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Using dynamic noise propagation to infer causal regulatory relationships in biochemical networks.

Lipinski-Kruszka J, Stewart-Ornstein J, Chevalier MW, El-Samad H - ACS Synth Biol (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: ∥The California Institute for Quantitative Biosciences, San Francisco, California 94158, United States.

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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Mean might be insufficientto distinguish between alternate networktopologies. (A) Example of mean expression of two genes, A and B,and two alternate network topologies that fit these data equally well.Population variability (inset) provides additional information. (B)Sources of noise can be divided into two components: intrinsic noisedue to the stochastic nature of biochemical reactions, and propagatednoise from upstream regulators.
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fig1: Mean might be insufficientto distinguish between alternate networktopologies. (A) Example of mean expression of two genes, A and B,and two alternate network topologies that fit these data equally well.Population variability (inset) provides additional information. (B)Sources of noise can be divided into two components: intrinsic noisedue to the stochastic nature of biochemical reactions, and propagatednoise from upstream regulators.

Mentions: Learning of networks structures is typically accomplished usingbulk data describing the average response of a population. Althoughinformative, these data often fail to establish causal relationships,resulting in nonunique solutions where multiple different topologiescan represent the same data pool equally well (Figure 1A).2


Using dynamic noise propagation to infer causal regulatory relationships in biochemical networks.

Lipinski-Kruszka J, Stewart-Ornstein J, Chevalier MW, El-Samad H - ACS Synth Biol (2014)

Mean might be insufficientto distinguish between alternate networktopologies. (A) Example of mean expression of two genes, A and B,and two alternate network topologies that fit these data equally well.Population variability (inset) provides additional information. (B)Sources of noise can be divided into two components: intrinsic noisedue to the stochastic nature of biochemical reactions, and propagatednoise from upstream regulators.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4384829&req=5

fig1: Mean might be insufficientto distinguish between alternate networktopologies. (A) Example of mean expression of two genes, A and B,and two alternate network topologies that fit these data equally well.Population variability (inset) provides additional information. (B)Sources of noise can be divided into two components: intrinsic noisedue to the stochastic nature of biochemical reactions, and propagatednoise from upstream regulators.
Mentions: Learning of networks structures is typically accomplished usingbulk data describing the average response of a population. Althoughinformative, these data often fail to establish causal relationships,resulting in nonunique solutions where multiple different topologiescan represent the same data pool equally well (Figure 1A).2

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: ∥The California Institute for Quantitative Biosciences, San Francisco, California 94158, United States.

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.

Show MeSH
Related in: MedlinePlus