Table2_Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism.XLS

<p>The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.</p

Image3_Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism.TIF

<p>The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.</p

Table1_Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism.XLS

<p>The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.</p

Image1_Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism.TIF

<p>The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.</p

Comparison of models extracted by the four evaluated contending methods: Mean values across contexts.

<p>Global characteristics of the models are derived by applying RegrEx (with automated determination of λ), RegrEx-λ₀ (<i>i</i>.<i>e</i>., RegrEx without regularization), Lee2012, iMAT and FastCORE. The abbreviations stand for the following: <i></i></p><p></p><p></p><p></p><p><i><mi>C</mi><mi>a</mi><mi>r</mi><mi>d</mi><mo>.</mo></i></p><i><mo>-</mo><p></p><p></p><p></p></i> denotes mean cardinality, <p></p><p></p><p></p><p><mi>O</mi></p><mo>-</mo><p></p><p></p><p></p>_<i>R</i>, mean data-orphan ratio,<p></p><p><mi> </mi></p><p></p><p><mi>ρ</mi></p><mo>-</mo><p></p><p></p><p></p>_{<i>(V</i>,<i>D)</i>}, mean correlation between data and predicted flux values, <p></p><p></p><p></p><p><mi>R</mi></p><mo>-</mo><p></p><p></p><p></p>_{<i>(V</i>,<i>D)</i>}, mean residual value between fluxes and data <p></p><p><mo>,</mo><mi> </mi></p><p></p><p><mi>I</mi></p><mo>-</mo><p></p><p></p><p></p>_<i>J</i>, mean Jaccard index to any other context, <i>Shared</i>, number of shared reactions across all contexts, and <i>Total Exclusive</i> represents total number of exclusively context-specific reactions across all contexts. Values in round brackets correspond to the standard deviation.<p></p

Alternative optima of CorEx and FastCORE context-specific network extractions.

<p>The results are divided into the leaf-specific scenario for the CorEx (A) and FastCORE (B) alternative optima, and the liver-specific scenario, for CorEx (C), FastCORE (D) and CORDA without applying the metabolic test (E) and applying the metabolic test (F) to further constraint the alternative optima space (see main text). In all cases, non-core reactions are partitioned into the set that is always included in all alternative networks, (the fixed non-core set, in green), the set that is always excluded (excluded non-core, grey) and the variable non-core set (yellow) which is formed by reactions that are included in some of the alternative networks. In both, the leaf- and the liver-specific scenario, the alternative optima networks generated by CorEx contain a larger proportion of fixed non-core reactions and a smaller proportion of variable non-core reactions. These differences in behavior may be explained by the greater number of non-core reactions that are added by FastCORE, as compare to CorEx, in the optimal solution (see main text).</p

Summary of the alternative optima space of the evaluated network-centered methods.

<p>Summary of the alternative optima space of the evaluated network-centered methods.</p

MFC value distribution across contexts for selected subsystems in Recon 2.

<p>Bile acid (bio)synthesis (A), vitamin B2 metabolism (B, equivalent to riboflavin metabolism in Recon 1) and D-alanine metabolism (C) are represented in all contexts in Recon 2. Cytochrome metabolism (D, equivalent to CYP in Recon 1) is represented only in two contexts, Lung and Colon, in Recon 2. In all cases, the first number preceding the name of the subsystem corresponds to its position in the ranking generated by the CV values, which are shown in round brackets here. Context names are displayed in the color bar aside. See main text for details.</p

Dendrogram clustering the evaluated methods and comparison of data- and model-derived z-scores quantifying the differences between contexts.

<p>(A) The dendrogram is obtained from the the Jaccard similarity that models have across the different methods. Two main clusters are formed, iMAT and FastCORE on one side, and Lee2012, RegrEx-λ₀ and RegrEx on the other. In the second cluster, RegrEx and RegrEx-λ₀ form a subcluster. (B-F) data- and model-derived z-scores are compared for RegrEx, RegrEx-λ₀, Lee2012, iMAT and FastCORE, respectively. Correlation values between the two series (data and model) are shown in the right upper corner in each case. <i>Adi</i>.:Adipose, <i>Col</i>.:Colon, <i>Hea</i>.:Heart, <i>Kid</i>.:Kidney, <i>Liv</i>.:Liver, <i>Lun</i>.:Lung, <i>Ova</i>.:Ovary, <i>S</i>.<i>Mus</i>.:Skeletal Muscle, <i>Spl</i>.:Spleen, <i>Tes</i>.:Testes.</p

Summary of the alternative optima space of RegrEx_LAD for two case studies, leaf and liver, and four values for the parameter λ.

<p>Summary of the alternative optima space of RegrEx_LAD for two case studies, leaf and liver, and four values for the parameter λ.</p