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Nägele T, Fürtauer L, Nagler M, et al. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information. Front Mol Biosci. 2016;3:6doi: 10.3389/fmolb.2016.00006.
Nägele, T., Fürtauer, L., Nagler, M., Weiszmann, J., & Weckwerth, W. (2016). A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information. Frontiers in molecular biosciences, 36. https://doi.org/10.3389/fmolb.2016.00006
Nägele, Thomas, et al. "A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information." Frontiers in molecular biosciences vol. 3 (2016): 6. doi: https://doi.org/10.3389/fmolb.2016.00006
Nägele T, Fürtauer L, Nagler M, Weiszmann J, Weckwerth W. A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information. Front Mol Biosci. 2016 Mar 07;3:6. doi: 10.3389/fmolb.2016.00006. eCollection 2016. PMID: 27014700; PMCID: PMC4779852.
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Front Mol Biosci. 2016 Mar 07;3:6. doi: 10.3389/fmolb.2016.00006. eCollection 2016.

A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information.

Frontiers in molecular biosciences

Thomas Nägele, Lisa Fürtauer, Matthias Nagler, Jakob Weiszmann, Wolfram Weckwerth

Affiliations

  1. Department of Ecogenomics and Systems Biology, University of ViennaVienna, Austria; Vienna Metabolomics Center, University of ViennaVienna, Austria.
  2. Department of Ecogenomics and Systems Biology, University of Vienna Vienna, Austria.

PMID: 27014700 PMCID: PMC4779852 DOI: 10.3389/fmolb.2016.00006

Abstract

The functional connection of experimental metabolic time series data with biochemical network information is an important, yet complex, issue in systems biology. Frequently, experimental analysis of diurnal, circadian, or developmental dynamics of metabolism results in a comprehensive and multidimensional data matrix comprising information about metabolite concentrations, protein levels, and/or enzyme activities. While, irrespective of the type of organism, the experimental high-throughput analysis of the transcriptome, proteome, and metabolome has become a common part of many systems biological studies, functional data integration in a biochemical and physiological context is still challenging. Here, an approach is presented which addresses the functional connection of experimental time series data with biochemical network information which can be inferred, for example, from a metabolic network reconstruction. Based on a time-continuous and variance-weighted regression analysis of experimental data, metabolic functions, i.e., first-order derivatives of metabolite concentrations, were related to time-dependent changes in other biochemically relevant metabolic functions, i.e., second-order derivatives of metabolite concentrations. This finally revealed time points of perturbed dependencies in metabolic functions indicating a modified biochemical interaction. The approach was validated using previously published experimental data on a diurnal time course of metabolite levels, enzyme activities, and metabolic flux simulations. To support and ease the presented approach of functional time series analysis, a graphical user interface including a test data set and a manual is provided which can be run within the numerical software environment Matlab®.

Keywords: data integration; metabolic network; metabolomics; network dynamics; systems biology; time series analysis

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