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Open Access Research article

Statistical ensemble analysis for simulating extrinsic noise-driven response in NF-κB signaling networks

Jaewook Joo1*, Steven J Plimpton2 and Jean-Loup Faulon3

Author Affiliations

1 Department of Physics and Astronomy, University of Tennessee, Knoxville 37996, USA

2 Scalable Algorithms Department, Sandia National Laboratories, Albuquerque, NM 87185, USA

3 Department of Biology, Evry University, Evry Cedex, France

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BMC Systems Biology 2013, 7:45  doi:10.1186/1752-0509-7-45

Published: 7 June 2013



Gene expression profiles and protein dynamics in single cells have a large cell-to-cell variability due to intracellular noise. Intracellular fluctuations originate from two sources: intrinsic noise due to the probabilistic nature of biochemical reactions and extrinsic noise due to randomized interactions of the cell with other cellular systems or its environment. Presently, there is no systematic parameterization and modeling scheme to simulate cellular response at the single cell level in the presence of extrinsic noise.


In this paper, we propose a novel statistical ensemble method to simulate the distribution of heterogeneous cellular responses in single cells. We capture the effects of extrinsic noise by randomizing values of the model parameters. In this context, a statistical ensemble is a large number of system replicates, each with randomly sampled model parameters from biologically feasible intervals. We apply this statistical ensemble approach to the well-studied NF-κB signaling system. We predict several characteristic dynamic features of NF-κB response distributions; one of them is the dosage-dependent distribution of the first translocation time of NF-κB.


The distributions of heterogeneous cellular responses that our statistical ensemble formulation generates reveal the effect of different cellular conditions, e.g., effects due to wild type versus mutant cells or between different dosages of external stimulants. Distributions generated in the presence of extrinsic noise yield valuable insight into underlying regulatory mechanisms, which are sometimes otherwise hidden.

Statistical ensemble; Extrinsic noise; Cell to cell variability; NF-κB signal transduction network