Evidence map›Paper›PMID 24864263›Full record

ArticleBioMed research international2014

Agent-based modeling of the immune system: NetLogo, a promising framework.

Ferdinando Chiacchio, Marzio Pennisi, Giulia Russo, Santo Motta, Francesco Pappalardo

Abstract read
In one paragraph

Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Article
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  5. Review
  6. Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2023
    Review
  7. Targeting alternative splicing in cancer immunotherapy.Frontiers in cell and developmental biology · 2023
    Review
  8. Article
  9. Article
  10. Article
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  12. Review
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  18. Trends in mathematical modeling of host-pathogen interactions.Cellular and molecular life sciences : CMLS · 2020
    Review
  19. Article
  20. 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

5 authors.

Ferdinando ChiacchioDepartment of Electric, Electronics and Computer Engineering, University of Catania, V.le A. Doria 6, 95125 Catania, Italy.
Marzio PennisiDepartment of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125 Catania, Italy.ORCID 0000-0003-0231-7653
Giulia RussoDepartment of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy.
Santo MottaDepartment of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125 Catania, Italy.ORCID 0000-0003-3533-2972
Francesco PappalardoDepartment of Drug Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.

Indexed as

Models, ImmunologicalProgramming LanguagesHumansImmune SystemNeoplasms

Identifiers

PMID24864263
PMCPMC4016927

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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.