NDLI: Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells

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Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells

Content Provider	Royal Society of Chemistry (RSC)
Author	Nayak, Satyaprakash Yen, Andrew Varner, Jeffrey D. Song, Sang Ok Tasseff, Ryan
Copyright Year	2011
Abstract	Manipulation of differentiation programs has therapeutic potential in a spectrum of human cancers and neurodegenerative disorders. In this study, we integrated computational and experimental methods to unravel the response of a lineage uncommitted precursor cell-line, HL-60, to Retinoic Acid (RA). HL-60 is a human myeloblastic leukemia cell-line used extensively to study human differentiation programs. Initially, we focused on the role of the BLR1 receptor in RA-induced differentiation and G1/0-arrest in HL-60. BLR1, a putative G protein-coupled receptor expressed following RA exposure, is required for RA-induced cell-cycle arrest and differentiation and causes persistent MAPK signaling. A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell-lines with and without RA. The current model described the dynamics of 729 proteins and protein complexes interconnected by 1356 interactions. An ensemble strategy was used to compensate for uncertain model parameters. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. The ensemble of models also correctly predicted Rb and p47phox regulation and the correlation between p21-CDK4-cyclin D formation and G1/0-arrest following exposure to RA. Finally, we investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. Taken together, the model presented here was a first step toward a systematic framework for analysis of programmed differentiation. These studies also demonstrated that mechanistic network modeling can help prioritize experimental directions by generating falsifiable hypotheses despite uncertainty.
Starting Page	578
Ending Page	591
Page Count	14
File Format	HTM / HTML PDF
ISSN	17579694
Volume Number	3
Issue Number	5
Journal	Integrative Biology
DOI	10.1039/c0ib00141d
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	MAPK Unravel Acute myeloblastic leukemia Cell surface receptor G protein\u2013coupled receptor Mitogen-activated protein kinase Mathematical model Protein P21 Cyclin-dependent kinase 4 Cyclin Falsifiability
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry Biophysics

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