E2 @ T=30ºC; E3 @ T=37ºC• Initially sub-optimal performance
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How does network structure
affect flux organization?
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Statistical properties of optimal fluxes
SUCC: Succinate uptakeGLU : 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).
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Mass predominantly flows along un-branched pathways!
2Evaluate single metabolite usepattern 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).
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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).
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How does microbial metabolism adapt to
its environment?
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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
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• 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)
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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
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• 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
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Genetic interactionsmediated by metabolic
network
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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
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Results: Gene knockoutsknockouts can improve function
Computational predictions in E. coli:Two types of metabolic recovery from gene knockoutson 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.
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• University of Notre Dame:A.-L. BarabásiZ. DeszoB. KovacsP.J. Macdonald
• Northwestern UniversityA. Motter
•Los Alamos Nat’l LabN. Gulbahce
• University of PittsburghZ. Oltvai
• Virginia TechR. Kulkarni
• Kent State UniversityR. Jin
• Trinity UniversityA. Holder
• Network Biology Group (LLNL)Eivind AlmaasJoya DeriCheol-Min GhimSungmin LeeAli Navid
Collaborators