Explaining the ageing of skeletal muscle fibres through a novel evolutionary mechanism

缺失的线粒体DNA （mtDNA）分子的扩张与衰老有关，尤其是在肌肉纤维中，它会导致肌肉减少症。尽管经过了三年的研究，肌纤维老化的机制仍然不清楚。先前的研究认为，被删除的线粒体DNA具有选择性优势，但事实上，细胞可以选择性地去除不利的线粒体DNA。在这个项目中，我们一直在探索如何将空间扩展模型与增强的突变体密度和随机性相结合，令人满意地再现我们从实验数据中估计的膨胀及其速度。我们已经找到了膨胀以波状方式发生的证据，我们也找到了波速的经验公式。值得注意的是，这些模型允许突变体的选择性劣势，但仍然产生传播波。我们介绍了最密集的随机生存机制，我们正在探索其作为传统的基于适者生存的进化理论的替代方案的合理性。因此，肌肉纤维老化是噪音和空间结构在产生新的进化现象中的作用的候选范例。基于我们提出的新机制，我们对肌肉衰老的理解表明，现有药物与减缓突变波的相关性。基于我们的扩展速度的经验公式，我们计划探索可能的药物干预的相关性，可以减缓突变体的传播。在这个项目中使用的工具是分析和计算技术的结合。我们正在使用随机微积分来获得模型中的分析洞察力，并使用Gillespie算法来模拟它。此外，我们使用统计技术将我们的预测和竞争模型与实验数据进行比较。

The expansion of deleted mitochondrial DNA (mtDNA) molecules has been linked to ageing, particularly in muscle fibres, where it leads to sarcopenia. Despite three years of research, the mechanism underlying muscle fibre ageing has remained unclear. Previous accounts have assigned a selective advantage to the deleted mitochondrial DNA, but, in fact, cells can selectively remove disadvantageous mtDNA. In this project we have been exploring how spatially extended models with a combination of enhanced density for mutants and stochasticity satisfactorily reproduce the expansion and its speed, that we have estimated estimate form experimental data. We have found evidence that the expansion takes place in a wavelike fashion and we have found an empirical formula for the wavespeed. Notably, these models permit a selective disadvantage for the mutants while nonetheless yielding spreading waves. We introduce the mechanism of stochastic survival of the densest, and we are exploring its plausibility as an alternative to conventional evolutionary theory based survival of the fittest. Muscle fibre ageing is thus a candidate exemplar for the role of noise and spatial structure in yielding novel evolutionary phenomena.Our understanding of muscle ageing, based on the novel mechanism we propose, suggests the relevance of existing drugs for slowing waves of mutants. Based on our empirical formula for the expansion speed, we plan to explore the relevance of possible pharmaceutical interventions that could slow down the spreading of mutants.The tools used in this project are a combination of analytical and computational techniques. We are using stochastic calculus to gain analytical insight in our model and the Gillespie algorithm to simulate it. Additionally, we use statistical techniques to compare the predictions of ours and competing models to experimental data.