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Joint European Thermodynamics Conference - Chemnitz, 2010
1. General remarks on biothermodynamics
2. Thermodynamics of microbial growth
3. Opening the black box: thermodynamics of metabolicpathways
4. Conclusions
THERMODYNAMICS OF THEMICROBIAL CYTOSOL
THERMODYNAMICS OF THEMICROBIAL CYTOSOL
U. v. Stockar, I.W. Marison,Th. Maskow, V. Vojinovic
1. General remarks on 1. General remarks on biothermodynamicsbiothermodynamics
Heat evolution of cellular cultures: cooling facility design, on-line monitoring Insight into energetics of cellular growth, understanding driving forces Culture performance parameters for process development and design: growth
and product yield, growth rate, maintenance coefficients, thresholdconcentrations
Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering
Physical-chemical properties of biomolecules Prediction of phase equilibria for downstream processing Structural and functional stability of proteins and other biomolecules Biochemical reaction equilibria in biotransformations Effects of T, P, pH, solvents and solutes on activity and selectivity of
biocatalysts
Live Cultures Whole cell thermodynamics
Metabolism Thermodynamics of metabolism
Biomolecules Biomolecular thermodynamics
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2. Thermodynamics of microbial growth2. Thermodynamics of microbial growthFi
ner
Des
crip
tion
Coa
rser Heat evolution of cellular cultures: cooling facility design, on-line monitoring
Insight into energetics of cellular growth, understanding driving forces Culture performance parameters for process development and design: growth
and product yield, growth rate, maintenance coefficients, thresholdconcentrations
Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering
Physical-chemical properties of biomolecules Prediction of phase equilibria for downstream processing Structural and functional stability of proteins and other biomolecules Biochemical reaction equilibria in biotransformations Effects of T, P, pH, solvents and solutes on activity and selectivity of
biocatalysts
Live Cultures Whole cell thermodynamics
Metabolism Thermodynamics of metabolism
Biomolecules Biomolecular thermodynamics
Substrates
New biomassG G
rrbiosbios >> 0 ! 0 !
ΔGbios > 0
Biosyntheticreactions
2. Thermodynamics of microbial growth2. Thermodynamics of microbial growth
Substrates
New biomass
∆rG = (1-YX/S)•∆Gcat + YX/S • ∆Gbios
Catabolic products
G GEnergy yieldingreaction
rrbiosbios >> 0 ! 0 !
ΔGbios > 0
ΔGcat << 0!
Driving force for growth and biomass yieldDriving force for growth and biomass yield
Biosyntheticreactions
0
-100
-200
-300
-400
-500
-600
-700
H° X
ΔrG
° X (k
J/C
-mol
)Δ
r,
0.4 0.5 0.6 0.7 0.8 0.9 1YX/S (C-mol/C-mol)
∆rHX
∆rGX
K. fragilis− Δ rHx S. cerevisiae
E. coli
C. utilis
C. pseudotropicalis− Δ rGx
°
°
Driving force for growth and biomass yieldDriving force for growth and biomass yield
Catabolic reaction: C6H12O6 + 6 O2 => 6 CO2 + 6 H2O
0
-100
-200
-300
-400
-500
-600
-700
0.4 0.5 0.6 0.7 0.8 0.9 1YX/S (C-mol/C-mol)
∆rHX
∆rGX
K. fragilis− Δ rHx S. cerevisiae
E. coli
C. utilis
C. pseudotropicalis− Δ rGx
°
°
∆rGX
Too close toequilibrium, growth too slow
∆rGX
∆rGXToo much energydissipation,YX/S too low
IDEALCOMPROMISE!
Driving force for growth and biomass yieldDriving force for growth and biomass yield
Catabolic reaction: C6H12O6 + 6 O2 => 6 CO2 + 6 H2O
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3. Opening the black box: thermo of metabolism3. Opening the black box: thermo of metabolism
Heat evolution of cellular cultures: cooling facility design, on-line monitoring Insight into energetics of cellular growth, understanding driving forces Culture performance parameters for process development and design: growth
and product yield, growth rate, maintenance coefficients, thresholdconcentrations
Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering
Physical-chemical properties of biomolecules Prediction of phase equilibria for downstream processing Structural and functional stability of proteins and other biomolecules Biochemical reaction equilibria in biotransformations Effects of T, P, pH, solvents and solutes on activity and selectivity of
biocatalysts
Live Cultures Whole cell thermodynamics
Metabolism Thermodynamics of metabolism
Biomolecules Biomolecular thermodynamics
3.1. Opening the black box
In the whole cell:• well > 1000 compounds• well > 2000 reactions
Aim in Systems Biology:
•• Predict all rates!Predict all rates!
HELP FROM THERMODYNAMICS!!
EXPECTED USEFULNESSEXPECTED USEFULNESS
Gaining new insight into functioning of metabolism
Identifying potential metabolic bottlenecks
Predicting feasibility of new metabolic pathways to beengineered into production or medical strains
Thermodynamics predicts direction of reaction!!Thermodynamics predicts direction of reaction!!
According to the 2nd Law:
∆rGj • rj < 0∆rGj • rj < 0
Glc Glc6P Fru6P FruDP GAP PGP 3PG 2PG PEP PYR LAC
DHAP
(1) (2) (3)
(4) (5)
(6) (7) (8) (9) (10) (11)
But, for each reaction:
!
" rG = "Go + RT # ln ci$ i
i% !
3.2. Thermodynamic feasibility analysis
Proposition:
Limit TFA to glycolysisAll information available!Result of analysis known!
Does TFA yield meaningful resultsDoes TFA yield meaningful resultswith experimental data for with experimental data for ∆∆rrGGoo ʼ̓, , ccii etc?etc?
Genome-wide application of Thermodynamic Feasibility Analysis:
Metabolite concentrations UNKNOWN!∆rGoʼ UNKNOWN!
TFA when Ample Data is Available
R6 GAP + NAD+ + Pi BGP + NADH + H+
ΔrG
o ', k
J / m
ol
pH
3.3. Importance of ΔrGo values
Enormous Uncertainty of Standard Gibbs Energy of Reaction !Enormous Uncertainty of Standard Gibbs Energy of Reaction !
ΔrGo’ depends on:
I
pH
pMg
But: Experimental literature values measuredat different I, pH, and pMg!!
In order to compare, one needs to understand
ΔrGo’ = f(I, pH, pMg)
R. A. Alberty: Thermodynamics of Biochemical Reactions, 2003
3.3. Importance of ΔrGo values
MgHPO4
HATP3- HADP2- H2PO4-
H2ATP2- H2ADP-
ATP4- + H2O ADP3- + H+ + HPO42-
MgHADPMgHATP-
MgATP2-
Mg2ATP
MgADP-
K’7.60
4.68 4.36
7.18 7.22
6.18
2.69
3.63 2.50
4.65
2.71
16 species12 equilibria !
ATP + H2O = ADP + Pi
ΔrGo' for the hydrolysis of ATP as a function of pH and pMg
- 30
- 40
- 48
- 24
45
67
8 1
2
3
4
5
6ΔrGo'kJ / mol
pH
pMg
I = 0.25 M, T = 298.15 K
Experimental ∆rGo' values reaction 6
R6 GAP + NAD+ + Pi BGP + NADH + H+
ΔrG
o ', k
J / m
ol
pH
R6 GAP + NAD+ + Pi BGP + NADH + H+
ΔrG
o '',
kJ /
mol
pH
Corrected ∆rGo' values reaction 6
1. Glc + ATP = Glc6P + ADP2. Glc6P = Fru6P3. Fru6P + ATP = FruDP + ADP4. FruDP = DHAP + GAP5. DHAP = GAP6. GAP + NAD + Pi = PGP + NADH7. PGP + ADP = 3PG + ATP8. 3PG = 2PG9. 2PG = PEP10. PEP + ADP = ATP + Pyr11. Pyr + NADH = Lac + NAD
TOTAL independent
155
1585
1516
77
1714
249
2113
9222211112421
Reactions Species
Glycolysis is composed of:
56! 61!
4 5 6 7 8 9 0
0.2
0.4
0.6
0.8
1
pH
Ioni
c st
reng
th, M
"Thermodynamic feasibility diagrams"
Experimentallyreported values(pH, I)
3.4. The importance of concentrations
4 5 6 7 8 9 0
0.2
0.4
0.6
0.8
1
pH
Ioni
c st
reng
th, M
all concentrations: 0.01 - 20 mM
pMg = 1
Importance of ci: Very wide possible range
Choosing highly realistic cmin and cmax
cmin and cmax according to lowest and highest published value
Metabolite Source B Source C Source D Source E Source G S. H Range
ATP 0.5 0.75 - 1.74 0.31 1.85 1.21 0.17 0.31 - 1.85ADP 0.075 0.23 - 0.84 0.4 0.138 - - 0.075 - 0.84 Pi 0.5 - - 1.0 - - 0.5 - 1.0NADH 0.05 0.038 - 0.145 - - - 0.038 - 0.145NAD 1.31 0.65 - 1.2 3.55 - - 0.65 - 3.55
Glc - - - 5.0 - - 5.0 - 5.0G6P - - 0.22 0.083 1.21 0.17 0.083 - 1.21F6P - - 0.25 0.014 0.48 0.04 0.014 - 0.48FBP - - 3.29 0.031 3.1 - 0.031 - 3.3DHAP - - - 0.138 - - 0.138 - 3.29GAP - - - 0.0185 - - 0.0185 - 0.185BPG - - - 0.06 - - 0.06 - 0.253PG - - - 0.118 - - 0.118 - 0.3542PG - - - 0.0295 - - 0.0295 - 0.089PEP - - - 0.023 0.2 0.6 0.023 - 0.6Pyr - - - 0.051 - 0.4 0.051 - 0.4Lac - - - - - - 0.051 - 0.4
4 5 6 7 8 9 0
0.2
0.4
0.6
0.8
1
Ioni
c st
reng
th, M
pH
Thermodynami-cally forbidden pMg = 3
All concentrations according to their own published range
Highly realistic concentration ranges
➽ Glycolysis = entirely unfeasible!
Possible consequences
J. W. Gibbs (1839-1903) Mavrovouniotis (1993) ISMB-3Mavrovouniotis (1996) Chem. Eng. Sci. (51)
THERE MUST BE SOMETHING WRONG WITH THEFEASIBILITY ANALYSIS!
4 5 6 7 8 9 0
0.2
0.4
0.6
0.8
1
Ioni
c st
reng
th, M
pH
➽ Glycolysis = feasible!
Thermodynami-cally forbidden pMg = 3
Conc min of BPG assumed = 0.0007 mM!
Assuming concentration of BPG is very low
Even with all data available thermodynamic feasibility analysisyields erroneous result!
Concentration of BPG lower than published? Other concentrations too constrained? Must be cleared up!
Very large influence of cmin and cmax More data on intracellular concentrations!
ΔrGo' values: Enormous influence Need for equilibrium measurements !!
Reliable group contribution methods !
4. CONCLUSIONS4. CONCLUSIONS
MOre REsearch NEeded !!!
MORENE
USEFULNESS IN GENOME-SCALE MODELINGUSEFULNESS IN GENOME-SCALE MODELING
Gaining insight into metabolism+/- OK!
Identifying potential metabolic bottlenecksDoubtful!
Predicting feasibility of new metabolic pathways to be engineeredinto production or medical strainsAbsolutely impossible for the time being. BIG POTENTIAL, BUT:
Besten Dank
für Ihre Aufmerksamkeit!für Ihre Aufmerksamkeit!
Besten Dank