Title :
Monte Carlo simulation of freezing of confluent tissues
Author :
Irimia, Daniel ; Karlsson, Jens O M
Abstract :
Summary form only given. Tissue freezing is an important phenomenon in cryopreservation and cryosurgery. Although much is known about the mechanisms and kinetics of the deleterious process of intracellular ice formation (IIF) in isolated cells, this knowledge is not sufficient to describe the formation of ice in tissues. Unlike isolated cells, cells in confluent tissues interact with each other, resulting in an increase in the probability of cell damage, due to intercellular ice propagation. In experiments in a micropatterned two-cell system, we have shown that ice propagation is mediated by gap junctions, increasing the rate of IIF by an order of magnitude. Based on these results, we have developed a theoretical model of intracellular freezing in multicellular tissues, using Monte Carlo techniques to simulate the coupled stochastic processes of ice nucleation and ice propagation. A parametric analysis is presented, demonstrating the differences in the kinetics of ice propagation between one-, two-, and three- dimensional tissue, as well as the sensitivity of the probability of IIF to variations in the number of cell neighbors and the degree of intercellular interaction. By predicting the dynamics of freezing in a tissue with a simulated tumor, conditions under which cryosurgical ablation would yield a favorable outcome were determined.
Keywords :
Monte Carlo methods; biothermics; cellular biophysics; freezing; ice; physiological models; stochastic processes; surgery; cell damage probability; cell neighbors number; coupled stochastic processes; coupled stochastic processes simulation; gap junctions; intercellular interaction degree; intracellular freezing; micropatterned two-cell system; multicellular tissues; one-dimensional tissue; probability sensitivity; theoretical model; three-dimensional tissue; tissue freezing dynamics; two-dimensional tissue; Ice; Kinetic theory; Monte Carlo methods; Neoplasms; Predictive models; Stochastic processes;
Conference_Titel :
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
Print_ISBN :
0-7803-7612-9
DOI :
10.1109/IEMBS.2002.1137098