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Dynamic gene regulatory network inference from single-cell data using optimal transport.

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Titel: Dynamic gene regulatory network inference from single-cell data using optimal transport.
Autoren: Lamoline F; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg., Haasler I; Department of Information Technology, Uppsala University, Uppsala, 751 05, Sweden.; Department of Mathematics, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden., Karlsson J; Department of Mathematics, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden., Gonçalves J; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg.; Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, United Kingdom., Aalto A; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg.; Department of Cancer Research, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg.
Quelle: Bioinformatics (Oxford, England) [Bioinformatics] 2025 Aug 02; Vol. 41 (8).
Publikationsart: Journal Article
Sprache: English
Info zur Zeitschrift: Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s): Original Publication: Oxford : Oxford University Press, c1998-
MeSH-Schlagworte: Gene Regulatory Networks* , Single-Cell Analysis*/methods , Computational Biology*/methods, Parkinson Disease/genetics ; Humans ; Algorithms ; Software ; Systems Biology/methods
Abstract: Motivation: Modelling gene expression is a central problem in systems biology. Single-cell technologies have revolutionized the field by enabling sequencing at the resolution of individual cells. This results in a much richer data compared to what is obtained by bulk technologies, offering new possibilities and challenges for gene regulatory network inference.
Results: In this work, we introduce GRIT (gene regulation inference by transport)-a method to fit a differential equation model and to infer gene regulatory networks from single-cell data using the theory of optimal transport. The idea consists in tracking the evolution of the cell distribution over time and finding the system whose temporal marginals minimize the transport cost with the observations. GRIT is finally used to identify genes and pathways affected by two Parkinson's disease associated mutations.
Availability and Implementation: Matlab implementation of the method and code for data generation are at gitlab.com/uniluxembourg/lcsb/systems-control/grit together with a user guide. A snapshot of the code used for the results of this article is at doi: 10.5281/zenodo.15582432.
(© The Author(s) 2025. Published by Oxford University Press.)
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Grant Information: CORE19/13684479/DynCell Luxembourg National Research Fund
Entry Date(s): Date Created: 20250712 Date Completed: 20250825 Latest Revision: 20250825
Update Code: 20250827
PubMed Central ID: PMC12352743
DOI: 10.1093/bioinformatics/btaf394
PMID: 40650986
Datenbank: MEDLINE
Beschreibung
Abstract:Motivation: Modelling gene expression is a central problem in systems biology. Single-cell technologies have revolutionized the field by enabling sequencing at the resolution of individual cells. This results in a much richer data compared to what is obtained by bulk technologies, offering new possibilities and challenges for gene regulatory network inference.<br />Results: In this work, we introduce GRIT (gene regulation inference by transport)-a method to fit a differential equation model and to infer gene regulatory networks from single-cell data using the theory of optimal transport. The idea consists in tracking the evolution of the cell distribution over time and finding the system whose temporal marginals minimize the transport cost with the observations. GRIT is finally used to identify genes and pathways affected by two Parkinson's disease associated mutations.<br />Availability and Implementation: Matlab implementation of the method and code for data generation are at gitlab.com/uniluxembourg/lcsb/systems-control/grit together with a user guide. A snapshot of the code used for the results of this article is at doi: 10.5281/zenodo.15582432.<br /> (© The Author(s) 2025. Published by Oxford University Press.)
ISSN:1367-4811
DOI:10.1093/bioinformatics/btaf394