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:
- 金额:$ 16.65万
- 依托单位:
- 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:
- 关键词:AccelerationAffectAreaAtrophicAutologousBehaviorBiochemicalBlood PlateletsCCL2 geneCalibrationCell CountCellsCommunitiesComplexComputer ModelsCoupledDataData SetDiseaseEmbryoEnvironmentEstrogen declineEstrogensExclusionFemaleFibroblastsFibrosisFreezingGenesGenetic ProgrammingGoalsGonadal Steroid HormonesGrowthGrowth FactorHormonesIndividualInfiltrationInflammationInflammatoryInflammatory ResponseInjuryInterleukin-10InterventionLiteratureMachine LearningMenstrual cycleMethodsModelingMusMuscleMuscle CellsMuscle FibersMyosin Heavy ChainsNatural regenerationOutcomeOvariectomyPlasmaPostmenopausePrediction of Response to TherapyPredispositionProceduresProcessPsychological reinforcementRecoveryRoleSex DifferencesSpecific qualifier valueTNF geneTestingTimeTissuesTreatment outcomeVariantWomanWorkcell typedesigndosageeffective therapyexperienceexperimental groupexperimental studyhealingimprovedin silicoin vivoin vivo evaluationinjuredinjury recoveryinsightmalemuscle regenerationnoveloptimal treatmentspredictive modelingregeneration following injuryregeneration modelresponsesatellite cellsexsimulationstem cellstool
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
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。
项目成果
期刊论文数量(0)
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会议论文数量(0)
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Silvia Salinas Blemker其他文献
Silvia Salinas Blemker的其他文献
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{{ truncateString('Silvia Salinas Blemker', 18)}}的其他基金
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:
10363144 - 财政年份:2022
- 资助金额:
$ 16.65万 - 项目类别:
A quantitative framework to examine sex differences in musculoskeletal scaling and function
检查肌肉骨骼尺度和功能性别差异的定量框架
- 批准号:
10220349 - 财政年份:2021
- 资助金额:
$ 16.65万 - 项目类别:
A quantitative framework to examine sex differences in musculoskeletal scaling and function
检查肌肉骨骼尺度和功能性别差异的定量框架
- 批准号:
10478238 - 财政年份:2021
- 资助金额:
$ 16.65万 - 项目类别:
A quantitative framework to examine sex differences in musculoskeletal scaling and function
检查肌肉骨骼尺度和功能性别差异的定量框架
- 批准号:
10684930 - 财政年份:2021
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开发商业上可行的机器学习产品来自动检测肩袖肌肉病理
- 批准号:
10495191 - 财政年份:2021
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