Improved metabolite profile smoothing for flux estimation

2015 ◽  
Vol 11 (9) ◽  
pp. 2394-2405 ◽  
Author(s):  
Robert A. Dromms ◽  
Mark P. Styczynski

We develop several methods to improve the estimation of metabolite concentrations and accumulation fluxes from noisy time-course data, including use of a sigmoidal impulse function and a resampling-based approach.

1973 ◽  
Vol 136 (3) ◽  
pp. 455-465 ◽  
Author(s):  
J. M. Gunn ◽  
C. B. Taylor

1. The time-course for the induction of hepatic glucokinase, hexokinase, phosphofructokinase, liver-type and muscle-type pyruvate kinases in reponse to various diets and insulin has been investigated over the first 48h of change in both diabetic and non-diabetic rats. 2. The results are consistent with there being separate regulatory mechanisms for the induction of each of the three key enzymes, that is for glucokinase, phosphofructokinase and liver-type pyruvate kinase. 3. To investigate the possibility that induction of these enzymes is mediated through specific metabolites a full metabolite profile has been determined under conditions identical with those in the induction experiments and the results examined for correlations between metabolite concentrations and enzyme activities. 4. Several such relationships were detected and those between glucokinase activity and the phosphorylation state of the adenine nucleotides and between liver-type pyruvate kinase activity and the concentrations of dihydroxyacetone phosphate and pyruvate are discussed in relation to the concept of inducing metabolites. 5. It is suggested that the induction of glycolytic enzymes by insulin may be secondary to the changes in the concentration of specific hepatic metabolites brought about by the acute effects of the hormone. 6. The details of the metabolite concentrations in the various experimental states have been deposited as Supplementary Publication SUP 50021 at the British Library (Lending Division) (formerly the National Lending Library for Science and Technology), Boston Spa, Yorks. LS23 7BQ, U.K., from whom copies can be obtained on the terms indicated in Biochem. J. (1973), 131, 5.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.


2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.


2013 ◽  
Vol 104 (8) ◽  
pp. 1676-1684 ◽  
Author(s):  
Max Puckeridge ◽  
Bogdan E. Chapman ◽  
Arthur D. Conigrave ◽  
Stuart M. Grieve ◽  
Gemma A. Figtree ◽  
...  

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