Evidence map›Paper›PMID 21569565›Full record

ArticleBMC systems biology2011

Agent-based simulation of reactions in the crowded and structured intracellular environment: Influence of mobility and location of the reactants.

Michael T Klann, Alexei Lapin, Matthias Reuss

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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

24 citing papers in PubMed, 75 citations in OpenAlex.

  1. Cytoplasmic crowding acts as a porous medium reducing macromolecule diffusion.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  11. Anomalous diffusion and transport in heterogeneous systems separated by a membrane.Proceedings. Mathematical, physical, and engineering sciences · 2016
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  19. Hybrid spatial Gillespie and particle tracking simulation.Bioinformatics (Oxford, England) · 2012
    Article
  20. From microscopy data to in silico environments for in vivo-oriented simulations.EURASIP journal on bioinformatics & systems biology · 2012
    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

3 authors at 1 institution in 2 countries.

Michael T KlannAutomatic Control Laboratory, ETH Zurich, Physikstrasse 3 8092 Zurich, Switzerland. mklann@ee.ethz.ch
Alexei Lapin
Matthias Reuss
University of Stuttgart · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this paper we apply a novel agent-based simulation method in order to model intracellular reactions in detail. The simulations are performed within a virtual cytoskeleton enriched with further crowding elements, which allows the analysis of molecular crowding effects on intracellular diffusion and reaction rates. The cytoskeleton network leads to a reduction in the mobility of molecules. Molecules can also unspecifically bind to membranes or the cytoskeleton affecting (i) the fraction of unbound molecules in the cytosol and (ii) furthermore reducing the mobility. Binding of molecules to intracellular structures or scaffolds can in turn lead to a microcompartmentalization of the cell. Especially the formation of enzyme complexes promoting metabolic channeling, e.g. in glycolysis, depends on the co-localization of the proteins.

resultsWhile the co-localization of enzymes leads to faster reaction rates, the reduced mobility decreases the collision rate of reactants, hence reducing the reaction rate, as expected. This effect is most prominent in diffusion limited reactions. Furthermore, anomalous diffusion can occur due to molecular crowding in the cell. In the context of diffusion controlled reactions, anomalous diffusion leads to fractal reaction kinetics. The simulation framework is used to quantify and separate the effects originating from molecular crowding or the reduced mobility of the reactants. We were able to define three factors which describe the effective reaction rate, namely f diff for the diffusion effect, f volume for the crowding, and f access for the reduced accessibility of the molecules.

conclusionsMolecule distributions, reaction rate constants and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of a realistic cell environment. As such, the present simulation can help to bridge the gap between in vivo and in vitro kinetics.

Indexed as

AlgorithmsComputational BiologyComputer SimulationCytoplasmCytoskeletonDiffusionEnvironmentKineticsModels, BiologicalMovementSignal TransductionSoftwareStochastic ProcessesSystems Biology

Identifiers

PMID21569565
PMCPMC3123599
OpenAlexW2101765772

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.