Charting cellular differentiation trajectories with Ricci flow
Charting cellular differentiation trajectories with Ricci flow
Complex biological processes, such as cellular differentiation, require an intricate rewiring of intra-cellular signalling networks. Previous characterisations of these networks revealed that promiscuity in signalling, quantified by a raised network entropy, underlies a less differentiated and malignant cell state. A theoretical connection between entropy and Ricci curvature has led to applications of discrete curvatures to characterise biological signalling networks at distinct time points during differentiation and malignancy. However, understanding and predicting the dynamics of biological network rewiring remains an open problem. Here we construct a framework to apply discrete Ricci curvature and Ricci flow to the problem of biological network rewiring. By investigating the relationship between network entropy and Forman-Ricci curvature, both theoretically and empirically on single-cell RNA-sequencing data, we demonstrate that the two measures do not always positively correlate, as has been previously suggested, and provide complementary rather than interchangeable information. We next employ discrete normalised Ricci flow, to derive network rewiring trajectories from transcriptomes of stem cells to differentiated cells, which accurately predict true intermediate time points of gene expression time courses. In summary, we present a differential geometry toolkit for investigation of dynamic network rewiring during cellular differentiation and cancer.
Baptista, Anthony
3ca93c8d-b361-4b16-ab36-2b08df637251
MacArthur, Ben D.
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Banerji, Christopher R.S.
1f2450d6-5772-46b5-a913-2333f7b53a2a
13 March 2024
Baptista, Anthony
3ca93c8d-b361-4b16-ab36-2b08df637251
MacArthur, Ben D.
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Banerji, Christopher R.S.
1f2450d6-5772-46b5-a913-2333f7b53a2a
Baptista, Anthony, MacArthur, Ben D. and Banerji, Christopher R.S.
(2024)
Charting cellular differentiation trajectories with Ricci flow.
Nature Communications, 15, [2258].
(doi:10.1101/2023.07.20.549833).
Abstract
Complex biological processes, such as cellular differentiation, require an intricate rewiring of intra-cellular signalling networks. Previous characterisations of these networks revealed that promiscuity in signalling, quantified by a raised network entropy, underlies a less differentiated and malignant cell state. A theoretical connection between entropy and Ricci curvature has led to applications of discrete curvatures to characterise biological signalling networks at distinct time points during differentiation and malignancy. However, understanding and predicting the dynamics of biological network rewiring remains an open problem. Here we construct a framework to apply discrete Ricci curvature and Ricci flow to the problem of biological network rewiring. By investigating the relationship between network entropy and Forman-Ricci curvature, both theoretically and empirically on single-cell RNA-sequencing data, we demonstrate that the two measures do not always positively correlate, as has been previously suggested, and provide complementary rather than interchangeable information. We next employ discrete normalised Ricci flow, to derive network rewiring trajectories from transcriptomes of stem cells to differentiated cells, which accurately predict true intermediate time points of gene expression time courses. In summary, we present a differential geometry toolkit for investigation of dynamic network rewiring during cellular differentiation and cancer.
Text
2023.07.20.549833v1.full
- Accepted Manuscript
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s41467-024-45889-6
- Version of Record
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Accepted/In Press date: 6 February 2024
Published date: 13 March 2024
Identifiers
Local EPrints ID: 487618
URI: http://eprints.soton.ac.uk/id/eprint/487618
ISSN: 2041-1723
PURE UUID: 6ae37c2b-08c2-4e3d-88df-7b72884afcea
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Date deposited: 29 Feb 2024 17:38
Last modified: 10 Aug 2024 01:37
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Author:
Anthony Baptista
Author:
Christopher R.S. Banerji
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