Evidence map›Paper›PMID 26329787›Full record

ArticleBMC bioinformatics2015

Multiscale modeling of mucosal immune responses.

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

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. A roadmap for translational cancer glycoimmunology at single cell resolution.Journal of experimental & clinical cancer research : CR · 2022
    Review
  5. Computational models in inflammatory bowel disease.Clinical and translational science · 2022
    Review
  6. Article
  7. Agent-based models of inflammation in translational systems biology: A decade later.Wiley interdisciplinary reviews. Systems biology and medicine · 2019
    Review
  8. Article
  9. Review
  10. Article
  11. Solving Immunology?Trends in immunology · 2017
    Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Goals in Nutrition Science 2015-2020.Frontiers in nutrition · 2015
    Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

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

Funding

PHS HHS HHSN272201000056C
6 · The paper itself

Abstract

backgroundComputational 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. 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.

conclusionWe 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.

Indexed as

Immunity, MucosalModels, BiologicalSignal TransductionCD4-Positive T-LymphocytesComputer SimulationHumans

Identifiers

PMID26329787
PMCPMC4705510

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.