Computational Modeling of Cell Migration in 3D Matrices

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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.