Leukocyte recruitment has a crucial role in inflammation and immunity. An interplay between adhesion molecules and pro-adhesive agonists generates a complex molecular network controlling tissue-specific and inflammation-dependent leukocyte vascular recognition. Recent findings highlight the importance of quantitative parameters in controlling the specificity of leukocyte vascular recognition. Introduction of quantitative parameters demonstrates the non-linear behavior of the process and suggests the necessity for a revision of the traditional model. We propose a formalization of the original multi-step model of leukocyte vascular recognition by introducing the notion of concurrency that explains how the quantitative variation of pro-adhesive parameters might control the specificity and the sensitivity of this process. Moreover, we discuss how concurrency, by integrating quantitative parameters, constitutes a central concept for the implementation of a predictive computer modeling of leukocyte vascular recognition.
Concurrency in leukocyte vascular recognition: developing the tools for a predictive computer model
CONSTANTIN, Gabriela;LAUDANNA, Carlo
2004-01-01
Abstract
Leukocyte recruitment has a crucial role in inflammation and immunity. An interplay between adhesion molecules and pro-adhesive agonists generates a complex molecular network controlling tissue-specific and inflammation-dependent leukocyte vascular recognition. Recent findings highlight the importance of quantitative parameters in controlling the specificity of leukocyte vascular recognition. Introduction of quantitative parameters demonstrates the non-linear behavior of the process and suggests the necessity for a revision of the traditional model. We propose a formalization of the original multi-step model of leukocyte vascular recognition by introducing the notion of concurrency that explains how the quantitative variation of pro-adhesive parameters might control the specificity and the sensitivity of this process. Moreover, we discuss how concurrency, by integrating quantitative parameters, constitutes a central concept for the implementation of a predictive computer modeling of leukocyte vascular recognition.File | Dimensione | Formato | |
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