Evidence map›Paper›PMID 40321947›Full record

ArticleArXiv2025

Synaptic Spine Head Morphodynamics from Graph Grammar Rules for Actin Dynamics.

Matthew Hur, Thomas Bartol, Padmini Rangamani, Terrence Sejnowski, Eric Mjolsness

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In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Matthew HurProgram in Mathematical, Computational, and Systems Biology, Univeristy of California Irvine, Irvine, CA, USA.
Thomas BartolComputational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
Padmini RangamaniDepartments of Pharmacology and Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, USA.
Terrence SejnowskiComputational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA.
Eric MjolsnessDepartments of Computer Science and Mathematics, Univeristy of California Irvine, Irvine, CA, USA.

Funding

CRCNS: Regulation of assembly and disassembly of the postsynaptic density during synaptic plasticity and its effect on AMPAR trappingR01MH129066 · NIMH · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI KENNEDY, MARY B, SEJNOWSKI, TERRENCE J · 2021 to 2024
$1.2M
Multiscale theory of synapse function with model reduction by machine learningRF1DA055668 · NIDA · UNIVERSITY OF CALIFORNIA-IRVINE · PI MJOLSNESS, ERIC D · 2021 to 2021
$1.1M
NIDA NIH HHS RF1 DA055668NIMH NIH HHS R01 MH129066
6 · The paper itself

Abstract

There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons, in response to the temporal correlation of local presynaptic and postsynaptic signals. These morphological factors are in turn sculpted by the dynamics of the actin cytoskeleton. In this paper, we use Dynamical Graph Grammars (DGGs) implemented within a computer algebra system to model how networks of actin filaments can dynamically grow or shrink, reshaping the spine head. Dynamical Graph Grammars (DGGs) provide a well-defined way to accommodate dynamically changing system structure such as active cytoskeleton represented using dynamic graphs, within nonequilibrium statistical physics under the master equation. We show that DGGs can also incorporate biophysical forces between graph-connected objects at a finer time scale, with specialized DGG kinetic rules obeying biophysical constraints of Galilean invariance, conservation of momentum, and dissipation of conserved global energy. We use graph-local energy functions for cytoskeleton networks interacting with membranes, and derive DGG rules from the specialization of dissipative stochastic dynamics - separated into dissipative and thermal noise rule types - to a mutually exclusive and exhaustive collection of graph-local neighborhood types for the rule left hand sides. The dissipative rules comprise a stochastic version of gradient descent dynamics. The thermal noise rules use a Gaussian approximation of each position coordinate to sample jitter-like displacements. For the spine head model we designed and implemented DGG grammar mathematical sub-models including actin network growth, non-equilibrium statistical mechanics, and filament-membrane mechanical interaction to regulate the re-writing of graph objects. We simulate emergent biophysics of simplified networks of actin polymers and their interactions with membranes. From a biological perspective, we observe regulatory effects of three actin-binding proteins (ABPs) on the membrane size and find evidence supporting mechanisms of membrane growth.

Identifiers

PMID40321947
PMCPMC12047902

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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.