Evidence map›Paper›PMID 27458581›Full record

ArticleFrontiers in bioengineering and biotechnology2016

Exploring Instructive Physiological Signaling with the Bioelectric Tissue Simulation Engine.

Alexis Pietak, Michael Levin

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
5.4field-weighted citation impact, top 4% 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

51 citing papers in PubMed, 92 citations in OpenAlex.

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  16. Engineering multicellular living systems-a Keystone Symposia report.Annals of the New York Academy of Sciences · 2022
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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

2 authors at 1 institution in 1 country.

Alexis PietakAllen Discovery Center at Tufts University , Medford, MA , USA.
Michael LevinAllen Discovery Center at Tufts University , Medford, MA , USA.
Tufts University · US

Funding

Tissue Regeneration by Biophysical SignalingR01AR055993 · NIAMS · TUFTS UNIVERSITY MEDFORD · PI KAPLAN, DAVID L., LEVIN, MICHAEL · 2010 to 2018
$3.2M
Decoding the electric face: development and channelopathy-induced birth defectsR01HD081326 · NICHD · TUFTS UNIVERSITY MEDFORD · PI ADAMS, DANY SPENCER, LEVIN, MICHAEL · 2014 to 2017
$1.3M
Induction of limb development in XenopusR03HD081401 · NICHD · TUFTS UNIVERSITY MEDFORD · PI LEVIN, MICHAEL · 2014 to 2015
$154k
NIAMS NIH HHS R01 AR055993NICHD NIH HHS R01 HD081326NICHD NIH HHS R03 HD081401
6 · The paper itself

Abstract

Bioelectric cell properties have been revealed as powerful targets for modulating stem cell function, regenerative response, developmental patterning, and tumor reprograming. Spatio-temporal distributions of endogenous resting potential, ion flows, and electric fields are influenced not only by the genome and external signals but also by their own intrinsic dynamics. Ion channels and electrical synapses (gap junctions) both determine, and are themselves gated by, cellular resting potential. Thus, the origin and progression of bioelectric patterns in multicellular tissues is complex, which hampers the rational control of voltage distributions for biomedical interventions. To improve understanding of these dynamics and facilitate the development of bioelectric pattern control strategies, we developed the BioElectric Tissue Simulation Engine (BETSE), a finite volume method multiphysics simulator, which predicts bioelectric patterns and their spatio-temporal dynamics by modeling ion channel and gap junction activity and tracking changes to the fundamental property of ion concentration. We validate performance of the simulator by matching experimentally obtained data on membrane permeability, ion concentration and resting potential to simulated values, and by demonstrating the expected outcomes for a range of well-known cases, such as predicting the correct transmembrane voltage changes for perturbation of single cell membrane states and environmental ion concentrations, in addition to the development of realistic transepithelial potentials and bioelectric wounding signals. In silico experiments reveal factors influencing transmembrane potential are significantly different in gap junction-networked cell clusters with tight junctions, and identify non-linear feedback mechanisms capable of generating strong, emergent, cluster-wide resting potential gradients. The BETSE platform will enable a deep understanding of local and long-range bioelectrical dynamics in tissues, and assist the development of specific interventions to achieve greater control of pattern during morphogenesis and remodeling.

Indexed as

bioelectric simulationpattern formationresting potentialtransmembrane voltage

Identifiers

PMID27458581
PMCPMC4933718
OpenAlexW2471673931

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.