Download Cellular Metabolic Network Modeling

UCRL-PRES-231343
NetSci Conference 2007
New York Hall of Science
Cellular Metabolic Network Modeling
Eivind Almaas
Microbial Systems Group
Biosciences & Biotechnology Division
Lawrence Livermore National Laboratory
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.
Microbes are ubiquitous
Bison hot spring
Gypsum crust
Yellowstone Nat’l Park
Eliat salt pond
Roadside puddle
Next to road, PA
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 … !!
Micro-organisms: The good, the bad & the ugly
Escherichia coli
Cells are chemical factories
Helicobacter pylori
Saccharomyces cerevisiae
Metabolic Network Structure
Nodes: chemicals
(substrates)
Links: chem. reaction
Archaea
Bacteria
Eukaryotes
Organisms
from all 3
domains of life
are scale-free
networks.
H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature 407, 651 (2000).
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)
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
…
Flux Balance Analysis
T3
M2
M1ext T1
M1
M3ext
M3
R3
R1
R4
M4 R5
M5
R2
M5ext
R6
T2
Stoichiometric
matrix
…
RN
V1
V2
…
...
R1 R2
M1 S11 S12
M2 S21 S22
Flux vector
=0
Constraints &
Optimization for growth
M5
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)
Simple network example
Optimal growth curve
b3
b1
1
1
2
6
2
b4
6
  
3 3 44 5 5

7 7
b2
Optimization goal
0
1
2
optimal growth line
J.S. Edwards et al, Biotechn. Bioeng. 77, 27 (2002)
3
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, Nature 420, 186 (2002)
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).
Single metabolite use patterns
2
Evaluate single metabolite use
pattern by calculating:
Two possible extremes:
(a) All fluxes approx equal
(b) One flux dominates
Mass predominantly flows
along un-branched pathways!
E. Almaas, B. Kovacs, T. Vicsec, Z. Oltvai and A.-L. Barabási, Nature 427, 839 (2004).
Metabolic super-highways
The metabolite high-flux pathways are connected, creating a
High Flux Backbone
Carbon source: Glutamate
Carbon source: Succinate
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
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)
• 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)
The metabolic core
A connected set of reactions that are ALWAYS active
 not random effect
The larger the network, the smaller the core
 a collective network effect
E. Almaas, Z. N. Oltvai, A.-L. Barabási, PLoS Comput. Biol. 1(7):e68 (2005)
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)
Genetic interactions
mediated by metabolic
network
Epistatic interactions & cellular metabolism
Epistasis:
 Nonlinear gene - gene interactions
 Partly responsible for inherent complexity and nonlinearity 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.
E. coli experiments
Consequence: New paradigm for gene essentiality!
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
A.E. Motter, N. Gulbahce, E. Almaas, A.-L. Barabási, Submitted.
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.
Collaborators
• 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
•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