↓ Skip to main content

Multiscale modeling of mucosal immune responses

Overview of attention for article published in BMC Bioinformatics, August 2015
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
55 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Multiscale modeling of mucosal immune responses
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/1471-2105-16-s12-s2
Pubmed ID
Authors

Yongguo Mei, Vida Abedi, Adria Carbo, Xiaoying Zhang, Pinyi Lu, Casandra Philipson, Raquel Hontecillas, Stefan Hoops, Nathan Liles, Josep Bassaganya-Riera

Abstract

Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Unknown 54 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 24%
Researcher 8 15%
Student > Bachelor 7 13%
Student > Doctoral Student 4 7%
Other 4 7%
Other 13 24%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 22%
Biochemistry, Genetics and Molecular Biology 10 18%
Engineering 6 11%
Computer Science 5 9%
Pharmacology, Toxicology and Pharmaceutical Science 3 5%
Other 12 22%
Unknown 7 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 02 June 2019.
All research outputs
#6,797,753
of 22,824,164 outputs
Outputs from BMC Bioinformatics
#2,584
of 7,287 outputs
Outputs of similar age
#79,837
of 267,539 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 124 outputs
Altmetric has tracked 22,824,164 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,539 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.