System Biology ISA5101 Final Project

1. System Biology ISA5101Final Project Group 14 g923922 李昀 g925929 何瓊雯

2. Part II • Propose experiments to determine the metabolic fluxes of the network associated with each product • Propose ways to maximize yield

3. Biosynthetic Fractional 13C Labeling and 2D NMR • Biosynthetically directed fractional 13C labeling of the proteinogenic amino acids is achieved by feeding a mixture of uniformly 13C-labeled and unlabeled carbon source compounds into a bio-reaction network • Attractive features include • an inherently small demand for 13C-labeled source compound • High sensitivity of 2D [ 13C, 1H ]-correlation NMR spectroscopy for analysis of 13C-labeling patterns

4. Biochemical Systems Theory (BST) • Michaelis-Menton (MM) models are long been considered as the gold standard for biochemical analysis • New approaches to capture the behavior of biochemical systems • Biochemical systems theory (BST) • Metabolic control analysis (MCA) • S-system • Each rate law for synthesis and degradation is presented by a product of power-law functions of all variables that have a direct influence upon the rate law in question

5. Canonical Modeling • S-systems and related systems have been called canonical models because of their rigid structures • The general S-system description is as follow: whose parameters are called rate constants ( and ) and kinetic orders ( and ) for i =1, 2,…, n

6. Canonical Modeling (cont) • In the canonical model, each kinetic order measures the slope of a rate V as a function of a metabolite or effector Xi in logarithmic coordinates • The rate constant is obtained by equating the power-law term with the traditional rate at steady state and substituting the numerical values for kinetic orders

7. Canonical Modeling (cont) • Thus, mass balance equations and kinetic rate equations must be fully understood to construct the S-system model for a target pathway • Besides, the calculation of kinetic orders and rate constants is required the fully preparation of the steady state concentration of each metabolite and parameter values involved in every rate equation • It seems as an impossible mission!!

8. Our Proposed Model • First, we merge two existing dynamic models, which includes • Central Carbon Metabolism • Glucose Transport System (PTS) • Glycolysis Pathway • Pentose-Phosphate Pathway • Tryptophan Synthesis • Second, append the model above to further consider both the repression of trp operon and the feedback inhibition of the enzymes by tryptophan

9. Metabolic Pathways Involved in the Synthesis of Tryptophan

10. Mass Balance Equations in Central Carbon Metabolism Model and Tryptophan Synthesis Model

11. Trp operon Model (Marin-Sanguino and Torres, 2000)

12. Trp operon Model (cont)

13. Our Proposed Model (cont) • Data is sufficient in Central Carbon Metabolism model for us to construct the S-system model of this part • However, data in Tryptophan synthesis model is deficient for the construction of S-system model of this part • Therefore, even with the S-system model of trp operon at hand, we still cannot simulate the quantitative optimization of Tryptophan production with the existing MATLAB package

14. Metabolic Pathway Analysis Tool ― BSTLab

15. Metabolic Pathway Analysis Tool ― BSTLab

16. Metabolic Pathway Analysis Tool ― BSTLab

17. Metabolic Pathway Analysis Tool ― BSTLab

18. Optimization Method • Key advantage of formulating the biochemical pathway as an S-system model is that the steady state is characterized by linear algebraic equations • Typical objective functions and constraints on fluxes and metabolites can be formulated as linear equations or linear inequalities • The optimization can be achieved by any existing linear optimization packages

19. Optimization Method (cont) • However, the steady-state solution of the S-system must be checked with stability and possibly with robustness • If significant discrepancy between the S-system model and the original MM model is detected, the system is to be revisited with more stringent constraints • In (Marin-Sanguino and Torres, 2000), p5 and ki are key parameters in modulating the production of tryptophan, which may help design a different strain of E. coli

20. Conclusion • A great deal of experimental work is still needed to implement the systematic optimization approach presented here • Natural experimental uncertainties should also be considered, which in some case may cause a 50% deviation in the expression of a given gene

21. References • Schmid, J.W., Mauch, K., Reuss, M., Gilles, E.D., Kremling, A., 2004. Metabolic design based on a coupled gene expression---metabolic network model of tryptophan production in Escherichia coli. Metabolic Engineering 6, 364-377 • Xiu, Z.-L., Zeng, A.-P., Deckwer, W.-D., 1997. Model analysis concerning the effects of growth rate and intracellular tryptophan level on the stability and dynamics of tryptophan biosynthesis in bacteria. J. Biotechnol. 58, 125-140 • Voit, E.O., Torres Darias, N.V., 1998. Canonical modeling of complex pathways in biotechnology. Biotech. & Bioeng. 1, 321-341 • Chassagnole, C., Noisommit-Rizzi, N. Schmid, J.W., Mauch, K., Reuss, M., 2002 .Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. 79, 53-73 • Marin-Sanguino, A., Torres, N.V., 2000. Optimization of tryptophan production in bacteria. Design of a strategy for genetic manipulation of the tryptophan operon for tryptophan flux maximization. Biotechnol. Prog. 16, 133-145 • Rizzi, M., Baltes, M., Theobald, U., Reuss, M., 1996. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae:II. Mathematical model. Biotechnol. Bioeng. 55, 592-608