TY - JOUR

T1 - A new parametric method to smooth time-series data of metabolites in metabolic networks

AU - Miyawaki, Atsuko

AU - Sriyudthsak, Kansuporn

AU - Hirai, Masami Yokota

AU - Shiraishi, Fumihide

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent estimation of model parameters. Here, an efficient parametric method is proposed for smoothing metabolite time-series data, and its performance is evaluated. To simplify parameter estimation, the method uses S-system-type equations with simple power law-type efflux terms. Iterative calculation using this method was found to readily converge, because parameters are estimated stepwise. Importantly, smoothing curves are determined so that metabolite concentrations satisfy mass balances. Furthermore, the slopes of smoothing curves are useful in estimating parameters, because they are probably close to their true behaviors regardless of errors that may be present in the actual data. Finally, calculations for each differential equation were found to converge in much less than one second if initial parameters are set at appropriate (guessed) values.

AB - Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent estimation of model parameters. Here, an efficient parametric method is proposed for smoothing metabolite time-series data, and its performance is evaluated. To simplify parameter estimation, the method uses S-system-type equations with simple power law-type efflux terms. Iterative calculation using this method was found to readily converge, because parameters are estimated stepwise. Importantly, smoothing curves are determined so that metabolite concentrations satisfy mass balances. Furthermore, the slopes of smoothing curves are useful in estimating parameters, because they are probably close to their true behaviors regardless of errors that may be present in the actual data. Finally, calculations for each differential equation were found to converge in much less than one second if initial parameters are set at appropriate (guessed) values.

UR - http://www.scopus.com/inward/record.url?scp=84991252526&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84991252526&partnerID=8YFLogxK

U2 - 10.1016/j.mbs.2016.09.011

DO - 10.1016/j.mbs.2016.09.011

M3 - Article

C2 - 27693302

AN - SCOPUS:84991252526

VL - 282

SP - 21

EP - 33

JO - Mathematical Biosciences

JF - Mathematical Biosciences

SN - 0025-5564

ER -