Regulatory Crosstalk Analysis of Biochemical Networks in the Hippocampus and Nucleus Accumbens

Abstract

This chapter describes mathematical modeling of neuronal biochemical pathways, especially for pathological and non-pathological features of molecular and cellular mechanisms in the hippocampus and nucleus accumbens. We modeled both types of neurons with a variety of techniques: dynamic equations, constraint-based modeling, and complex network analysis. The last two approaches are called static modeling. In this chapter, we introduced these 3 methods to model the process of signal transduction, metabolism, ion fluxes, and gene regulation in a neuron, and their recent applications to the pathological characterization of the system. (1) The first one is a model of synaptic plasticity in the hippocampal CA1 neurons, which is thought to be relevant for learning and memory. We selected a constraint-based approach to model the cell, which uses constraint conditions in models from the stoichiometry matrix of chemical reactions in the absence of kinetic data. (2) The second model focuses on hippocampal signaling pathways in Alzheimer’s disease, including neurite outgrowth, synaptic plasticity and neuronal death. This is an application of complex network analysis to biological networks, with a particular emphasis on the k shortest path and the k-cycle. (3) The synaptic plasticity in medium spiny neurons in the nucleus accumbens is the main topic of the third model, which is thought to be relevant for reward system. An approach to reveal the dynamic properties of the model is a conventional ordinary differential equation-based modeling and perturbation analysis. Finally, brief concluding remarks appear in Sect. 4.5.

Keywords

Molecular systems neuroscience ODE model Stoichiometric model Complex network analysis Synaptic plasticity Learning system Reward system Drug addiction Systems biology Computational neuroscience Chemical reactions Dynamic model Static model Signal transduction Genetic network Hippocampus Nucleus accumbens Psychostimulant Extreme pathway analysis k shortest path k-cycle Sensitivity Microarray analysis 

List of Acronyms

amyloid β

AC

adenylate cyclase

ACh

acetylcholine

AD

Alzheimer’s disease

ADP

adenosine diphosphate

ADN

Alzheimer’s disease network

AMPH

amphetamine

AMPT

α-methyl paratyrosine

APP

amyloid β precursor protein

ATP

adenosine triphosphate

AMPAR

alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate receptor

BDNF

brain-derived neurotrophic factor

cAMP

cyclic adenosine monophosphate

CaM

calmodulin

CaMKII

calcium/calmodulin-dependent protein kinase type II

CaN

calcineurin

CDK5

cyclin dependent kinase 5

CN

control network

CREB

cAMP responsive element binding protein

DA

dopamine

DARPP-32

dopamine- and cAMP-regulated phosphoprotein of 32-kDa

DAT

dopamine transporter

EGF

epidermal growth factor

EP

extreme pathway

ER

endoplasmic reticulum

ES

enzyme-substrate (complex)

ESF

extreme signaling flow

FasL

Fas ligand

GABA

γ-Aminobutyric acid

GAP

GTPase-activating protein

Glu

glutamate

HFS

high frequency stimulation

ICAD

inhibitor of caspase-activated DNase

IGF1

insulin-like growth factor-1

IP3

inositol 1, 4, 5-phosphate

I1

inhibitor 1

LFS

low frequency stimulation

LTD

long-term depression

LTP

long-term potentiation

mGluR

metabotropic glutamate receptor

MAPK

mitogen-activated protein kinase

MAO

monoamine oxidase

MDD

major depressive disorder

MINT-1

Munc18-interacting protein 1

MSNs

medium spiny neurons

NAc

nucleus accumbens

NFAT

nuclear factor of activated T cells

Ng

neurogranin

NGF

nerve growth factor

NMDAR

N-methyl-D-aspartate receptor

NRG

neuregulin

NT

neurotrophin

ODE

ordinary differential equation

PDE

phosphodiesterase

PKA

protein kinase A

PKC

protein kinase C

PLCβ

phospholipase Cβ

PP1

protein phosphatase 1

PP2A

protein phosphatase 2A

PP2B

protein phosphatase 2B (a.k.a. calcineurin)

RRN

randomly removed network

SN

substantia nigra

TH

tyrosine hydroxylase

TNFα

tumor necrosis factor-α

VMAT2

vesicular monoamine transporter 2

VTA

ventral tegmental area

Notes

Acknowledgments

The authors would like to thank Professor Masaru Tomita, Yoshiya Matsubara, and Ryoji Yanashima at Institute for Advanced Biosciences, Keio University, Professor Shun Ishizaki at Faculty of Environment and Information Studies, Keio University, and Dr. Zhen Qi at Department of Biomedical Engineering, Georgia Institute of Technology for helpful discussions. Part of this chapter was rewritten from collaborative papers with these coauthors.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.The Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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