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Stem Cells International
Volume 2012, Article ID 367567, 9 pages
Review Article

Stem Cell Niche Dynamics: From Homeostasis to Carcinogenesis

1Computational and Systems Biology Interdepartmental Program, School of Medicine, University of California, Los Angeles, CA 90095, USA
2Department of Human Genetics, School of Medicine, University of California, Los Angeles, CA 90095, USA
3Division of Hematology-Oncology, Department of Medicine, School of Medicine, University of California, Los Angeles, P.O. Box 957059, Suite 2333 PVUB, Los Angeles, CA 90095-7059, USA

Received 7 June 2011; Accepted 23 October 2011

Academic Editor: Linheng Li

Copyright © 2012 Kevin S. Tieu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The stem cell microenvironment is involved in regulating the fate of the stem cell with respect to self-renewal, quiescence, and differentiation. Mathematical models are helpful in understanding how key pathways regulate the dynamics of stem cell maintenance and homeostasis. This tight regulation and maintenance of stem cell number is thought to break down during carcinogenesis. As a result, the stem cell niche has become a novel target of cancer therapeutics. Developing a quantitative understanding of the regulatory pathways that guide stem cell behavior will be vital to understanding how these systems change under conditions of stress, inflammation, and cancer initiation. Predictions from mathematical modeling can be used as a clinical tool to guide therapy design. We present a survey of mathematical models used to study stem cell population dynamics and stem cell niche regulation, both in the hematopoietic system and other tissues. Highlighting the quantitative aspects of stem cell biology, we describe compelling questions that can be addressed with modeling. Finally, we discuss experimental systems, most notably Drosophila, that can best be used to validate mathematical predictions.