Project 5: Predictive Mathematical Model of Inflammation for Shock/Trauma

项目5:休克/创伤炎症预测数学模型

基本信息

项目摘要

Trauma and hemorrhage elicit an acute inflammatory response. This complex process is observed at the cellular, tissue, organ, and whole-organism levels. It is now appreciated that Damage-Associated Molecular Pattern (DAMP) molecules and the signaling cascades induced by their receptors on multiple cell types mediate and modulate central aspects of this inflammatory response. Though daunting at first blush, the complexity of inflammation can be studied and outcomes can be predicted using pioneering computational simulations created by our group. We hypothesize that a validated and calibrated mathematical model of inflammation and its pathologic consequences at the multiple scales will be useful for predicting outcome in patients suffering from traumatic/hemorrhagic shock. We will test this hypothesis in three Specific Aims. In Aim 1, we will utilize gene- modified mice and cells, multiplexed analyte data, statistical methods, and multi-scale simulations of the inflammatory response in order to discern DAMP-driven master switches that might be modified therapeutically. Preconditioning (the phenomenon in which prior exposure to a given stimulus will modify the response to a subsequent stimulus) is a central feature of the non-linear Inflammatory trajectories and outcomes of trauma patients, and its dependence on initial conditions and other system states makes preconditioning a prime example of inflammation as a complex system. In Aim 2, we will deflne in silico the in vitro and in vivo roles of DAMP'S and their receptors in the phenomenon of preconditioning. In Aim 3, we will create patient-specific and population simulations of the human inflammatory response to trauma that include both plasma analyte dynamics and cytokine single nucleotide polymorphisms, coupled to in silico clinical trials using novel computational insights and methods. The research proposed herein will impact both basic and translational research on the inflammatory process of shock/trauma. RELEVANCE (See instructions): The work proposed herein would lead to the creation of a series of computational simulations of inflammation, testing the hypothesis that the response to damaged tissue acts as a central mediator, integrator, and possible therapeutic target in the setting of trauma/hemorrhage. This work would include the creation of patient-specific diagnostics as well as simulated clinical trials, and thus is translational. PROJECJ/
创伤和出血引起急性炎症反应。这个复杂的过程是在细胞,

项目成果

期刊论文数量(0)
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专利数量(0)

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YORAM VODOVOTZ其他文献

YORAM VODOVOTZ的其他文献

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{{ truncateString('YORAM VODOVOTZ', 18)}}的其他基金

Project 5: Predictive Mathematical Model of Inflammation for Shock/Trauma
项目5:休克/创伤炎症预测数学模型
  • 批准号:
    7751472
  • 财政年份:
    2009
  • 资助金额:
    $ 28.41万
  • 项目类别:
Mathematical Modeling of Inflammation in ARDS
ARDS 炎症的数学模型
  • 批准号:
    7501603
  • 财政年份:
    2008
  • 资助金额:
    $ 28.41万
  • 项目类别:
Mathematical Modeling of Inflammation in ARDS
ARDS 炎症的数学模型
  • 批准号:
    7923836
  • 财政年份:
    2008
  • 资助金额:
    $ 28.41万
  • 项目类别:
Mathematical Modeling of Inflammation in ARDS
ARDS 炎症的数学模型
  • 批准号:
    7677285
  • 财政年份:
    2008
  • 资助金额:
    $ 28.41万
  • 项目类别:
PREDICTIVE MATHEMATICAL MODEL OF INFLAMMATION FOR SHOCK/TRAUMA
休克/创伤炎症的预测数学模型
  • 批准号:
    6861601
  • 财政年份:
    2004
  • 资助金额:
    $ 28.41万
  • 项目类别:
Mathematical Modeling of Anthrax Infection
炭疽感染的数学模型
  • 批准号:
    6555519
  • 财政年份:
    2002
  • 资助金额:
    $ 28.41万
  • 项目类别:
PREDICTIVE MATHEMATICAL MODEL OF INFLAMMATION FOR SHOCK/TRAUMA
休克/创伤炎症的预测数学模型
  • 批准号:
    7094107
  • 财政年份:
  • 资助金额:
    $ 28.41万
  • 项目类别:
PREDICTIVE MATHEMATICAL MODEL OF INFLAMMATION FOR SHOCK/TRAUMA
休克/创伤炎症的预测数学模型
  • 批准号:
    7274165
  • 财政年份:
  • 资助金额:
    $ 28.41万
  • 项目类别:
Project 5: Predictive Mathematical Model of Inflammation for Shock/Trauma
项目5:休克/创伤炎症预测数学模型
  • 批准号:
    8294841
  • 财政年份:
  • 资助金额:
    $ 28.41万
  • 项目类别:
PREDICTIVE MATHEMATICAL MODEL OF INFLAMMATION FOR SHOCK/TRAUMA
休克/创伤炎症的预测数学模型
  • 批准号:
    7465385
  • 财政年份:
  • 资助金额:
    $ 28.41万
  • 项目类别:

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