A consensus approach for estimating the predictive accuracy of dynamic models in biology

Computer Methods and Programs in Biomedicine

Volumn 119, Issue 1, 2015, Pages 17-28

  Villaverde, Alejandro F ^a   Bongard, Sophia ^b   Mauch, Klaus ^b   Müller, Dirk ^b   Balsa Canto, Eva ^a   Schmid, Joachim ^b   Banga, Julio R ^a  

^a INSTITUTO DE INVESTIGACIONES MARINAS   (Spain)

^b INSILICO Biotechnology GmbH   (Germany)

Author keywords

Cell line engineering; Consensus; Dynamic modelling; Ensemble modelling; Metabolic engineering; Systems biology

Indexed keywords

BIOLOGY; CELL CULTURE; CELL ENGINEERING; COMPLEX NETWORKS; ESTIMATION; FORECASTING; METABOLIC ENGINEERING; METABOLISM; REACTION RATES; RECOMBINANT PROTEINS;

BIOCHEMICAL REACTION RATE; BIOTECHNOLOGICAL APPLICATIONS; CELL LINES; CHINESE HAMSTER OVARY CELLS; CONSENSUS; LARGE-SCALE DYNAMIC MODELS; RECOMBINANT PROTEIN PRODUCTIONS; SYSTEMS BIOLOGY;

DYNAMIC MODELS;

RECOMBINANT PROTEIN;

ANIMAL CELL; ARTICLE; BIOLOGICAL MODEL; CONSENSUS; NONHUMAN; PREDICTION; RELIABILITY; SYSTEMS BIOLOGY; TIME SERIES ANALYSIS; ANIMAL; BIOLOGY; BIOSYNTHESIS; CHO CELL LINE; CRICETULUS; FERMENTATION; HAMSTER; KINETICS; METABOLISM; REPRODUCIBILITY;

ANIMALS; CHO CELLS; COMPUTATIONAL BIOLOGY; CRICETINAE; CRICETULUS; FERMENTATION; KINETICS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; RECOMBINANT PROTEINS; REPRODUCIBILITY OF RESULTS; SYSTEMS BIOLOGY;

EID: 84924607090     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2015.02.001     Document Type: Article

References (28)

1. Kremling A., Saez-Rodriguez J. Systems biology-an engineering perspective. J. Biotechnol. 2007, 129:329-351.
2. Antony P., Balling R., Vlassis N. From systems biology to systems biomedicine. Curr. Opin. Biotechnol. 2012, 23(4):604-608.
3. Medina M.Á. Systems biology for molecular life sciences and its impact in biomedicine. Cell. Mol. Life Sci. 2013, 70(6):1035-1053. 10.1007/s00018-012-1109-z.
4. De Maria C., Grassini D., Vozzi F., Vinci B., Landi A., Ahluwalia A., Vozzi G. HEMET: mathematical model of biochemical pathways for simulation and prediction of HEpatocyte METabolism. Comput. Methods Progr. Biomed. 2008, 92:121-134. 10.1016/j.cmpb.2008.06.004.
5. Zakharov M.N., Bhasin S., Szafran A.T., Mancini M.A., Jasuja R. Numerical framework to model temporally resolved multi-stage dynamic systems. Comput. Methods Progr. Biomed. 2012, 108:750-759. 10.1016/j.cmpb.2012.04.010.
6. Carinhas N., Oliveira R., Alves P.M., Carrondo M.J.T., Teixeira A.P. Systems biotechnology of animal cells: the road to prediction. Trends Biotechnol. 2012, 30(7):377-385.
7. Hattersley J.G., Pérez-Velázquez J., Chappell M.J., Bearup D., Roper D., Dowson C., Bugg T., Evans N.D. Indistinguishability and identifiability of kinetic models for the MurC reaction in peptidoglycan biosynthesis. Comput. Methods Progr. Biomed. 2011, 104(2):70-80. 10.1016/j.cmpb.2010.07.009.
8. Villaverde A.F., Banga J.R. Reverse engineering and identification in systems biology: strategies, perspectives and challenges. J. R. Soc. Interface 2014, 11:20130505.
9. Sontag E. For differential equations with r parameters, 2r+1 experiments are enough for identification. J. Nonlinear Sci. 2002, 12:553-583.
10. Kaltenbach H.M., Dimopoulos S., Stelling J. Systems analysis of cellular networks under uncertainty. FEBS Lett. 2009, 583(24):3923-3930.
11. Parker W.S. Ensemble modeling, uncertainty and robust predictions. WIREs Clim. Change 2013, 4(3):213-223.
12. Smith T., Ross A., Maire N., Chitnis N., Studer A., Hardy D., Brooks A., Penny M., Tanner M. Ensemble modeling of the likely public health impact of a pre-erythrocytic malaria vaccine. PLoS Med. 2012, 9(1):e1001157.
13. Kauffman S. A proposal for using the ensemble approach to understand genetic regulatory networks. J. Theor. Biol. 2004, 230(4):581-590.
14. Tran L.M., Rizk L.M., Liao J.C. Ensemble modeling of metabolic networks. Biophys. J. 2008, 95(12):5606-5617.
15. Kuepfer L., Peter M., Sauer U., Stelling J. Ensemble modeling for analysis of cell signaling dynamics. Nat. Biotechnol. 2007, 25:1001-1006.
16. Tan Y., Liao J.C. Metabolic ensemble modeling for strain engineers. Biotechnol. J. 2012, 7:343-353.
17. Bever C. Selecting High-Confidence Predictions from Ordinary Differential Equation Models of Biological Networks 2008, (Ph.D. thesis), MIT.
18. Cedersund G. Conclusions via unique predictions obtained despite unidentifiability - new definitions and a general method. FEBS J. 2012, 279(18):3513-3527.
19. Villaverde A.F., Bongard S., Mauch K., Müller D., Balsa-Canto E., Schmid J., Banga J.R. High-confidence predictions in systems biology dynamic models. 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014) 2014, 161-171. Springer International Publishing.
20. Chassagnole C., Noisommit N., Schmid J.W., Mauch K., Reuss M. Dynamic modeling of the central carbon metabolism of E. coli. Biotechnol. Bioeng. 2002, 79(1):53-73.
21. Visser D., Heijnen J.J. Dynamic simulation and metabolic redesign of a branched pathway using linlog kinetics. Metab. Eng. 2003, 5(3):164-176.
22. Egea J.A., Martí R., Banga J.R. An evolutionary method for complex process optimization. Comp. Oper. Res. 2010, 37(2):315-324.
23. Villaverde A.F., Ross J., Morán F., Balsa-Canto E., Banga J.R. Use of a generalized Fisher equation for global optimization in chemical kinetics. J. Phys. Chem. A 2011, 115(30):8426-8436.
24. Villaverde A.F., Egea J.A., Banga J.R. A cooperative strategy for parameter estimation in large scale systems biology models. BMC Syst. Biol. 2012, 6:75.
25. Balsa-Canto E., Banga J.R. AMIGO, a toolbox for advanced model identification in systems biology using global optimization. Bioinformatics 2011, 27(16):2311-2313.
26. Pinho J., Sobral J.L., Rocha M. Parallel evolutionary computation in bioinformatics applications. Comput. Methods Progr. Biomed. 2013, 110(2):183-191. 10.1016/j.cmpb.2012.10.001.
27. Ahn W.S., Antoniewicz M.R. Towards dynamic metabolic flux analysis in CHO cell cultures. Biotechnol. J. 2012, 7(1):61-74.
28. Villaverde A.F., Henriques D., Smallbone K., Bongard S., Schmid J., Cicin-Sain D., Crombach A., Saez-Rodriguez J., Mauch K., Balsa-Canto E., Mendes P., Jäger J., Banga J.R. BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology. BMC Syst. Biol. 2015, (in press).