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


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.


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


adenylate cyclase




Alzheimer’s disease


adenosine diphosphate


Alzheimer’s disease network




α-methyl paratyrosine


amyloid β precursor protein


adenosine triphosphate


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


brain-derived neurotrophic factor


cyclic adenosine monophosphate




calcium/calmodulin-dependent protein kinase type II




cyclin dependent kinase 5


control network


cAMP responsive element binding protein




dopamine- and cAMP-regulated phosphoprotein of 32-kDa


dopamine transporter


epidermal growth factor


extreme pathway


endoplasmic reticulum


enzyme-substrate (complex)


extreme signaling flow


Fas ligand


γ-Aminobutyric acid


GTPase-activating protein




high frequency stimulation


inhibitor of caspase-activated DNase


insulin-like growth factor-1


inositol 1, 4, 5-phosphate


inhibitor 1


low frequency stimulation


long-term depression


long-term potentiation


metabotropic glutamate receptor


mitogen-activated protein kinase


monoamine oxidase


major depressive disorder


Munc18-interacting protein 1


medium spiny neurons


nucleus accumbens


nuclear factor of activated T cells




nerve growth factor


N-methyl-D-aspartate receptor






ordinary differential equation




protein kinase A


protein kinase C


phospholipase Cβ


protein phosphatase 1


protein phosphatase 2A


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


randomly removed network


substantia nigra


tyrosine hydroxylase


tumor necrosis factor-α


vesicular monoamine transporter 2


ventral tegmental area



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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© 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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