The effects of the acute administration of low-dosage ethanol on the phasic neurochemical oscillations of the basal ganglia

Noori, HR

doi:10.1093/imammb/dqr006

Cited by 6 publications

(9 citation statements)

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“…The spatial distribution of ethanol within the brain, the time course of the blood concentration of ethanol, and its agonizing or antagonizing effects on the activity of the neurotransmitter systems can be directly embedded within the framework of the above mathematical model. The interested reader is advised to review the detailed mathematical aspects of drug modeling in Noori (). In order to validate this model, we focused on its outcomes regarding the modeling of the effects of acute ethanol administration in adult rats in comparison with published microdialysis reports (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, in silico investigations of drug‐induced molecular and physiological alterations within the brain are helpful in understanding disease progression and pharmacological treatment efficacy. Various mathematical models have already been developed to describe the dynamics of molecular and physiological processes within the brain (Durstewitz, Kelc & Güntürkün ; Ahmed, Bobashev & Gutkin ; Kobayashi & Schultz ; Noori & Jager ; Noori ), but these models are confronted with similar difficulties: the complexity of brain tissue and neuronal connectivity. Thus, mathematicians are forced to restrict their models to either single‐cell properties, small neuronal networks or simplified brain region networks (Cervantes & Fraguela Collar ; Cohen & Frank ).…”

Section: Discussionmentioning

confidence: 99%

“…Following the modeling strategy by Noori (), the dynamical changes in the extracellular neurotransmitter concentrations

s_{i}^{k}

(transmitter k in region i ), as measured by microdialysis techniques, are described as systems of non‐linear delay differential equations:

\begin{matrix} \frac{d s_{i}^{k}}{d t} = \sum_{m = 0}^{deg (i)} - f_{i m}^{k} false(Γ_{i} false(t - τ_{i}^{i n} false(s_{i}^{k} false(t false) false) false) false) s_{i}^{k} false(t false) \\ + g_{m i}^{k} (Γ_{m} (t - τ_{i}^{i n} (s_{i}^{k} (t))), μ_{m}^{k}) s_{m}^{k} (t - τ_{m}^{e x}) + σ_{i}^{k} (s_{i}^{k} (t)), \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…As opportunities to use and abuse drugs have increased dramatically during the past 50 years, so has addiction research. A systematic review on publication trends in the addiction research field also indicates the increasing application of neurocomputational methods (Chen ; Ahmed & Koob ; Gutkin, Dehaene & Changeux ; Bobashev, Costenbader & Gutkin ; Noori & Jäger ; Helinski & Spanagel ; Noori ; Noori ). These models deal with molecular processes at the system level and as such require essential information on large‐scale brain topology, in terms of regional connectivities and neurochemical localizations.…”

Section: Introductionmentioning

confidence: 99%

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Neurocircuitry for modeling drug effects

2012

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The identification and functional understanding of the neurocircuitry that mediates alcohol and drug effects that are relevant for the development of addictive behavior is a fundamental challenge in addiction research. Here we introduce an assumption-free construction of a neurocircuitry that mediates acute and chronic drug effects on neurotransmitter dynamics that is solely based on rodent neuroanatomy. Two types of data were considered for constructing the neurocircuitry: (1) information on the cytoarchitecture and neurochemical connectivity of each brain region of interest obtained from different neuroanatomical techniques; (2) information on the functional relevance of each region of interest with respect to alcohol and drug effects. We used mathematical data mining and hierarchical clustering methods to achieve the highest standards in the preprocessing of these data. Using this approach, a dynamical network of high molecular and spatial resolution containing 19 brain regions and seven neurotransmitter systems was obtained. Further graph theoretical analysis suggests that the neurocircuitry is connected and cannot be separated into further components. Our analysis also reveals the existence of a principal core subcircuit comprised of nine brain regions: the prefrontal cortex, insular cortex, nucleus accumbens, hypothalamus, amygdala, thalamus, substantia nigra, ventral tegmental area and raphe nuclei. Finally, by means of algebraic criteria for synchronizability of the neurocircuitry, the suitability for in silico modeling of acute and chronic drug effects is indicated. Indeed, we introduced as an example a dynamical system for modeling the effects of acute ethanol administration in rats and obtained an increase in dopamine release in the nucleus accumbens-a hallmark of drug reinforcement-to an extent similar to that seen in numerous microdialysis studies. We conclude that the present neurocircuitry provides a structural and dynamical framework for large-scale mathematical models and will help to predict chronic drug effects on brain function.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Following the modeling strategy by Noori (), the dynamical changes in the extracellular neurotransmitter concentrations

s_{i}^{k}

(transmitter k in region i ), as measured by microdialysis techniques, are described as systems of non‐linear delay differential equations:

\begin{matrix} \frac{d s_{i}^{k}}{d t} = \sum_{m = 0}^{deg (i)} - f_{i m}^{k} false(Γ_{i} false(t - τ_{i}^{i n} false(s_{i}^{k} false(t false) false) false) false) s_{i}^{k} false(t false) \\ + g_{m i}^{k} (Γ_{m} (t - τ_{i}^{i n} (s_{i}^{k} (t))), μ_{m}^{k}) s_{m}^{k} (t - τ_{m}^{e x}) + σ_{i}^{k} (s_{i}^{k} (t)), \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neurocircuitry for modeling drug effects

2012

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“…Noori et al, 2012) [14,15,16] mainly apply deterministic dynamical systems to identify druginduced alterations of electrophysiological and or neurochemical network characteristics. …”

mentioning

confidence: 99%

Identification of Cost Effective Drug Through in-Silico Approach of Catharanthus Roseus Plant Ligand-Receptor Docking for Type 2 Diabetes Niddm.

Tolambiya¹

2017

WJPPS

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Objective:-Diabetes type2 is common disease found in every agesegment of human beings. This study was proposed to design cost effective drug through herbal medicinal plant Catharanthus roseus and its parts, directly or indirectly being used for diabetes treatment.Methodology:-GCMS is conducted on alcoholic stem extraction to identify the different alkaloids substances. The chemical structure of these alkaloids was identified through pubchem database. PTB1B is a novel target for type 2 diabetes and its 3D structure designed by target sequence. The blast of target sequence was performed with PDB to get template 1NL9 which is 100% identical with target sequence. As a result, 1NL9 resolution is found at least 2.0 Angstroms and its R-factor is not greater than 20.0, hence this structure is considered as a model or a receptor. Docking of these chemical structure of alkaloids against protein-tyrosine phosphatase1B(PTP1B)is done through Auto dock 4.2 software. The present study involves computation of 21 compound to identify the least energy molecules. These molecules follows the Lipinski's rule and assist the drug development avoiding expensive post clinical experiments. This process provides the actual active compound for drug discovery. Conclusion:-It is anticipated that 3-Nitrophthalic Acid has least energy molecule and it fulfills the Lipinski rules of five, hence it could inhibit the PTP1B and improve insulin action for recovery of Type-2 Diabetes. Such studies reduce the time and costs involved in drug discovery process and have no adverse effects on the environment.

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