Inferring gene regulation dynamics from static snapshots of gene expression variability

Joly-Smith, Euan; Wang, Zitong Jerry; Hilfinger, Andreas

doi:10.1103/physreve.104.044406

Cited by 3 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 1), allowing for growing and dividing cells, measurement noise, and fluctuations in degradation rates as would be relevant for its application to experimental single-cell data. Note that due to the possibility of feedback, the dynamics of X and Y is not generally symmetric even though X and Y are co-regulated [20], see Fig. S1A.…”

Section: Invariant Relation In the Absence Of Causal Interactionsmentioning

confidence: 99%

“…We propose a novel inference method to detect whether a gene X causally affects a gene Z which rests on a mathematical identity that constrains an entire class of models. We show how covariability measurements of molecular abundances can then be used to detect causal interactions by specifying only "local" aspects of the underlying gene expression dynamics [19][20][21].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Exploiting fluctuations in gene expression to detect causal interactions between genes

Joly-Smith,

Talpur,

Allard

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Characterizing and manipulating cellular behaviour requires a mechanistic understanding of the causal interactions between cellular components. We present an approach that can detect causal interactions between genes without the need to perturb the physiological state of cells. This approach exploits naturally occurring cell-to-cell variability which is experimentally accessible from static population snapshots of genetically identical cells without the need to follow cells over time. Our main contribution is a simple mathematical relation that constrains the propagation of gene expression noise through biochemical reaction networks. This relation allows us to rigorously interpret fluctuation data even when only a small part of a complex gene regulatory process can be observed. This relation can be exploited to detect causal interactions by synthetically engineering a passive reporter of gene expression, akin to the established “dual reporter assay”. While the focus of our contribution is theoretical, we also present an experimental proof-of-principle to illustrate the approach. Our data from synthetic gene regulatory networks inE. coliare not unequivocal but suggest that the method could prove useful in practice to identify causal interactions between genes from non-genetic cell-to-cell variability.

show abstract

Section: Invariant Relation In the Absence Of Causal Interactionsmentioning

confidence: 99%

mentioning

confidence: 99%

Exploiting fluctuations in gene expression to detect causal interactions between genes

Joly-Smith,

Talpur,

Allard

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Exploiting fluctuations in gene expression to detect causal interactions between genes

Joly-Smith,

Talpur,

Allard

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Characterizing and manipulating cellular behaviour requires a mechanistic understanding of the causal interactions between cellular components. We present an approach that can detect causal interactions between genes without the need to perturb the physiological state of cells. This approach exploits naturally occurring cell-to-cell variability which is experimentally accessible from static population snapshots of genetically identical cells without the need to follow cells over time. Our main contribution is a simple mathematical relation that constrains the propagation of gene expression noise through biochemical reaction networks. This relation allows us to rigorously interpret fluctuation data even when only a small part of a complex gene regulatory process can be observed. This relation can be exploited to detect causal interactions by synthetically engineering a passive reporter of gene expression, akin to the established “dual reporter assay”. While the focus of our contribution is theoretical, we also present an experimental proof-of-principle to illustrate the approach. Our data from synthetic gene regulatory networks in E. coli are not unequivocal but suggest that the method could prove useful in practice to identify causal interactions between genes from non-genetic cell-to-cell variability.

show abstract

Exploiting fluctuations in gene expression to detect causal interactions between genes

Joly-Smith,

Talpur,

Allard

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Characterizing and manipulating cellular behaviour requires a mechanistic understanding of the causal interactions between cellular components. We present an approach that can detect causal interactions between genes without the need to perturb the physiological state of cells. This approach exploits naturally occurring cell-to-cell variability which is experimentally accessible from static population snapshots of genetically identical cells without the need to follow cells over time. Our main contribution is a simple mathematical relation that constrains the propagation of gene expression noise through biochemical reaction networks. This relation allows us to rigorously interpret fluctuation data even when only a small part of a complex gene regulatory process can be observed. This relation can be exploited to detect causal interactions by synthetically engineering a passive reporter of gene expression, akin to the established “dual reporter assay”. While the focus of our contribution is theoretical, we also present an experimental proof-of-principle to illustrate the approach. Our data from synthetic gene regulatory networks in E. coli are not unequivocal but suggest that the method could prove useful in practice to identify causal interactions between genes from non-genetic cell-to-cell variability.

show abstract

Inferring gene regulation dynamics from static snapshots of gene expression variability

Cited by 3 publications

References 34 publications

Exploiting fluctuations in gene expression to detect causal interactions between genes

Exploiting fluctuations in gene expression to detect causal interactions between genes

Exploiting fluctuations in gene expression to detect causal interactions between genes

Exploiting fluctuations in gene expression to detect causal interactions between genes

Contact Info

Product

Resources

About