Cerebellar long-term depression (LTD) (Antunes and De Schutter 2012)

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Accession:141270
Many cellular processes involve small number of molecules and undergo stochastic fluctuations in their levels of activity. Among these processes is cerebellar long-term depression (LTD), a form of synaptic plasticity expressed as a reduction in the number of synaptic AMPA receptors (AMPARs) in Purkinje cells. Using a stochastic model of the signaling network and mechanisms of AMPAR trafficking involved in LTD, we show that the network activity in single synapses switches between two discrete stable states (LTD and non-LTD). Stochastic fluctuations affecting more intensely the level of activity of a few components of the network lead to the probabilistic induction of LTD and threshold dithering. The non-uniformly distributed stochasticity of the network allows the stable occurrence of several different macroscopic levels of depression, determining the experimentally observed sigmoidal relationship between the magnitude of depression and the concentration of the triggering signal.
Reference:
1 . Antunes G, De Schutter E (2012) A stochastic signaling network mediates the probabilistic induction of cerebellar long-term depression J. Neurosci. 32:9288-9300 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum purkinje cell;
Channel(s):
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: STEPS;
Model Concept(s): Synaptic Plasticity;
Implementer(s): De Schutter, Erik [erik at oist.jp]; Antunes, Gabriela [gabri_antunes at hotmail.com];
Search NeuronDB for information about:  Cerebellum purkinje cell; AMPA;
  
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LTDmodel
README.txt
Info_on_the_model.pdf
ltd.py
ltd_updated.py
                            
G. Antunes and E. De Schutter: A stochastic signaling network mediates
the probabilistic induction of cerebellar long-term
depression. Journal of Neuroscience 32: 9288-9300 (2012).

We provide two versions of the model script, ltd.py and
ltd_updated.py, which were created for different versions of STEPS:
- current versions of STEPS and any version from 1.2 on: use
  ltd_updated.py
- old versions of STEPS (before 1.2): use ltd.py

The work described in the paper was done with a pre-release version of
STEPS, equivalent to version 1.0.0.  The ltd-py script is included
mainly for reference, we strongly advise to use ltd_updated.py with
the most recent STEPS version. We deem the two scripts to be
numerically equivalent for the respective STEPS versions.

The differences between STEPS versions 1.2 and above, and STEPS
versions below 1.2, are in how some reaction constants are
interpreted. For a zero-order reaction (in STEPS this is a reaction
which has no reactants) the unit for the reaction constant in STEPS
1.2 and above is M/s, which defines the molar concentration of the
product(s) such a reaction will produce per second. In previous
versions of STEPS the unit was /s and defined the number of molecules
of the product(s) produced per second.

For a 2-dimensional reaction, that is a reaction where all reactants
are situated in a 'patch', units are based on square meters (since
there is no 2D equivalent of the liter). Therefore, for a 2nd-order
reaction the units in STEPS 1.2 and above are m^2/(mol.s).  All other
reactions are unaffected.

Please refer to STEPS documentation at
http://steps.sourceforge.net/manual/manual_index.html for further
information about STEPS.

To install STEPS please visit:
http://steps.sourceforge.net/STEPS/Download.html This script requires
that you also have NumPy, SciPy and Matplotlib installed.

At the end of the simulations, 5 figures will be plotted sequentially
using Matplotlib. If you don't have Matplotlib installed, please check
the Python documentation to obtain information on how to save the
results of the simulations, and use Matlab or a similar program to
open them.

Antunes G, De Schutter E (2012) A stochastic signaling network mediates the probabilistic induction of cerebellar long-term depression J. Neurosci. 32:9288-9300[PubMed]

References and models cited by this paper

References and models that cite this paper

Anwar H, Hepburn I, Nedelescu H, Chen W, De Schutter E (2013) Stochastic calcium mechanisms cause dendritic calcium spike variability J Neurosci 33(40):15848-15867 [Journal]

   Stochastic calcium mechanisms cause dendritic calcium spike variability (Anwar et al. 2013) [Model]

Anwar H, Roome CJ, Nedelescu H, Chen W, Kuhn B, De Schutter E (2014) Dendritic diameters affect the spatial variability of intracellular calcium dynamics in computer models Frontiers in Cellular Neuroscience 8(168):1-14 [Journal] [PubMed]

   [1 reconstructed morphology on NeuroMorpho.Org]
   Calcium dynamics depend on dendritic diameters (Anwar et al. 2014) [Model]

Chen W, De Schutter E (2014) Python-based geometry preparation and simulation visualization toolkits for STEPS Front. Neuroinform. 8:37 [Journal]

   Python-based toolkits for STEPS (Chen and De Schutter 2014) [Model]

Hepburn I, Jain A, Gangal H, Yamamoto Y, Tanaka-Yamamoto K, De Schutter E (2017) A model of induction of cerebellar Long-Term Depression including RKIP inactivation of Raf and MEK Frontiers in Molecular Neuroscience [Journal]

   A model of cerebellar LTD including RKIP inactivation of Raf and MEK (Hepburn et al 2017) [Model]

Jedrzejewska-Szmek J, Luczak V, Abel T, Blackwell KT (2017) ß-adrenergic signaling broadly contributes to LTP induction PLOS Computational Biology 13(7):e1005657 [Journal] [PubMed]

   Signaling pathways underlying LTP in hippocampal CA1 pyramidal cells (Jedrzejewska-Szmek et al 2017) [Model]

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