Computational neurogenetics models: integration of gene regulatory networks and artificial neural networks

The properties of cells for example neurons are determined by their proteins. Types and amount of proteins areadopted in response to inner and outer signals.Obviously, properties of neurons are determining factor for structure and dynamics of neuronal networks. Theinteractions between genes in neurons have effect on dynamicity of any designed model for nervous system andthis effect is temporary and related to gene expression functionGene regulatory networks that determined gene expression function are consisted of regulating molecules likeDNA, RNA or protein or a set of them. Relationship among these molecules can be modeled by artificial neuralnetworks by considering a coefficient for every relationship to show the relationship significance.In computer sciences the artificial neural networks are statistical learning models inspired by biological neuralnetworks that are used for machine learning.In this review we want to describe Computational Neurogenetic Modeling )CNGM( for brain event, that is:(1) how to model dynamics of gene/protein (2) how to relate model parameters to gene/protein (3) how to selectwhich gen/protein variable should be in model (4) validation of CNGM for real brain data

Keywords:

Computational Neurogenetic Modeling , Neurogenetics , Neuroinformatics