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NF-κB-dependent Luciferase Activation and Quantification of Gene Expression in Salmonella Infected Tissue Culture Cells
Published on: January 12, 2020
Statistical ensemble analysis for simulating extrinsic noise-driven response in NF-κB signaling networks
Jaewook Joo1, Steven J Plimpton, Jean-Loup Faulon
1Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA. jjoo1@utk.edu
BMC Systems Biology
|June 8, 2013
Summary
This study introduces a new statistical method to model cell responses, accounting for external noise. This approach helps understand cellular variability and regulatory mechanisms in systems like NF-κB signaling.
Area of Science:
- Systems Biology
- Computational Biology
- Cellular Signaling
Background:
- Single-cell gene expression and protein dynamics exhibit significant cell-to-cell variability due to intracellular noise.
- Intracellular noise arises from intrinsic (biochemical reaction randomness) and extrinsic (environmental interactions) sources.
- Current methods lack systematic parameterization for simulating single-cell responses under extrinsic noise.
Purpose of the Study:
- To develop a novel statistical ensemble method for simulating heterogeneous cellular responses at the single-cell level.
- To incorporate the effects of extrinsic noise by randomizing model parameters.
- To provide a framework for understanding cellular variability and regulatory mechanisms.
Main Methods:
- Proposed a statistical ensemble approach, generating numerous system replicates with randomly sampled model parameters.
- Applied the method to the Nuclear Factor kappa B (NF-κB) signaling pathway.
- Simulated distributions of cellular responses, capturing extrinsic noise effects.
Main Results:
- Successfully simulated the distribution of heterogeneous cellular responses in single cells.
- Predicted characteristic dynamic features of NF-κB response distributions.
- Identified a dosage-dependent distribution of the first translocation time of NF-κB as a key prediction.
Conclusions:
- The statistical ensemble method effectively reveals the impact of different cellular conditions (e.g., wild type vs. mutant, varying stimulant dosages).
- Simulated distributions under extrinsic noise provide insights into underlying regulatory mechanisms.
- This approach enhances understanding of cellular heterogeneity and signaling dynamics.
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