Modeling to design optimized estrogen-specific muscle regeneration treatment

建模以设计优化的雌激素特异性肌肉再生治疗

• 批准号: 10557923
• 负责人: Silvia Salinas Blemker
• 金额: $ 16.65万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10557923
• 关键词: Acceleration; Affect; Area; Atrophic; Autologous; Behavior; Biochemical; Blood Platelets; CCL2 gene; Calibration; Cell Count; Cells; Communities; Complex; Computer Models; Coupled; Data; Data Set; Disease; Embryo; Environment; Estrogen decline; Estrogens; Exclusion; Female; Fibroblasts; Fibrosis; Freezing; Genes; Genetic Programming; Goals; Gonadal Steroid Hormones; Growth; Growth Factor; Hormones; Individual; Infiltration; Inflammation; Inflammatory; Inflammatory Response; Injury; Interleukin-10; Intervention; Literature; Machine Learning; Menstrual cycle; Methods; Modeling; Mus; Muscle; Muscle Cells; Muscle Fibers; Myosin Heavy Chains; Natural regeneration; Outcome; Ovariectomy; Plasma; Postmenopause; Prediction of Response to Therapy; Predisposition; Procedures; Process; Psychological reinforcement; Recovery; Role; Sex Differences; Specific qualifier value; TNF gene; Testing; Time; Tissues; Treatment outcome; Variant; Woman; Work; cell type; design; dosage; effective therapy; experience; experimental group; experimental study; healing; improved; in silico; in vivo; in vivo evaluation; injured; injury recovery; insight; male; muscle regeneration; novel; optimal treatments; predictive modeling; regeneration following injury; regeneration model; response; satellite cell; sex; simulation; stem cells; tool

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.

概括 该提案的目的是开发基于雌激素特异性的肌肉再生剂模型（ABM）和 在硅实验中使用它来确定一组最佳的生长因子干预措施，以改善肌肉 受伤后再生。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，FDA清除的当前肌肉损伤治疗有性 - 差异可以归因于性激素的差异。同样，雌激素有充分的文献证明 对肌肉再生有多方面的影响。但是，尽管肌肉再生模型已经 开发的，它们基于仅男性数据，并且不提供对性别差异如何改变肌肉的见解 伤害结果。这些深刻的限制使该领域没有任何工具来检查性别的差异 激素可能会影响肌肉损害，再生和治疗结果。 该项目具有两个关键目标，可以解决这些深刻的限制。第一个目标将发展和 验证肌肉再生ABM，该ABM解释了通过使用体内耦合的雌激素的作用 并在计算机实验中。通过从冻伤受伤的雌性小鼠那里收集数据来实现此目标 接收指定数量的雌激素并使用数据的不同子集调整和验证ABM。 第二个目标将模拟在不同水平的雌激素下生长因子的组合，以确定如何 可以通过根据雌激素水平计时生长因子的剂量来优化治疗程序 在月经周期中。加固机学习将用于确定增长的组合 因素，剂量和时机会导致雌激素水平不同的肌肉回收率最快。这些 每个雌激素水平的模型预测的最佳治疗方法将在体内进行实验测试。在一起， 这些目的将开发出第一个计算模型，该计算模型结合了雌激素水平的研究 它们如何影响肌肉损伤治疗。这项工作将为基于性别提供重要的新理解 肌肉损伤，炎症，卫星干细胞反应和整体肌肉再生的差异 对于开发解决这些差异的治疗方法至关重要的结果。