Incremental parameter estimation of kinetic metabolic network models

BMC Systems Biology

Volumn 6, Issue , 2012, Pages

  Jia, Gengjie ^a   Stephanopoulos, Gregory ^b   Gunawan, Rudiyanto ^c  

^a SINGAPORE MIT ALLIANCE   (Singapore)

^b Massachusetts Institute of Technology   (United States)

^c ETH ZURICH   (Switzerland)

Author keywords

GMA model; Incremental parameter estimation; Kinetic modeling; Metabolic network

Indexed keywords

ARTICLE; BIOLOGICAL MODEL; GLYCOSYLATION; KINETICS; LACTOCOCCUS LACTIS; METABOLISM;

GLYCOSYLATION; KINETICS; LACTOCOCCUS LACTIS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL;

MLCS; MLOWN;

EID: 84869217594     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/1752-0509-6-142     Document Type: Article

References (33)

1. Chou IC, Voit EO. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math Biosci 2009, 219(2):57-83.
2. Mendes P, Kell D. Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 1998, 14(10):869-883.
3. Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 2003, 13(11):2467-2474.
4. Savageau MA. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J Theor Biol 1969, 25(3):365-369.
5. Savageau MA. Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. J Theor Biol 1969, 25(3):370-379.
6. Voit EO. Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists 2000, New York: Cambridge University Press.
7. Voit EO, Almeida J. Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 2004, 20(11):1670-1681.
8. Tsai KY, Wang FS. Evolutionary optimization with data collocation for reverse engineering of biological networks. Bioinformatics 2005, 21(7):1180-1188.
9. Kimura S, Ide K, Kashihara A, Kano M, Hatakeyama M, Masui R, Nakagawa N, Yokoyama S, Kuramitsu S, Konagaya A. Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 2005, 21(7):1154-1163.
10. Maki Y, Ueda T, Masahiro O, Naoya U, Kentaro I, Uchida K. Inference of genetic network using the expression profile time course data of mouse P19 cells. Genome Inform 2002, 13:382-383.
11. Jia G, Stephanopoulos G, Gunawan R. Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method. Bioinformatics 2011, 27(14):1964-1970.
12. Bardow A, Marquardt W. Incremental and simultaneous identification of reaction kinetics: methods and comparison. Chem Eng Sci 2004, 59(13):2673-2684.
13. Marquardt W, Brendel M, Bonvin D. Incremental identification of kinetic models for homogeneous reaction systems. Chem Eng Sci 2006, 61(16):5404-5420.
14. Goel G, Chou IC, Voit EO. System estimation from metabolic time-series data. Bioinformatics 2008, 24(21):2505-2511.
15. Voit EO, Goel G, Chou IC, Fonseca LL. Estimation of metabolic pathway systems from different data sources. IET Syst Biol 2009, 3(6):513-522.
16. Voit EO, Almeida J, Marino S, Lall R, Goel G, Neves AR, Santos H. Regulation of glycolysis in Lactococcus lactis: an unfinished systems biological case study. Syst Biol (Stevenage) 2006, 153(4):286-298.
17. Egea JA, Rodriguez-Fernandez M, Banga JR, Marti R. Scatter search for chemical and bio-process optimization. J Global Optimization 2007, 37(3):481-503.
18. Rodriguez-Fernandez M, Egea JA, Banga JR. Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. BMC Bioinformatics 2006, 7:483.
19. Akaike H. New Look at Statistical-Model Identification. IEEE T Automat Contr 1974, Ac19(6):716-723.
20. Montgomery DC, Runger GC. Applied statistics and probability for engineers 2007, Hoboken, NJ: Wiley, 4.
21. Neves AR, Ramos A, Costa H, van Swam II, Hugenholtz J, Kleerebezem M, de Vos W, Santos H. Effect of different NADH oxidase levels on glucose metabolism by Lactococcus lactis: kinetics of intracellular metabolite pools determined by in vivo nuclear magnetic resonance. Appl Environ Microbiol 2002, 68(12):6332-6342.
22. Neves AR, Ramos A, Nunes MC, Kleerebezem M, Hugenholtz J, de Vos WM, Almeida J, Santos H. In vivo nuclear magnetic resonance studies of glycolytic kinetics in Lactococcus lactis. Biotechnol Bioeng 1999, 64(2):200-212.
23. Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmuller U, Timmer J. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 2009, 25(15):1923-1929.
24. Srinath S, Gunawan R. Parameter identifiability of power-law biochemical system models. J Biotechnol 2010, 149(3):132-140.
25. Almeida JS. Predictive non-linear modeling of complex data by artificial neural networks. Curr Opin Biotechnol 2002, 13(1):72-76.
26. Eilers PH. A perfect smoother. Anal Chem 2003, 75(14):3631-3636.
27. Vilela M, Borges CC, Vinga S, Vasconcelos AT, Santos H, Voit EO, Almeida JS. Automated smoother for the numerical decoupling of dynamics models. BMC Bioinformatics 2007, 8:305.
28. Jaulin L, Kieffer M, Didrit O, Walter E. Applied interval analysis: with examples in parameter and state estimation, robust control and robotics 2001, London: Springer.
29. Lin YD, Stadtherr MA. Validated solution of ODEs with parametric uncertainties. 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering 2006, 21:167-172.
30. Latendresse M, Paley S, Karp PD. Browsing metabolic and regulatory networks with BioCyc. Methods Mol Biol 2012, 804:197-216.
31. Imoto S, Kim S, Goto T, Miyano S, Aburatani S, Tashiro K, Kuhara S. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. J Bioinform Comput Biol 2003, 1(2):231-252.
32. Nagarajan R, Upreti M. Comment on causality and pathway search in microarray time series experiment. Bioinformatics 2008, 24(7):1029-1032.
33. Tung TQ, Ryu T, Lee KH, Lee D. Inferring gene regulatory networks from microarray time series data using transfer entropy. Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems:20-22 June 2007; Maribor, Slovenia 2007, 383-388. Los Alamitos: IEEE Computer Society, Kokol P, Los A.