ArticleJournal of mathematical biology2014
Identification of alterations in the Jacobian of biochemical reaction networks from steady state covariance data at two conditions.
Article in Journal of mathematical biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 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
- Network structure and fluctuation data improve inference of metabolic interaction strengths with the inverse Jacobian.NPJ systems biology and applications · 2024Article
- COVRECON: automated integration of genome- and metabolome-scale network reconstruction and data-driven inverse modeling of metabolic interaction networks.Bioinformatics (Oxford, England) · 2023Article
- Inverse Data-Driven Modeling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation.Cell reports · 2020Article
- Article
- A Strategy for Functional Interpretation of Metabolomic Time Series Data in Context of Metabolic Network Information.Frontiers in molecular biosciences · 2016Article
- Metabolic network discovery by top-down and bottom-up approaches and paths for reconciliation.Frontiers in bioengineering and biotechnology · 2014Review
- Fruit metabolite networks in engineered and non-engineered tomato genotypes reveal fluidity in a hormone and agroecosystem specific manner.Metabolomics : Official journal of the Metabolomic SocietyArticle
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Authors and funding
2 authors.
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Abstract
Model building of biochemical reaction networks typically involves experiments in which changes in the behavior due to natural or experimental perturbations are observed. Computational models of reaction networks are also used in a systems biology approach to study how transitions from a healthy to a diseased state result from changes in genetic or environmental conditions. In this paper we consider the nonlinear inverse problem of inferring information about the Jacobian of a Langevin type network model from covariance data of steady state concentrations associated to two different experimental conditions. Under idealized assumptions on the Langevin fluctuation matrices we prove that relative alterations in the network Jacobian can be uniquely identified when comparing the two data sets. Based on this result and the premise that alteration is locally confined to separable parts due to network modularity we suggest a computational approach using hybrid stochastic-deterministic optimization for the detection of perturbations in the network Jacobian using the sparsity promoting effect of [Formula: see text]-penalization. Our approach is illustrated by means of published metabolomic and signaling reaction networks.
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