Modeling to design optimized estrogen-specific muscle regeneration treatment

总结 该提案的目标是开发一种基于雌激素特异性肌肉再生剂的模型（ABM）， 用它来进行计算机实验，以确定最佳的生长因子干预，改善肌肉 损伤后再生。这个新模型将使该领域了解性别差异如何发挥作用 在治疗肌肉损伤方面。众所周知，目前FDA批准的肌肉损伤治疗方法具有性别- 差异，这可以归因于性激素的差异。同样，有充分的证据表明， 对肌肉再生有多方面的影响。然而，虽然肌肉再生模型已经被 这些研究都是基于男性的数据，没有提供任何关于性别差异如何改变肌肉的见解。 伤害结果。这些深刻的局限性使得该领域没有任何工具来研究性别差异是如何产生的。 激素可能影响肌肉损伤、再生和治疗结果。 该项目有两个关键目标，将解决这些深刻的限制。第一个目标将发展， 验证肌肉再生ABM，通过使用偶联的体内 and in silico硅experiments实验.这一目标将通过收集冷冻损伤的雌性小鼠的数据来实现， 接收特定量的雌激素并使用不同的数据子集来调整和验证ABM。 第二个目标是模拟不同雌激素水平下生长因子的组合，以确定 根据雌激素水平选择生长因子的剂量可以优化治疗程序 在月经周期中。强化机器学习将用于识别增长的组合 因素，剂量和时间，将导致最快的肌肉恢复在不同水平的雌激素。这些 模型预测的每种雌激素水平的最佳治疗将在体内进行实验测试。综合起来看， 这些目标将开发第一个计算模型，该模型将雌激素水平的影响纳入研究范围。 如何影响肌肉损伤治疗。这项工作将提供一个重要的新的理解性为基础的 肌肉损伤、炎症、卫星干细胞反应和整体肌肉再生的差异 这些结果对于开发解释这些差异的治疗方法至关重要。

SUMMARY The goal of this proposal is to develop an estrogen-specific muscle regeneration agent-based model (ABM) and use it to conduct in silico experiments to identify the optimal set of growth factor interventions that improve muscle regeneration following injury. This new model will allow the field to understand how sex differences play a role in the treatment of muscle injury. It is known that current muscle injury treatments cleared by the FDA have sex- differences, which can be attributed to differences in sex hormones. Likewise, it is well documented that estrogen has a multi-faceted impact on muscle regeneration. However, while muscle regeneration models have been developed, they are based on male-only data and do not offer any insight into how sex differences alter muscle injury outcomes. These profound limitations leave the field without any tools to examine how differences in sex hormones may influence muscle damage, regeneration, and treatment outcomes. This project has two key aims that will resolve these profound limitations. The first aim will develop and validate a muscle regeneration ABM that accounts for the role of estrogen through the use of coupled in vivo and in silico experiments. This aim will be achieved by collecting data from freeze injured female mice that are receiving specified amounts of estrogen and using different subsets of the data to tune and validate the ABM. The second aim will simulate combinations of growth factors at varying levels of estrogen to identify how treatment procedures could be optimized by timing the dosage of growth factors according to estrogen levels during the menstrual cycle. Reinforcement machine learning will be used to identify the combination of growth factors, dosages, and timing that would lead to the fastest muscle recovery at varying levels of estrogen. These model-predicted optimal treatments for each estrogen level will be experimentally tested in vivo. Taken together, these aims will develop the first computational model that incorporates the effects of estrogen levels to study how they impact muscle injury treatments. This work will provide an important new understanding of sex-based differences in muscle damage, inflammation, satellite stem cell response, and overall muscle regeneration outcomes that will be crucial to developing treatments that account for these differences.