Inferring gene regulation dynamics from static snapshots of gene expression variability
Abstract
Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from single-cell sequencing or flow-cytometry experiments into regulatory dynamics. We show how static population snapshots of covariability can be exploited to rigorously infer properties of gene expression dynamics when gene expression reporters probe their upstream dynamics on separate timescales. This can be experimentally exploited in dual-reporter experiments with fluorescent proteins of unequal maturation times, thus turning an experimental bug into an analysis feature. We derive correlation conditions that detect the presence of closed-loop feedback regulation in gene regulatory networks. Furthermore, we show how genes with cell-cycle-dependent transcription rates can be identified from the variability of coregulated fluorescent proteins. Similar correlation constraints might prove useful in other areas of science in which static correlation snapshots are used to infer causal connections between dynamically interacting components.
Additional Information
© 2021 American Physical Society. (Received 26 November 2020; revised 25 May 2021; accepted 27 August 2021; published 13 October 2021) We thank Raymond Fan, Brayden Kell, Seshu Iyengar, Timon Wittenstein, Sid Goyal, Ran Kafri, and Josh Milstein for many helpful discussions. We thank Laurent Potvin-Trottier and Nathan Lord for valuable feedback on the manuscript. This work was supported by the Natural Sciences and Engineering Research Council of Canada and a New Researcher Award from the University of Toronto Connaught Fund. A.H. gratefully acknowledges funding through Grant No. NSF-1517372 while in Johan Paulsson's group at Harvard Medical School.Attached Files
Published - PhysRevE.104.044406.pdf
Accepted Version - 2109.00392.pdf
Supplemental Material - Supplemental-Material.pdf
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Additional details
- Eprint ID
- 111452
- Resolver ID
- CaltechAUTHORS:20211014-212144085
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- University of Toronto
- DMS-1517372
- NSF
- Created
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2021-10-18Created from EPrint's datestamp field
- Updated
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2021-10-18Created from EPrint's last_modified field