Evidence map›Paper›PMID 30621747›Full record

ArticleBiology direct2018

Reconstruction of endosomal organization and function by a combination of ODE and agent-based modeling strategies.

Luis S Mayorga, Ignacio Cebrian, Meghna Verma, Stefan Hoops, Josep Bassaganya-Riera

Abstract read
In one paragraph

Article in Biology direct, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Agent-based models of inflammation in translational systems biology: A decade later.Wiley interdisciplinary reviews. Systems biology and medicine · 2019
    Review
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.

Luis S MayorgaFacultad de Ciencias Médicas, Facultad de Ciencias Exactas y Naturales, IHEM (Universidad Nacional de Cuyo, CONICET), Casilla de Correo 56, 5500, Mendoza, Argentina. lmayorga@fcm.uncu.edu.ar.ORCID 0000-0002-5995-0671
Ignacio CebrianFacultad de Ciencias Médicas, Facultad de Ciencias Exactas y Naturales, IHEM (Universidad Nacional de Cuyo, CONICET), Casilla de Correo 56, 5500, Mendoza, Argentina.
Meghna VermaNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, USA.
Stefan HoopsNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, USA.
Josep Bassaganya-RieraNutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute, Virginia Tech, Blacksburg, VA, USA.

Funding

COPASI software for modeling and simulation of biochemical networksR01GM080219 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI HOOPS, STEFAN, MENDES, PEDRO · 2007 to 2019
$5.4M
NIGMS NIH HHS R01 GM080219
6 · The paper itself

Abstract

backgroundReproducing cell processes using an in silico system is an essential tool for understanding the underlying mechanisms and emergent properties of this extraordinary complex biological machine. However, computational models are seldom applied in the field of intracellular trafficking. In a cell, numerous molecular interactions occur on the surface or in the interior of membrane-bound compartments that continually change position and undergo dynamic processes of fusion and fission. At present, the available simulation tools are not suitable to develop models that incorporate the dynamic evolution of the cell organelles.

resultsWe developed a modeling platform combining Repast (Agent-Based Modeling, ABM) and COPASI (Differential Equations, ODE) that can be used to reproduce complex networks of molecular interactions. These interactions occur in dynamic cell organelles that change position and composition over the course of time. These two modeling strategies are fundamentally different and comprise of complementary capabilities. The ODEs can easily model the networks of molecular interactions, signaling cascades, and complex metabolic reactions. On the other hand, ABM software is especially suited to simulate the movement, interaction, fusion, and fission of dynamic organelles. We used the combined ABM-ODE platform to simulate the transport of soluble and membrane-associated cargoes that move along an endocytic route composed of early, sorting, recycling and late endosomes. We showed that complex processes that strongly depend on transport can be modeled. As an example, the hydrolysis of a GM2-like glycolipid was programmed by adding a trans-Golgi network compartment, lysosomal enzyme trafficking, endosomal acidification, and cholesterol processing to the simulation model.

conclusionsThe model captures the highly dynamic nature of cell compartments that fuse and divide, creating different conditions for each organelle. We expect that this modeling strategy will be useful to understand the logic underlying the organization and function of the endomembrane system. REVIEWERS: This article was reviewed by Drs. Rafael Fernández-Chacón, James Faeder, and Thomas Simmen.

Indexed as

Protein TransportEndosomesModels, TheoreticalOrganellesSystems AnalysisAgent-based modelingDifferential equation modelingEndocytic pathwayIntracellular transportLipid metabolismRab domains

Identifiers

PMID30621747
PMCPMC6883406

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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.