Evidence map›Paper›PMID 37508862›Full record

ArticleBioengineering (Basel, Switzerland)2023

A General Approach for the Modelling of Negative Feedback Physiological Control Systems.

Alfonso Maria Ponsiglione, Francesco Montefusco, Leandro Donisi, Annarita Tedesco, Carlo Cosentino, Alessio Merola, Maria Romano, Francesco Amato

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Alfonso Maria PonsiglioneDipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy.ORCID 0000-0003-1346-515X
Francesco MontefuscoDipartimento di Scienze Economiche, Giuridiche, Informatiche e Motorie, Università degli Studi di Napoli Parthenope, 80035 Nola, Italy.ORCID 0000-0002-3264-9686
Leandro DonisiDipartimento di Scienze Mediche e Chirurgiche Avanzate, Università degli studi della Campania "Luigi Vanvitelli", P.zza L. Miraglia 2, 80138 Napoli, Italy.
Annarita TedescoDipartimento di Ingegneria per l'Innovazione, Universitá del Salento, 73100 Lecce, Italy.
Carlo CosentinoSchool of Computer and Biomedical Engineering, Dipartimento di Medicina Sperimentale e Clinica, Università degli Studi Magna Græcia di Catanzaro, Campus di Germaneto "Salvatore Venuta", 88100 Catanzaro, Italy.ORCID 0000-0001-5768-1829
Alessio MerolaSchool of Computer and Biomedical Engineering, Dipartimento di Medicina Sperimentale e Clinica, Università degli Studi Magna Græcia di Catanzaro, Campus di Germaneto "Salvatore Venuta", 88100 Catanzaro, Italy.ORCID 0000-0002-8728-2084
Maria RomanoDipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy.
Francesco AmatoDipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy.ORCID 0000-0002-9053-3139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and in vitro counterparts. This work's objective is to capture the characteristics that a nonlinear dynamical mathematical model should exhibit, in order to describe physiological control systems at different scales. The similarities among various negative feedback physiological systems have been investigated and a unique general framework to describe them has been proposed. Within such a framework, both the existence and stability of equilibrium points are investigated. The model here introduced is based on a closed-loop topology, on which the homeostatic process is based. Finally, to validate the model, three paradigmatic examples of physiological control systems are illustrated and discussed: the ultrasensitivity mechanism for achieving homeostasis in biomolecular circuits, the blood glucose regulation, and the neuromuscular reflex arc (also referred to as muscle stretch reflex). The results show that, by a suitable choice of the modelling functions, the dynamic evolution of the systems under study can be described through the proposed general nonlinear model. Furthermore, the analysis of the equilibrium points and dynamics of the above-mentioned systems are consistent with the literature.

Indexed as

homeostasisnegative feedback control systemsphysiological systemsstability

Identifiers

PMID37508862
PMCPMC10376068

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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.