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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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Linear correlationbetween the noise profiles of two nodes in anetwork is not a reliable predictor of their connectivity. Noise ofA and B in a simple model: (db/dt) = (αba/a + K) – γbb showsa nonlinear relationship. Inset: mean expression of A (αa = 65 and 192, γa = 1) and B (αb = 2732, γb = 2, Kb = 696) as a function of time.
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fig5: Linear correlationbetween the noise profiles of two nodes in anetwork is not a reliable predictor of their connectivity. Noise ofA and B in a simple model: (db/dt) = (αba/a + K) – γbb showsa nonlinear relationship. Inset: mean expression of A (αa = 65 and 192, γa = 1) and B (αb = 2732, γb = 2, Kb = 696) as a function of time.

Mentions: Some early studies tested for regulatory relationships byattempting to directly score the linear correlation between the noisetrajectories of a pair of genes. Such correlations can potentiallypinpoint active connections particularly when taking time dynamicsinto consideration.13 However, the relationshipbetween noise in different components of a circuit is governed bypotentially complex relationships as depicted by eqs 6 and, therefore, might be poorly quantified by linear pairwisenoise correlation (Figure 5). This is becausethe fidelity with which noise propagates depends on factors such asthe susceptibility of a gene to the upstream fluctuations, the amountof the upstream noise, and rates at which the protein is able to respondto upstream change. All of these factors change over time, most rapidlyin the dynamic range where proteins concentrations change the most,conditions under which most experiments are usually conducted.


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)

Linear correlationbetween the noise profiles of two nodes in anetwork is not a reliable predictor of their connectivity. Noise ofA and B in a simple model: (db/dt) = (αba/a + K) – γbb showsa nonlinear relationship. Inset: mean expression of A (αa = 65 and 192, γa = 1) and B (αb = 2732, γb = 2, Kb = 696) as a function of time.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Linear correlationbetween the noise profiles of two nodes in anetwork is not a reliable predictor of their connectivity. Noise ofA and B in a simple model: (db/dt) = (αba/a + K) – γbb showsa nonlinear relationship. Inset: mean expression of A (αa = 65 and 192, γa = 1) and B (αb = 2732, γb = 2, Kb = 696) as a function of time.
Mentions: Some early studies tested for regulatory relationships byattempting to directly score the linear correlation between the noisetrajectories of a pair of genes. Such correlations can potentiallypinpoint active connections particularly when taking time dynamicsinto consideration.13 However, the relationshipbetween noise in different components of a circuit is governed bypotentially complex relationships as depicted by eqs 6 and, therefore, might be poorly quantified by linear pairwisenoise correlation (Figure 5). This is becausethe fidelity with which noise propagates depends on factors such asthe susceptibility of a gene to the upstream fluctuations, the amountof the upstream noise, and rates at which the protein is able to respondto upstream change. All of these factors change over time, most rapidlyin the dynamic range where proteins concentrations change the most,conditions under which most experiments are usually conducted.

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