Computational Modeling of Cell Migration in 3D Matrices

描述(由申请人提供)：我们的总体目标是构建和测试预测性计算模型，以确定不同的细胞内信号如何整合，以管理细胞对三维环境中线索的迁移反应。我们的技术方法包括创新组合中的几个功能：{1)在相关生物大分子基质中对单个细胞迁移参数进行3D定量跟踪；{2)在3D迁移过程中对关键细胞内信号进行定量生化测量；{3)通过对受体属性的遗传操作和对关键分子开关的药物抑制来调制这些信号；{4)对这些信号与随后的迁移反应的定量关系进行计算建模；以及{5)非直观模型预测的实验测试。我们专注于由表皮生长因子受体家族(ErbB)信号诱导的信号和反应。ErbB家族受体配体结合下游产生的信号强烈影响多种细胞类型的迁移反应行为，包括肿瘤细胞在肿瘤向侵袭和转移过程中的迁移反应。这一典型的“线索-信号-反应”系统在器官发生和组织再生过程中具有重要的生理学意义，当异常时能够使肿瘤侵袭和扩散的病理学发生。因此，了解信号控制是如何定量实施的，应该对基础科学和治疗应用具有广泛的相关性和有用的影响。虽然已经确定了ErbB信号网络中的许多独立成分，但将这些不同的信号通路与迁移反应行为整合起来的定量模型才刚刚出现。在真正代表肿瘤侵袭和扩散障碍的3D环境中，几乎没有关于控制迁移的信号的基础工作。我们的工作将计算建模与专门的ErbB家族诱导的细胞迁移和信号网络活动的定量实验测量紧密地结合在3D矩阵中。我们的建模方法不会关注更普遍追求的“线索-信号”方面(从配体/受体线索产生信号的方面)，而是严重不足的“信号-反应”方面。虽然ErbB受体下游的多条信号通路可以在调节迁移中发挥重要作用，但尚不清楚多条通路网络如何定量整合以产生所观察到的表型行为。这个问题将使用称为决策树分析的统计建模框架来解决，该框架定义了一个控制层次，将信号的逻辑组合与所有线索条件下的迁移行为响应联系起来。我们建议拨款的目标是应用决策树模型通过三维矩阵预测ErbB受体信号对上皮细胞和癌细胞迁移的影响。公共卫生相关性：我们的目标是构建和测试预测性计算模型，以确定细胞内信号如何整合，以管理三维环境中细胞对线索的迁移反应。我们专注于由表皮生长因子受体家族(ErbB)信号诱导的信号和反应，与组织再生和肿瘤侵袭性相关。我们使用被称为决策树分析的统计建模框架，解决了ErbB家族受体激活下游的多途径信号网络如何定量整合以产生观察到的迁移行为的问题，该框架定义了一个控制层次，将信号的逻辑组合与所有线索条件下的迁移反应联系起来。

DESCRIPTION (provided by applicant): Our overall objective is to construct and test predictive computational models for how diverse intracellular signals integrate to govern cell migration responses to cues in three-dimensional environments. Our technical approach comprises several features in innovative combination: {1} 3D quantitative tracking of individual cell migration parameters within relevant biomacromolecular matrices; {2} quantitative biochemical measurements of key intracellular signals during this 3D migration; {3} modulation of these signals by genetic manipulation of receptor properties and by pharmacological inhibition of key molecular switches; {4} computational modeling of the quantitative relationship of these signals to the consequent migration response; and {5} experimental test of non-intuitive model predictions. We focus on signals and responses induced by the epidermal growth factor receptor family (ErbB) cues. Signals generated downstream of ErbB family receptor ligand binding strongly influence migration response behavior of many cell types, including carcinoma cells during tumor progression to invasion and metastasis. This paradigmatic "cue-signal- response" system is important physiologically during organogenesis and tissue regeneration, and when aberrant enables the pathology of tumor invasion and dissemination. Thus, understanding how the signaling control is exerted quantitatively should have broadly relevant and useful implications for both basic science and therapeutic applications. Although a multitude of individual components in the ErbB signaling network have been identified, quantitative models integratively relating these divergent signaling pathways to migration response behavior are only now emerging. Very little fundamental work on signaling governing migration has been performed in 3D environments that truly represent the barriers to tumor invasion and dissemination. Our work intimately integrates computational modeling with dedicated quantitative experimental measurement of ErbB family-induced cell migration and signaling network activity within 3D matrices. Our modeling approach will focus not on the more commonly-pursued "cue-signal" facet (that of signal generation from ligand/receptor cues, but instead the sorely under-addressed "signal-response" facet. Although it is clear that multiple signaling pathways downstream of ErbB receptors can play significant roles in regulating migration, what is not understood is how multi-pathway networks quantitatively integrate to yield the observed phenotypic behavior. This question will be addressed using the statistical modeling framework known as Decision Tree analysis, which defines a control hierarchy relating logical combinations of signals to the migration behavioral response across all cue conditions. Our goal for the proposed grant is to apply decision tree modeling to prediction of effects of ErbB receptor signaling on epithelial and carcinoma cell migration through three-dimensional matrices. PUBLIC HEALTH RELEVANCE: Our goal is to construct and test predictive computational models for how intracellular signals integrate to govern cell migration responses to cues in three-dimensional environments. We focus on signals and responses induced by the epidermal growth factor receptor family (ErbB) cues, with relevance to tissue regeneration and tumor invasiveness. We address the question of how multi-pathway signaling networks downstream of ErbB family receptor activation quantitatively integrate to yield observed migration behavior, using the statistical modeling framework known as Decision Tree analysis which defines a control hierarchy relating logical combinations of signals to the migration response across all cue conditions.