Identification of a Metabolic Reaction Network from Time-Series Data of Metabolite Concentrations

Abstract

Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg–Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.

Thermal stability for the effective use of commercial catalase

“…The calculation of k D and E D using the logarithmic form arouses much controversy 20-23 with regard to the strong correlation between the parameters k D and E D . To decrease the correlation among the parameters in Equation (6), the reference temperature T ref 22 is introduced and Equation (6) takes the following form: 7Taking into account Equation 7, the change of activity of the enzyme can be described by the following expression: Based on Equation (8) the values of k D,Tref and E D were found using nonlinear regression -the Levenberg-Marquardt procedure [24][25][26][27] . It is a standard technique used for solving nonlinear equations by the least squares method and is the most popular alternative to the Gauss-Newton method for fi nding the minimum of a function that is a sum of the squares: (9) where: (a exp ) i -enzyme activity, as determined experimentally, -enzyme activity, as calculated from Equation (8).…”

Peer Review #3 of "13C based proteinogenic amino acid (PAA) and metabolic flux ratio analysis of Lactococcus lactis reveals changes in pentose phosphate (PP) pathway in response to agitation and temperature related stresses (v0.1)"

“…The mechanisms underlying metabolic fluxes are of great interest because they can provide better interpretation of an observed phenotype (Weindl et al 2016). Subsequently, the control of metabolic fluxes allows better design of metabolic systems (Sriyudthsak et al 2013).…”

Shedding metabo‘light’ on the search for sepsis biomarkers

“…In a recent article published in Critical Care , Kamisoglu and colleagues [ 1 ] used metabolomics to assess whether responses elicited by endotoxin recapitulate, at least in part, those seen in clinical sepsis [ 2 ]. The study is primarily a retrospective in silico analysis of metabolomes obtained from subjects who participated in an experimental endotoxemia study [ 3 ] and from patients enrolled in the Community Acquired Pneumonia and Sepsis Outcome and Diagnostics (CAPSOD) study who after independent audit fulfilled criteria for sepsis and outcomes [ 4 ]. Patients in the CAPSOD cohort were classified as uncomplicated sepsis, severe sepsis, septic shock, and non-infected systemic inflammatory response syndrome (‘ill’ controls with non-infectious SIRS).…”

“…Metabolomics is heavily supported by mass spectrometry (MS) and nuclear magnetic resonance (NMR) as parallel technologies that provide an overview of the complete set of small-molecule chemicals found within a biological sample (metabolome) [ 4 ]. The main advantage of MS is sensitivity - it can detect analytes routinely in the femtomolar to attomolar range.…”

“…Therefore, a single analytical tool is unlikely to detect all possible metabolites, suggesting a combination of techniques will be required to assign metabolites and patients to specific classes [ 3 ]. Moreover, of all the systems biology disciplines, metabolomics is closest to the phenotype, is profoundly affected by environmental factors, and dynamic changes suggest selection of appropriate time points for biomarker identification will be critical [ 4 , 7 ].…”