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Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice

Overview of attention for article published in BMC Bioinformatics, February 2016
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Title
Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0947-3
Pubmed ID
Authors

Mingon Kang, Liping Tang, Jean Gao

Abstract

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. 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. 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 14%
Researcher 3 14%
Student > Bachelor 2 10%
Student > Master 2 10%
Professor 2 10%
Other 3 14%
Unknown 6 29%
Readers by discipline Count As %
Engineering 3 14%
Computer Science 3 14%
Medicine and Dentistry 2 10%
Physics and Astronomy 2 10%
Materials Science 2 10%
Other 3 14%
Unknown 6 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 March 2016.
All research outputs
#6,331,552
of 7,325,869 outputs
Outputs from BMC Bioinformatics
#3,099
of 3,325 outputs
Outputs of similar age
#238,205
of 282,583 outputs
Outputs of similar age from BMC Bioinformatics
#132
of 136 outputs
Altmetric has tracked 7,325,869 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,325 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.