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A multi-scale approach reveals that NF-κB cRel enforces a B-cell decision to divide

Understanding the functions of multi-cellular organs in terms of the molecular networks within each cell is an important step in the quest to predict phenotype from genotype. B-lymphocyte population dynamics, which are predictive of immune response and vaccine effectiveness, are determined by indivi...

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Detalles Bibliográficos
Autores principales: Shokhirev, Maxim N, Almaden, Jonathan, Davis-Turak, Jeremy, Birnbaum, Harry A, Russell, Theresa M, Vargas, Jesse A D, Hoffmann, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BlackWell Publishing Ltd 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358656/
https://www.ncbi.nlm.nih.gov/pubmed/25680807
http://dx.doi.org/10.15252/msb.20145554
Descripción
Sumario:Understanding the functions of multi-cellular organs in terms of the molecular networks within each cell is an important step in the quest to predict phenotype from genotype. B-lymphocyte population dynamics, which are predictive of immune response and vaccine effectiveness, are determined by individual cells undergoing division or death seemingly stochastically. Based on tracking single-cell time-lapse trajectories of hundreds of B cells, single-cell transcriptome, and immunofluorescence analyses, we constructed an agent-based multi-modular computational model to simulate lymphocyte population dynamics in terms of the molecular networks that control NF-κB signaling, the cell cycle, and apoptosis. Combining modeling and experimentation, we found that NF-κB cRel enforces the execution of a cellular decision between mutually exclusive fates by promoting survival in growing cells. But as cRel deficiency causes growing B cells to die at similar rates to non-growing cells, our analysis reveals that the phenomenological decision model of wild-type cells is rooted in a biased race of cell fates. We show that a multi-scale modeling approach allows for the prediction of dynamic organ-level physiology in terms of intra-cellular molecular networks.