Stochastic Modeling of Stem Cells
- Author(s): Yang, Jienian
- Advisor(s): Komarova, Natalia
- et al.
Stem cells (SCs) are the body's raw materials - cells from which all other cells with specialized functions are generated. Understanding the dynamics of SC lineages is of central importance both for healthy and cancerous tissues. We study stochastic population dynamics of the two-compartment (stem and differentiated cells) system and the three-compartment (SCs, intermediate cell type and differentiated cells) model. Cell decisions such as proliferation vs. differentiation decisions are under regulation from surrounding cells. Successful maintenance of cellular lineages depends on the fate decision dynamics of SCs upon division. There are three possible strategies with respect to SC fate decision symmetry: (a) asymmetric mode, (b) symmetric mode and (c) mixed mode. Theoretically, either of these strategies can achieve lineage homeostasis. We start the whole project by only considering symmetric mode for the two-compartment system. We derive simple explicit expressions for the means and the variances of SC and differentiated cell number. The methodology is formulated without any specific assumptions on the functional form of the controls, and thus can be used for any biological system. We then extend the study of the two-compartment system to include asymmetric mode. In particular, we focus on minimal control mechanisms and networks of the two-compartment system. Through stochastic analysis and simulations we show that asymmetric divisions can either stabilize or destabilize the lineage system, depending on the underlying control network. Next, we propose an algorithm to identify a set of candidate control networks that are compatible with (1) measured means and variances of cell populations, (2) qualitative information on cell population dynamics, and (3) statistical information on intra-crypt cell type correlations. We apply the algorithm on the data of human colon crypts, where lineages are an example of the three-compartment model. We start with 32 minimal control networks compatible with tissue stability, and zero in on only three networks that are most compatible with the measurements.