ArticleACS synthetic biology2015
Using dynamic noise propagation to infer causal regulatory relationships in biochemical networks.
Article in ACS synthetic biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division.Biophysical journal · 2023Article
- Network inference from short, noisy, low time-resolution, partial measurements: Application toProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.Frontiers in cell and developmental biology · 2023Article
- The circadian oscillator analysed at the single-transcript level.Molecular systems biology · 2021Article
- Reconstructing ecological networks with noisy dynamics.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- Article
- Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data.Scientific reports · 2018Article
- Article
- Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.Methods (San Diego, Calif.) · 2015Review
- The Low Noise Limit in Gene Expression.PloS one · 2015Article
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Authors and funding
4 authors.
Funding
Abstract
Cellular decision making is accomplished by complex networks, the structure of which has traditionally been inferred from mean gene expression data. In addition to mean data, quantitative measures of distributions across a population can be obtained using techniques such as flow cytometry that measure expression in single cells. The resulting distributions, which reflect a population's variability or noise, constitute a potentially rich source of information for network reconstruction. A significant portion of molecular noise in a biological process is propagated from the upstream regulators. This propagated component provides additional information about causal network connections. Here, we devise a procedure in which we exploit equations for dynamic noise propagation in a network under nonsteady state conditions to distinguish between alternate gene regulatory relationships. We test our approach in silico using data obtained from stochastic simulations as well as in vivo using experimental data collected from synthetic circuits constructed in yeast.
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Registered trials
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