Modeling stem cell population dynamics

Many tissues and organs contain self-renewing stem cell populations that are crucial for their maintenance. Synthesizing the relative effects of anatomical constraints, cell proliferation dynamics and cell fate specification on the overall stem cell population dynamics is challenging, and so we reasoned that dynamic computational models that have the potential to systematically manipulate different influences might facilitate an understanding of experimental studies on self-renewing cell populations.

In our study published in Development [1] we have built a computational model of germline development in C. elegans. In this model, germ cells move, divide, respond to signals, progress through mitosis and meiosis, and differentiate according to a developmental program specified for a “cell”. This developmental program incorporates cellular decision-making that influences germ cell behavior, as defined by a subset of cell components and their dynamic interactions. Simulations driven by the model recapitulate C. elegans germline development and the effects of various genetic manipulations, as shown in supplementary movies, also available at [2].

Our analyses of model simulations and laboratory studies suggest that: (1) when the ligand interaction occurs over a short distance (that is, reaching only the distal-most germ cells), small differences in this distance destabilize the system and introduce unexpected variability; (2) inherent differences between progenitor cell types need not necessarily be invoked to explain complex differentiation dynamics upon reduction of receptor activity; (3) population dynamics and anatomical constraints influence niche residence; and (4) the germ cell proliferation rate during larval stages influences the differentiation pattern in the adult.

The computational modeling in this project has been carried out in the computational science laboratory at Microsoft Research in Cambridge, in close collaboration with the Hubbard lab. We are applying and developing modeling methods that were originally introduced for building and understanding engineering and software systems. Since biological systems are far more complex and robust than man-made engineering systems, a long term goal of this research is to challenge the ways engineering and software systems are currently constructed and understood.

[1] Y. Setty, D. Dalfo, D.Z. Korta, E.J. Albert Hubbard, and H. Kugler, A model of stem cell population dynamics: in-silico analysis and in-vivo validation, in Development, vol. 139, 47-56, 2012.

[2] http://research.microsoft.com/celegans

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