A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)

 Download zip file 
Help downloading and running models
Accession:150207
A machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the activation of synaptic receptors, at very low computational cost. The method is designed to learn patterns and general principles from previous Monte Carlo simulations and to predict synapse behavior from them. The resulting procedure is accurate, automatic and can predict synapse behavior under experimental conditions that are different to the ones used during the learning phase. Since our method efficiently reduces the computational costs, it is suitable for the simulation of the vast number of synapses that occur in the mammalian brain.
Reference:
1 . Montes J, Gomez E, Merchan-Perez A, Defelipe J, Pena JM (2013) A machine learning method for the prediction of receptor activation in the simulation of synapses. PLoS One 8:e68888 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: Java; R;
Model Concept(s): Simplified Models;
Implementer(s): Montes, Jesus [jmontes at cesvima.upm.es];
Search NeuronDB for information about:  AMPA; Glutamate;
*******************************************************************************
*
*  A Machine Learning Method for the Prediction of Receptor
*  Activation in the Simulation of Synapses
*  Copyright (C) 2013 J. Montes, E. Gomez, A. Merchan-Perez, J. DeFelipe,
*                     J. M. Peña
*
*  This program is free software: you can redistribute it and/or modify
*  it under the terms of the GNU Lesser General Public License as published
*  by the Free Software Foundation, either version 3 of the License, or
*  (at your option) any later version.
*
*  This program is distributed in the hope that it will be useful,
*  but WITHOUT ANY WARRANTY; without even the implied warranty of
*  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
*  GNU Lesser General Public License for more details.
*
*  You should have received a copy of the GNU Lesser General Public License
*  along with this program.  If not, see <http://www.gnu.org/licenses/>.
*
*******************************************************************************

DISCLAIMER:

This is a lab development, intended for use only in experiments and not
for full distribution. Familiarity with UNIX-like systems (Linux, Mac,
etc.) command line operation is required for its use. An improved, more
user-friendly version fot his software is in development.


*******************************************************************************

TOOL:

A Machine Learning Method for the Prediction of Receptor Activation in the
Simulation of Synapses

*******************************************************************************

AUTHORS:

- J. Montes
- E. Gomez
- A. Merchán-Perez
- J. DeFelipe
- J. M. Peña

*******************************************************************************

VERSION:

1.0 alpha (pre-release)

*******************************************************************************

DESCRIPTION:

This is an implmentation of our machine-learning-based AMPA receptor
activation prediction model.

*******************************************************************************

SYSTEM REQUIREMENTS:

- UNIX-like command line environment (Linux, MacOS X or similar).
  Windows is not directly supported. This software could be executed
  in Windows using cygwin, or other tool capable of creating a
  Linux-like environment.
- Java 1.6 or higher.
- The R statistical tool (http://www.r-project.org/). This is used during
  the curve-fitting process. Previous verisons of this software used MATLAB
  for this task, but we have replace it with R, which produces the same result
  with improved performance. In adittion, R is free, like the rest of this
  program requirements.

*******************************************************************************

COMPONENTS:

- ML-AMPA.sh: This is the main program file. It is a bash
  shell script that performs the basic curve prediction tasks.
- AMPA.O_model_M5P.bin: This is the machine-learning model. It has been
  previously trained using a synapse dataset including 1000 different
  synapse configurations.
- weka.jar: The machine learning libary.
- src and bin directories: They contain the Java sorice code and binary files
  of the AMPA receptor activation prediction model.

*******************************************************************************

CONFIGURATION:

Before using this software, it has to be properly configured. To do so,
the ML-AMPA.sh file must be edited. More specifically, the R_HOME variable
inside this script has to be correctly set to the system path where R is
installed. Without R the program cannot perform the final curve-fitting stage
of the AMPA receptor activation prediction.

*******************************************************************************

USAGE:

To user this software, just change into the directory where the component files
are and run the ML-AMPA.sh script. This script requires a set of 5 arguments to
operate. These are the values of the synapse parameters:

- [AMPA]: AMPA concentration, in molecues per square micron.
- [T]   : Transporter concentration, in molecues per square micron.
- Ls    : Synapse length, in nm.
- Hc    : Synapse height, in nm.
- E     : Side of total apposition lenght, relative factor to Ls

Fo example, running the following command:

$ ./ML-AMPA.sh 2000 1600 500 16 1.5

Would predict the AMPA receptor activation curve of a syanpse with 2000 AMPA
receptors per square micrion, 1600 transporters per square micron, 500 nm of
synaptic length, 16 nm of synaptic height and a total apposition lenght of 1.5
times Ls, that is 750 nm in total.

Running this script will generate a csv file containing the predicted AMPA
activation curve, sampled in 0.05 ms intervals. The results file is called 
result.csv.

*******************************************************************************
*******************************************************************************

Loading data, please wait...