THERMODYNAMICS OF THE MICROBIAL CYTOSOL

Transcription

1 Joint European Thermodynamics Conference - Chemnitz, 2010 THERMODYNAMICS OF THE MICROBIAL CYTOSOL U. v. Stockar, I.W. Marison, Th. Maskow, V. Vojinovic 1. General remarks on biothermodynamics 2. Thermodynamics of microbial growth 3. Opening the black box: thermodynamics of metabolic pathways 4. Conclusions

2 1. General remarks on biothermodynamics Finer Description Coarser Live Cultures 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, threshold concentrations Metabolism Whole cell thermodynamics Thermodynamics of metabolism Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering Biomolecules Biomolecular thermodynamics 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

3 2. Thermodynamics of microbial growth Finer Description Coarser Live Cultures 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, threshold concentrations Metabolism Whole cell thermodynamics Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering Biomolecules Thermodynamics of metabolism Biomolecular thermodynamics 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

4 2. Thermodynamics of microbial growth G Biosynthetic reactions New biomass G ΔG bios > 0 Substrates r bios > 0!

5 Driving force for growth and biomass yield G Substrates ΔG cat << 0! Energy yielding reaction Biosynthetic reactions New biomass ΔG bios > 0 G r bios > 0! Catabolic products r G = (1-Y X/S ) G cat + Y X/S G bios

6 Driving force for growth and biomass yield r G X H X Δ r G X (kj/c-mol) Δ r, r H X Y X/S (C-mol/C-mol) Δ r H x S. cerevisiae C. utilis K. fragilis Δ r G x C. pseudotropicalis E. coli Catabolic reaction: C 6 H 12 O O 2 => 6 CO H 2 O

7 Driving force for growth and biomass yield Too close to equilibrium, growth too slow r G X r G X -200 IDEAL COMPROMISE! r G X -300 r H X -400 Too much energy dissipation, r G X Y X/S too low Y X/S (C-mol/C-mol) Δ r H x S. cerevisiae C. utilis K. fragilis Δ r G x C. pseudotropicalis E. coli Catabolic reaction: C 6 H 12 O O 2 => 6 CO H 2 O

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9 3. Opening the black box: thermo of metabolism Finer Description Coarser Live Cultures 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, threshold concentrations Metabolism Whole cell thermodynamics Prediction of product yields Stoichiometry of animal cell cultures Prediction of cell physiology, systems biology Metabolic pathway feasibility analysis for metabolic engineering Biomolecules Thermodynamics of metabolism Biomolecular thermodynamics 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

10 3.1. Opening the black box In the whole cell: well > 1000 compounds well > 2000 reactions Aim in Systems Biology: Predict all rates!

11 HELP FROM THERMODYNAMICS!! According to the 2nd Law: r G j r j < 0 Thermodynamics predicts direction of reaction!! EXPECTED USEFULNESS Gaining new insight into functioning of metabolism Identifying potential metabolic bottlenecks Predicting feasibility of new metabolic pathways to be engineered into production or medical strains

12 3.2. Thermodynamic feasibility analysis (1) (2) (3) Glc Glc6P Fru6P FruDP GAP PGP 3PG 2PG PEP PYR LAC (4) (5) DHAP (6) (7) (8) (9) (10) (11) But, for each reaction: " r G = "G o + RT # ln % $ c i i! i

13 TFA when Ample Data is Available Genome-wide application of Thermodynamic Feasibility Analysis: Metabolite concentrations UNKNOWN! r G oʼ UNKNOWN! Does TFA yield meaningful results with experimental data for r Goʼ, c i etc? Proposition: Limit TFA to glycolysis All information available! Result of analysis known!

14 3.3. Importance of Δ r G o values R6 GAP + NAD + + P i BGP + NADH + H + Δ r G o ', kj / mol ph Enormous Uncertainty of Standard Gibbs Energy of Reaction!

15 3.3. Importance of Δ r G o values Δ r G o depends on: I ph pmg But: Experimental literature values measured at different I, ph, and pmg!! In order to compare, one needs to understand Δ r G o = f(i, ph, pmg) R. A. Alberty: Thermodynamics of Biochemical Reactions, 2003

16 ATP + H 2 O = ADP + Pi H 2 ATP 2- H 2 ADP - MgHATP HATP 3- MgHADP HADP 2- H 2 PO K ATP 4- + H 2 O ADP 3- + H + + HPO MgHPO MgATP MgADP Mg 2 ATP 16 species 12 equilibria!

17 Δ r G o ' for the hydrolysis of ATP as a function of ph and pmg Δ r G o ' kj / mol ph pmg I = 0.25 M, T = K

18 Experimental r G o ' values reaction 6 R6 GAP + NAD + + P i BGP + NADH + H + Δ r G o ', kj / mol ph

19 Corrected r G o ' values reaction 6 R6 GAP + NAD + + P i BGP + NADH + H + Δ r G o '', kj / mol ph

20 Glycolysis is composed of: 1. Glc + ATP = Glc6P + ADP 2. Glc6P = Fru6P 3. Fru6P + ATP = FruDP + ADP 4. FruDP = DHAP + GAP 5. DHAP = GAP 6. GAP + NAD + Pi = PGP + NADH 7. PGP + ADP = 3PG + ATP 8. 3PG = 2PG 9. 2PG = PEP 10. PEP + ADP = ATP + Pyr 11. Pyr + NADH = Lac + NAD TOTAL independent Reactions Species ! 61!

21 3.4. The importance of concentrations Ionic strength, M Experimentally reported values (ph, I) ph "Thermodynamic feasibility diagrams"

22 Importance of c i : Very wide possible range all concentrations: mm Ionic strength, M pmg = ph

23 Choosing highly realistic c min and c max c min and c max according to lowest and highest published value Metabolite Source B Source C Source D Source E Source G S. H Range ATP ADP Pi NADH NAD Glc G6P F6P FBP DHAP GAP BPG PG PG PEP Pyr Lac

24 Highly realistic concentration ranges All concentrations according to their own published range 1 Ionic strength, M Thermodynamically forbidden pmg = ph Glycolysis = entirely unfeasible!

25 Possible consequences J. W. Gibbs ( ) Mavrovouniotis (1993) ISMB-3 Mavrovouniotis (1996) Chem. Eng. Sci. (51) THERE MUST BE SOMETHING WRONG WITH THE FEASIBILITY ANALYSIS!

26 Assuming concentration of BPG is very low Conc min of BPG assumed = mm! 1 Ionic strength, M Thermodynamically forbidden pmg = ph Glycolysis = feasible!

27 4. CONCLUSIONS Even with all data available thermodynamic feasibility analysis yields erroneous result! Concentration of BPG lower than published? Other concentrations too constrained? Must be cleared up! Very large influence of c min and c max More data on intracellular concentrations! Δ r G o ' values: Enormous influence Need for equilibrium measurements!! Reliable group contribution methods!

28 USEFULNESS IN GENOME-SCALE MODELING Gaining insight into metabolism +/- OK! Identifying potential metabolic bottlenecks Doubtful! Predicting feasibility of new metabolic pathways to be engineered into production or medical strains Absolutely impossible for the time being. BIG POTENTIAL, BUT: MORENE MOre REsearch NEeded!!!

29 Besten Dank für Ihre Aufmerksamkeit!