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This article is part of the supplement: Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access Open Badges Poster presentation

Reverse engineering of metabotropic glutamate receptor-dependent long-term depression in the hippocampus

Tim Tambuyzer1, Tariq Ahmed2, Daniel Berckmans1, Detlef Balschun2 and Jean-Marie Aerts1*

Author Affiliations

1 Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, Katholieke Universiteit Leuven, Leuven, B-3001, Belgium

2 Department of Psychology, Laboratory for Biological Psychology, Katholieke Universiteit Leuven, Leuven, 3000, Belgium

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BMC Neuroscience 2011, 12(Suppl 1):P1  doi:10.1186/1471-2202-12-S1-P1

The electronic version of this article is the complete one and can be found online at:

Published:18 July 2011

© 2011 Tambuyzer et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

This study focused on metabotropic glutamate receptor-dependent long-term depression (mGluR–LTD) in the hippocampus. This form of LTD is suggested to play a key role in learning, memory and the plasticity of behaviour. Recent advances have started to uncover the underlying mechanisms of mGluR-LTD [1]. However, it is not completely clear how these mechanisms are linked and it is believed that several crucial mechanisms still remain to be revealed.

The two main objectives of this study were (i) to quantify the dynamics of mGluR-LTD responses by dynamic data-based models and (ii) to identify underlying dominant processes of mGluR-LTD by applying mathematical system identification methods. In recent years, more and more researchers advocate the use of a top-down modelling approach (reverse engineering) for improving the knowledge of biological systems [2,3].

The drug dihydroxyphenylglycine (DHPG) was used to induce mGluR-LTD in rat brain slices (table 1). The drug was applied for different durations (5min, 15min, 2 hours) and in different concentrations (15mM, 30mM). In addition, also different sampling intervals (5min, 30s, 90s) were used.

Table 1. Overview of the experiments

For the modelling, discrete-time Transfer Functions (TF) models were used. The models described the relation between the DHPG application (input) and the long-term depression responses (output).

All models were very accurate (all RT2-values higher than 0,94) and reliably estimated. For a 2 hours application of 30 µM DHPG sampled with a frequency of 1/30s, the time-constant of the mGluR-LTD response was 92s. Thus, the models for high sampling rate indicated that a sampling interval of 30s would be ideal to minimize information loss of the dynamics of mGluR-LTD responses.

Interestingly, it was suggested that there are three dominant sub-processes underlying mGluR-LTD: one fast sub-process, one slow sub-process and an immediate sub-process.

This study suggests that the dynamic data-based modelling approach can be a valuable tool for reverse engineering of mGluR-dependent LTD responses.


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