Mantzaris Nikos V
Department of Chemical and Biomolecular Engineering and Bioengineering Department, Rice University, Houston, TX 77005, USA.
J Theor Biol. 2006 Aug 7;241(3):690-706. doi: 10.1016/j.jtbi.2006.01.005. Epub 2006 Feb 20.
A Monte Carlo algorithm, which can accurately simulate the dynamics of entire heterogeneous cell populations, was developed. The algorithm takes into account the random nature of cell division as well as unequal partitioning of cellular material at cell division. Moreover, it is general in the sense that it can accommodate a variety of single-cell, deterministic reaction kinetics as well as various stochastic division and partitioning mechanisms. The validity of the algorithm was assessed through comparison of its results with those of the corresponding deterministic cell population balance model in cases where stochastic behavior is expected to be quantitatively negligible. Both algorithms were applied to study: (a) linear intracellular kinetics and (b) the expression dynamics of a genetic network with positive feedback architecture, such as the lac operon. The effects of stochastic division as well as those of different division and partitioning mechanisms were assessed in these systems, while the comparison of the stochastic model with a continuum model elucidated the significance of cell population heterogeneity even in cases where only the prediction of average properties is of primary interest.
开发了一种蒙特卡罗算法，它能够精确模拟整个异质细胞群体的动力学。该算法考虑了细胞分裂的随机性以及细胞分裂时细胞物质的不均等分配。此外，它具有通用性，能够适应各种单细胞确定性反应动力学以及各种随机分裂和分配机制。在随机行为预计在数量上可忽略不计的情况下，通过将其结果与相应的确定性细胞群体平衡模型的结果进行比较，评估了该算法的有效性。两种算法都被用于研究：(a) 线性细胞内动力学，以及 (b) 具有正反馈结构的遗传网络（如乳糖操纵子）的表达动力学。在这些系统中评估了随机分裂以及不同分裂和分配机制的影响，而随机模型与连续模型的比较阐明了即使在仅主要关注平均性质预测的情况下细胞群体异质性的重要性。
