Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Kang, Mingon; Tang, Liping; Gao, Jean
• Is Part Of: BMC bioinformatics. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-12
• Subjects: Computational modeling -- Foreign body responses -- Phagocyte transmigration -- Parameter estimation; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-0947-3
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract 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.