The greatest challenge when working with mathematical models describing biological processes is to choose appropriate values of kinetic parameters. This applies especially to the models of environmental biological processes, such as bioremediation. Parameter values are usually obtained from various sources and under specific experimental conditions and constitute sources of uncertainties. A useful tool for dealing with model parameter uncertainties is sensitivity analysis. It studies how a given model depends on its input factors. In this work, the nonstationary Extended Fourier Amplitude Sensitivity analysis was used to analyze the effect of 28 kinetic parameters on 18 metabolite concentrations and 16 reaction rates of the biological part of the integrated BTEX bioremediation model. First-order sensitivities and total effect indices were estimated for three time points of the process to identify the places of the metabolism control shift. Analyses revealed that the model is nonadditive and there are no strong interactions between the parameters. Results of this study indicate the importance of using the global sensitivity analysis before bioremediation process planning.
Get full access to this article
View all access options for this article.
References
1.
AlmquistJ., CvijovicM., HatzumanikatisV., NielsenJ., and JirstrandM. (2014). Kinetic models in industrial biotechnology-Improving cell factory performance. Metab. Eng. 24, 38.
2.
BenekosI.D., ShoemakerC.A., and StedingerJ.R. (2007). Probabilistic risk and uncertainty analysis for bioremediation of four chlorinated ethenes in groundwater. Stoch. Environ. Res. Risk. A.21, 375.
3.
ChanK., SaltelliA., and TarantolaS. (1997). Sensitivity analysis of model output: Variance-based methods make the difference. Presented at the 1997 Winter simulation conference, Atlanta, Georgia, USA, December 7–10.
4.
CosenzaA., ManninaG., VanrolleghemP.A., and NeumannM.B. (2014). Variance-based sensitivity analysis for wastewater treatment plant modelling. Sci. Total Environ. 470, 1068.
5.
EssaidH.I., BekinsB.A., GodsyE.M., WarrenE., BaedeckerM.J., and CozzarelliI.M. (1995). Simulation of aerobic and anaerobic biodegradation processes at a crude oil spill site. Water Resour. Res. 31, 3309.
6.
FangS., GertnerG.Z., ShinkarevaS., WangG., and AndersonA. (2003). Improved generalized Fourier amplitude sensetivity test (FAST) for model assessment. Stat. Comp. 13, 221.
7.
GengX., BoufadelM.C., and WrennB. (2013). Mathematical modeling of the biodegradation of residual hydrocarbon in a variably-saturated sand column. Biodegradation. 24, 153.
8.
GengX., BoufadelM., PersonnaY., LeeK., TsaoD., and DemiccoE. (2014). BioB: A mathematical model for the biodegradation of low solubility hydrocarbons. Mar. Pollut. Bull. 83, 138.
9.
GombertA.K., and NielsenJ. (2000). Mathematical modelling of metabolism. Curr. Opin. Biotech. 11, 180.
10.
GujerW., HenzeM., MinoT., and Van LoosdrechtM.C.M. (1999). Activated sludge model No. 3. Water Sci. Technol. 39, 183.
11.
HeltonJ.C., JohnsonJ.D., SallaberryC.J., and StorlieC.B. (2006). Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliab. Eng. Syst. Safe. 91, 1175.
12.
HenzeM., GradyC.P.L., GujerW., MaraisG.V.R., and MatsuoT. (1987). Activated sludge model No. 1. IAWPRC Scientific and Technical Report No. 1. IAWPRC, London.
13.
HenzeM., GujerW., MinoT., MatsuoT., WentzelM.C., and MaraisG.V.R. (1995). Activated sludge model No. 2. IAWO Scientific and Technical Report No. 3. IAWO, London.
14.
HenzeM., GujerW., MinoT., MatsuoT., WentzelM.C., MaraisG.V.R., and Van LoosdrechtM.C.M. (1999). Activated sludge model No. 2d, ASM2d. Water Sci. Technol. 39, 165.
15.
HommaT., and SaltelliA. (1996). Importance measures in global sensitivity analysis of nonlinear models. Reliab. Eng. Syst. Saf. 52, 1.
16.
IoossB., and LemaîtreP. (2015). A review on global sensitivity analysis methods. In MeloniC. and DellinoG., Eds., Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications. New York: Springer US, p.101.
17.
JiaG., StephanopoulosG., and GunawanR. (2012). Incremental parameter estimation of kinetic metabolic network models. BMC Syst. Biol. 6, 142.
18.
KaoC.M., and WangC.C. (2000). Control of BTEX migration by intrinsic bioremediation at a gasoline spill site. Water Res. 13, 3413.
19.
KentE., NeumannS., KummerU., and MendesP. (2013). What can we learn from global sensitivity analysis of biochemical systems?. PLoS One. 8, 1.
20.
LiK.Y., XuT., CawleyW.A., ColapretJ.A., BonnerJ.S., ErnestA., and VerramachaneniP.B. (1994). Field test and mathematical modelling of bioremediation of an oil-contaminated soil-Part 2: Mathematical modelling. Waste Manage. 14, 571.
21.
MaurerM., and RittmannB.E. (2004). Formulation of the CBC-model for modelling the contaminants and footprints in natural attenuation of BTEX. Biodegradation. 15, 419.
22.
NassarM.A. (2011) Multi-parametric sensitivity analysis of CCHE2D for channel flow simulations in Nile River. J. Hydro-Env. Eng. 5, 187.
23.
NicolJ.P., WiseW.R., MolzF.J., and BenefieldL.D. (1994). Modeling biodegradation of residual petroleum in a saturated porous column. Water Resour. Res. 30, 3313.
24.
PeskovK., MoqilevskayaE., and DeminO. (2012). Kinetic modelling of central carbon metabolism in Escherichia coli. FEBS J. 18, 3374.
25.
ReedM.C., ThomasR.L., PavisicJ., JamesS.J., UlrichC.M., and NijhoutH.F. (2008). A mathematical model of glutathione metabolism. Theor. Biol. Med. Model. 5, 8.
26.
SaltelliA., and BoladoR. (1998). An alternative way to compute Fourier amplitude sensitivity test (FAST). Comput. Stat. Data Anal. 26, 445.
27.
SaltelliA., AnnoniP., AzziniI., CampolongoF., RattoM., and TarantolaS. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun. 181, 259.
28.
SaltelliA., TarantolaS., and CampolongoF. (2000). Sensitivity analysis as an ingredient of modelling. Stat. Sci. 15, 377.
29.
SaltelliA., TarantolaS., and ChanK. (1999). A quantitative, model independent method for global sensitivity analysis of model output. Technometrics. 41, 39.
30.
SchirmerM., ButlerB.J., RoyJ.W., FrindE.O., and BarkerJ.F. (1999). A relative-least-squares technique to determine unique Monod kinetic parameters of BTEX compounds using batch experiments. J. Contam. Hydrol. 37, 69.
31.
SchirmerM., MolsonJ.W., FrindE.O., and BarkerJ.F. (2000). Biodegradation modelling of a dissolved gasoline plume applying independent laboratory and field parameters. J. Contam. Hydrol. 46, 339.
32.
SchwiegerV. (2004). Variance-based sensitivity analysis for model evaluation in engineering surveys. Presented at INGEO 2004 and FIG Regional Central and Eastern European Conference on Engineering Surveying, Bratislava, Slovakia, November 11.
33.
SurajudeenA. (2012). Mathematical modelling of bioremediation of soil contaminated with spent motor oil. J. Emerg. Trends Eng. Appl. Sci. 3, 654.
34.
VanlierJ., TiemannC.A., HilbersP.A.J., and van RielN.A.W. (2013). Parameter uncertainty in biochemical models described by ordinary differential equations. Math. Biosci. 246, 305.
35.
WaymanJ.A., and VarnerJ.D. (2013). Biological systems modelling of metabolic and signalling networks. Curr. Opin. Chem. Eng. 2, 365.
36.
WiechertW. (2002). Modelling and simulation: Tools for metabolic engineering. J. Biotech. 94, 37.
37.
YaghmaeiS. (2002). Mathematical modelling of natural in situ bioremediation to estimate initial contaminate concentration effect. IJE Trans. 2, 105.
38.
ZhaoJ., ScheibeT.D., and MahadevanR. (2011). Model based analysis of the role of biological, hydrological and geochemical factors affecting uranium bioremediation. Biotechnol. Bioeng. 108, 1537.
39.
ZhaoJ., ScheibelT.D., FangY., LovleyD.R., and MahadevanR. (2010). Modeling and sensitivity analysis of electron capacitance for Geobacter in sedimentary environments. J. Contam. Hydrol. 112, 30.
