4.6 Article

Modeling Reveals Bistability and Low-Pass Filtering in the Network Module Determining Blood Stem Cell Fate

期刊

PLOS COMPUTATIONAL BIOLOGY
卷 6, 期 5, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1000771

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资金

  1. Rice University
  2. NSF [MCB-0845919]
  3. Leukemia Research Fund
  4. Div Of Molecular and Cellular Bioscience
  5. Direct For Biological Sciences [0920463] Funding Source: National Science Foundation
  6. Medical Research Council [G0800784B, G0800784] Funding Source: researchfish
  7. National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) [G0900729/1] Funding Source: researchfish
  8. MRC [G0800784] Funding Source: UKRI

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Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad-a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models.

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