ArticlePLoS computational biology2019
Inferring reaction network structure from single-cell, multiplex data, using toric systems theory.
Article in PLoS computational biology, 2019. 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.
- Maximum likelihood estimation of log-affine models using detailed-balanced reaction networks.Journal of mathematical biology · 2025Article
- Recovering biomolecular network dynamics from single-cell omics data requires three time points.NPJ systems biology and applications · 2024Article
- Article
- Algebra, Geometry and Topology of ERK Kinetics.Bulletin of mathematical biology · 2022Article
- Single cell metabolism: current and future trends.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- Data-driven network models for genetic circuits from time-series data with incomplete measurements.Journal of the Royal Society, Interface · 2021Article
- Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Unsupervised logic-based mechanism inference for network-driven biological processes.PLoS computational biology · 2021Article
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4 authors.
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Abstract
The goal of many single-cell studies on eukaryotic cells is to gain insight into the biochemical reactions that control cell fate and state. In this paper we introduce the concept of Effective Stoichiometric Spaces (ESS) to guide the reconstruction of biochemical networks from multiplexed, fixed time-point, single-cell data. In contrast to methods based solely on statistical models of data, the ESS method leverages the power of the geometric theory of toric varieties to begin unraveling the structure of chemical reaction networks (CRN). This application of toric theory enables a data-driven mapping of covariance relationships in single-cell measurements into stoichiometric information, one in which each cell subpopulation has its associated ESS interpreted in terms of CRN theory. In the development of ESS we reframe certain aspects of the theory of CRN to better match data analysis. As an application of our approach we process cytomery- and image-based single-cell datasets and identify differences in cells treated with kinase inhibitors. Our approach is directly applicable to data acquired using readily accessible experimental methods such as Fluorescence Activated Cell Sorting (FACS) and multiplex immunofluorescence.
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