Yakovlev Andrei, Yanev Nikolai
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, NY 14642, USA.
Math Biosci. 2006 Sep;203(1):37-63. doi: 10.1016/j.mbs.2006.06.001. Epub 2006 Jun 14.
This paper considers the utility of a new class of stochastic branching processes with non-homogeneous immigration in modeling complex renewing cell systems. Such systems typically include the population of stem cells that provides an inexhaustible supply of cells necessary for maintaining the cellular composition of a tissue. A stem cell may be induced to transform (differentiate) into a progenitor cell. Progenitor cells retain the ability to proliferate and their function is believed to provide a quick proliferative response to an increased demand for cells in the population. There may be several sub-types of progenitor cells. Terminally differentiated cells do not divide under normal conditions; they are responsible for maintaining tissue-specific functions. Recent advancements in experimental techniques offer considerable scope for quantitative studies of in vivo cell kinetics based on stochastic modeling of renewing cell populations. However, no ready-made theory is currently available to take full advantage of these advancements. This paper introduces such a theory with a special focus on its feasibility in biological applications.
本文考虑了一类具有非齐次迁入的新型随机分支过程在对复杂更新细胞系统进行建模中的效用。此类系统通常包括干细胞群体,该群体为维持组织的细胞组成提供了源源不断的必要细胞供应。干细胞可能会被诱导转化(分化)为祖细胞。祖细胞保留增殖能力,并且据信其功能是对群体中细胞需求增加提供快速的增殖反应。可能存在几种祖细胞亚型。终末分化细胞在正常条件下不分裂;它们负责维持组织特异性功能。实验技术的最新进展为基于更新细胞群体的随机建模对体内细胞动力学进行定量研究提供了相当大的空间。然而,目前尚无现成的理论可充分利用这些进展。本文介绍了这样一种理论,并特别关注其在生物学应用中的可行性。