Cell–cell interaction networks regulate blood stem and progenitor cell fate

DC Kirouac, GJ Madlambayan, M Yu… - Molecular systems …, 2009 - embopress.org
Molecular systems biology, 2009embopress.org
Communication networks between cells and tissues are necessary for homeostasis in
multicellular organisms. Intercellular (between cell) communication networks are particularly
relevant in stem cell biology, as stem cell fate decisions (self‐renewal, proliferation, lineage
specification) are tightly regulated based on physiological demand. We have developed a
novel mathematical model of blood stem cell development incorporating cell‐level kinetic
parameters as functions of secreted molecule‐mediated intercellular networks. By relation to …
Communication networks between cells and tissues are necessary for homeostasis in multicellular organisms. Intercellular (between cell) communication networks are particularly relevant in stem cell biology, as stem cell fate decisions (self‐renewal, proliferation, lineage specification) are tightly regulated based on physiological demand. We have developed a novel mathematical model of blood stem cell development incorporating cell‐level kinetic parameters as functions of secreted molecule‐mediated intercellular networks. By relation to quantitative cellular assays, our model is capable of predictively simulating many disparate features of both normal and malignant hematopoiesis, relating internal parameters and microenvironmental variables to measurable cell fate outcomes. Through integrated in silico and experimental analyses, we show that blood stem and progenitor cell fate is regulated by cell–cell feedback, and can be controlled non‐cell autonomously by dynamically perturbing intercellular signalling. We extend this concept by demonstrating that variability in the secretion rates of the intercellular regulators is sufficient to explain heterogeneity in culture outputs, and that loss of responsiveness to cell–cell feedback signalling is both necessary and sufficient to induce leukemic transformation in silico.
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