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

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

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.

Archaea Bacteria Eukaryotes

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 levelmetabolic 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

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

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

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

How does network structure

affect flux organization?

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).

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).

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 interactionsmediated 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

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.

• 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