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Author Notes:

E-mail: eberhard.voit@bme.gatech.edu

Conceived and designed the experiments: ZQ FT EOV.

Performed the experiments: ZQ EOV

Analyzed the data: ZQ EOV

Wrote the paper: ZQ FT EOV.

Disclaimer: Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring institutions.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

FT is employed by Isar-Amper-Klinikum gemeinnützige GmbH, Klinikum München-Ost, Haar, Landkreis München, Germany.

This affiliation does not alter the authors' adherence to all PLOS ONE policies on sharing data and materials.


Research Funding:

This work was supported by a grant from the National Institutes of Health (P01-ES016731, Gary W. Miller, PI) and an endowment from the Georgia Research Alliance (to EOV, PI).


  • Science & Technology
  • Multidisciplinary Sciences
  • Science & Technology - Other Topics

A Heuristic Model of Alcohol Dependence


Journal Title:



Volume 9, Number 3


, Pages e92221-e92221

Type of Work:

Article | Final Publisher PDF


Background: Substance dependence poses a critical health problem. Sadly, its neurobiological mechanisms are still unclear, and this lack of real understanding is reflected in insufficient treatment options. It has been hypothesized that alcohol effects are due to an imbalance between neuroexcitatory and neuroinhibitory amino acids. However, glutamate and GABA interact with other neurotransmitters, which form a complicated network whose functioning evades intuition and should be investigated systemically with methods of biomedical systems analysis. Methods and Results: We present a heuristic model of neurotransmitters that combines a neurochemical interaction matrix at the biochemical level with a mobile describing the balances between pairs of neurotransmitters at the physiological and behavioral level. We investigate the effects of alcohol on the integrated neurotransmitter systems at both levels. The model simulation results are consistent with clinical and experimental observations. The model demonstrates that the drug diazepam for symptoms of alcohol withdrawal effectively reduces the imbalances between neurotransmitters. Moreover, the acetylcholine signal is suggested as a novel target for treatment of symptoms associated with alcohol withdrawal. Conclusions: Efficient means of integrating clinical symptoms across multiple levels are still scarce and difficult to establish. We present a heuristic model of systemic neurotransmitter functionality that permits the assessment of genetic, biochemical, and pharmacological perturbations. The model can serve as a tool to represent clinical and biological observations and explore various scenarios associated with alcohol dependence and its treatments. It also is very well suited for educational purposes.

Copyright information:

© 2014 Qi et al.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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