ArticlePloS one2014
Solving the differential biochemical Jacobian from metabolomics covariance data.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging.NPJ systems biology and applications · 2025Trial
- Kernel-DMD for multiome data integration and control.PLoS computational biology · 2026Article
- Millets for a sustainable future.Journal of experimental botany · 2025Review
- Natural variation in the chickpea metabolome under drought stress.Plant biotechnology journal · 2024Article
- Network structure and fluctuation data improve inference of metabolic interaction strengths with the inverse Jacobian.NPJ systems biology and applications · 2024Article
- Metabolome plasticity in 241 Arabidopsis thaliana accessions reveals evolutionary cold adaptation processes.Plant physiology · 2023Article
- COVRECON: automated integration of genome- and metabolome-scale network reconstruction and data-driven inverse modeling of metabolic interaction networks.Bioinformatics (Oxford, England) · 2023Article
- A dynamical systems treatment of transcriptomic trajectories in hematopoiesis.Development (Cambridge, England) · 2023Article
- Metabolomics: a systems biology approach for enhancing heat stress tolerance in plants.Plant cell reports · 2022Review
- Dissecting Metabolism of Leaf Nodules inFrontiers in molecular biosciences · 2021Article
- Inverse Data-Driven Modeling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation.Cell reports · 2020Article
- Molecular Mechanisms of Microbial Survivability in Outer Space: A Systems Biology Approach.Frontiers in microbiology · 2020Review
- Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.Metabolites · 2018Review
- Quantitative Phosphoproteomic and System-Level Analysis of TOR Inhibition Unravel Distinct Organellar Acclimation inFrontiers in plant science · 2018Article
- Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field.Frontiers in plant science · 2018Article
- Combined Metabolomic Analysis of Plasma and Urine Reveals AHBA, Tryptophan and Serotonin Metabolism as Potential Risk Factors in Gestational Diabetes Mellitus (GDM).Frontiers in molecular biosciences · 2017Article
- Quantitative phosphoproteomics reveals the role of the AMPK plant ortholog SnRK1 as a metabolic master regulator under energy deprivation.Scientific reports · 2016Article
- Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data.Frontiers in molecular biosciences · 2016Review
- A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information.Frontiers in molecular biosciences · 2016Article
- SnRK1-triggered switch of bZIP63 dimerization mediates the low-energy response in plants.eLife · 2015Article
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6 authors.
Funding
Abstract
High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting complex data sets is lacking far behind. Here, we present a biomathematical method addressing this problem by using metabolomics data for the inverse calculation of a biochemical Jacobian matrix, thereby linking computer-based genome-scale metabolic reconstruction and in vivo metabolic dynamics. The incongruity of metabolome coverage by typical metabolite profiling approaches and genome-scale metabolic reconstruction was solved by the design of superpathways to define a metabolic interaction matrix. A differential biochemical Jacobian was calculated using an approach which links this metabolic interaction matrix and the covariance of metabolomics data satisfying a Lyapunov equation. The predictions of the differential Jacobian from real metabolomic data were found to be correct by testing the corresponding enzymatic activities. Moreover it is demonstrated that the predictions of the biochemical Jacobian matrix allow for the design of parameter optimization strategies for ODE-based kinetic models of the system. The presented concept combines dynamic modelling strategies with large-scale steady state profiling approaches without the explicit knowledge of individual kinetic parameters. In summary, the presented strategy allows for the identification of regulatory key processes in the biochemical network directly from metabolomics data and is a fundamental achievement for the functional interpretation of metabolomics data.
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