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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

基本信息

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
项目类别：
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

项目摘要

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.

总结