Additional file 1: of MICOP: Maximal information coefficient-based oscillation prediction to detect biological rhythms in proteomics data

Wide range comparison of MCC values of MICOP, ARS, JTK, and LS for decaying data. Sampling interval and noise level were gradually adjusted. The bar indicates MCC values (1 indicates a perfect prediction, 0 indicates a random prediction, and − 1 indicates a prediction in complete disagreement). (PDF 75 kb

Additional file 2: of MICOP: Maximal information coefficient-based oscillation prediction to detect biological rhythms in proteomics data

Wide-range comparison of MCC values of MICOP, ARS, JTK, and LS for non-decaying data. Sampling interval and noise level were gradually adjusted. The bar indicates MCC values (1 indicates a perfect prediction, 0 indicates a random prediction, and − 1 indicates a prediction in complete disagreement). (PDF 75 kb

Additional file 2: of Robust volcano plot: identification of differential metabolites in the presence of outliers

Performance evaluation of the proposed technique compared to other techniques using ROC curves and MER and AUC values for the artificial datasets in the absence and presence of outliers. Figure S1. Performance evaluation using ROC curves for different differential metabolite identification techniques (a) in the absence of outliers, (b) with 5% outliers, (c) with 10% outliers, (d) with 15% outliers, (e) with 20% outliers, and (f) with 25% outliers. Figure S2. Performance evaluation using box plots of 500 MERs for different differential metabolite identification techniques (a) in the absence of outliers, (b) with 5% outliers, (c) with 10% outliers, (d) with 15% outliers, (e) with 20% outliers, and (f) with 25% outliers. Figure S3. Performance evaluation using box plots of 500 AUC values for different differential metabolite identification techniques (a) in the absence of outliers, (b) with 5% outliers, (c) with 10% outliers, (d) with 15% outliers, (e) with 20% outliers, and (f) 25% outliers. Figure S4. Performance evaluation using Venn diagrams for the number of differential metabolites identified by different differential metabolite identification methods for the experimental dataset. (DOC 6677 kb

Additional file 3: of MICOP: Maximal information coefficient-based oscillation prediction to detect biological rhythms in proteomics data

Monte-Carlo simulation to calculate P-values. MIC values were calculated between random numbers. The x-axis indicates sample number (N time points) and the y-axis indicates MIC. The error bar indicates the standard deviation (N = 1000). The red color represents random values and the blue color represents the significance threshold (5%). (PDF 68 kb

DataSheet1_Simulation of the crosstalk between glucose and acetaminophen metabolism in a liver zonation model.pdf

The liver metabolizes a variety of substances that sometimes interact and regulate each other. The modeling of a single cell or a single metabolic pathway does not represent the complexity of the organ, including metabolic zonation (heterogeneity of functions) along with liver sinusoids. Here, we integrated multiple metabolic pathways into a single numerical liver zonation model, including drug and glucose metabolism. The model simulated the time-course of metabolite concentrations by the combination of dynamic simulation and metabolic flux analysis and successfully reproduced metabolic zonation and localized hepatotoxicity induced by acetaminophen (APAP). Drug metabolism was affected by nutritional status as the glucuronidation reaction rate changed. Moreover, sensitivity analysis suggested that the reported metabolic characteristics of obese adults and healthy infants in glucose metabolism could be associated with the metabolic features of those in drug metabolism. High activities of phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphate phosphatase in obese adults led to increased APAP oxidation by cytochrome P450 2E1. In contrast, the high activity of glycogen synthase and low activities of PEPCK and glycogen phosphorylase in healthy infants led to low glucuronidation and high sulfation rates of APAP. In summary, this model showed the effects of glucose metabolism on drug metabolism by integrating multiple pathways into a single liver metabolic zonation model.</p

Effect of masticatory stimulation on the quantity and quality of saliva and the salivary metabolomic profile - Fig 4

<p>Correlation between metabolite concentration of salivary volume for unstimulated <b>(A)</b> and stimulated <b>(B)</b> saliva. The relationship between metabolite concentration and salivary volume for the metabolites was significantly correlated (<i>P</i>-value<0.05, Spearman ranked correlation), and is visualized in small panels. Regression lines show the overall trends.</p

Ranking of pathways and related correlation between unstimulated and stimulated saliva samples.

<p>Ranking of pathways and related correlation between unstimulated and stimulated saliva samples.</p

The metabolic pathways that ranked first in the pathway analysis, and using only metabolites, showed significant differences (<i>P</i><0.05, Wilcoxon matched pairs test, corrected by false discovery rate) between unstimulated and stimulated saliva samples.

<p>The red bar indicates the average concentrations of unstimulated (left) and stimulated (right) saliva. To clearly visualize the data, these data were divided by the average of unstimulated saliva.</p

Loading plots of principal component analysis (PCA).

<p>Loading plots of principal component analysis (PCA).</p

Additional file 1: of Circulating cell-free DNA-based epigenetic assay can detect early breast cancer

This file provides detailed materials and methods for this study. (DOCX 66 kb