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首页> 外文期刊>BMC Bioinformatics >Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice
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Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice

机译:吞噬细胞迁移对小鼠皮下生物材料植入物异物反应的计算模型

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Background Computational modeling and simulation play an important role in analyzing the behavior of complex biological systems in response to the implantation of biomedical devices. Quantitative computational modeling discloses the nature of foreign body responses. Such understanding will shed insight on the cause of foreign body responses, which will lead to improved biomaterial design and will reduce foreign body reactions. One of the major obstacles in computational modeling is to build a mathematical model that represents the biological system and to quantitatively define the model parameters. Results In this paper, we considered quantitative inter connections and logical relationships among diverse proteins and cells, which have been reported in biological experiments and literature. Based on the established biological discovery, we have built a mathematical model while unveiling the key components that contribute to biomaterial-mediated inflammatory responses. For the parameter estimation of the mathematical model, we proposed a global optimization algorithm, called Discrete Selection Levenberg-Marquardt (DSLM). This is an extension of Levenberg-Marquardt (LM) algorithm which is a gradient-based local optimization algorithm. The proposed DSLM suggests a new approach for the selection of optimal parameters in the discrete space with fast computational convergence. Conclusions The computational modeling not only provides critical clues to recognize current knowledge of fibrosis development but also enables the prediction of yet-to-be observed biological phenomena.
机译:背景技术计算模型和仿真在分析复杂生物系统响应生物医学设备植入的行为中起着重要作用。定量计算建模揭示了异物反应的性质。这种理解将使人们对异物反应的原因有更深入的了解,这将导致改进的生物材料设计并减少异物反应。计算建模中的主要障碍之一是建立代表生物系统的数学模型并定量定义模型参数。结果在本文中,我们考虑了生物学实验和文献中已报道的各种蛋白质和细胞之间的定量相互联系和逻辑关系。基于已建立的生物学发现,我们在揭示有助于生物材料介导的炎症反应的关键成分的同时,建立了数学模型。对于数学模型的参数估计,我们提出了一种全局优化算法,称为离散选择Levenberg-Marquardt(DSLM)。这是Levenberg-Marquardt(LM)算法的扩展,该算法是基于梯度的局部优化算法。提出的DSLM提出了一种新的方法,用于在离散空间中以快速的计算收敛来选择最佳参数。结论计算模型不仅提供识别当前纤维化发展知识的关键线索,而且还可以预测尚未观察到的生物学现象。

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