Dynamic Interaction Networks and Global Ontology-Based Modelling of Brain Dynamics

Abstract

With the advancements of bioinformatics and brain research technologies more and more data becomes available tracing the activity of genes, neurons, neural networks and brain areas over time. How can such data be used to create dynamic models that capture dynamic interactions at a particular functional level and across levels over time? The paper addresses these questions through dynamic interaction network (DIN) modelling: first, at a genetic level, a Gene Regulatory Network (GRN) model can be created from a time series gene expression data; second, at a cognitive level, a DIN can be created from a time series of data (e.g. LFP/EEG data) related to perceptual or cognitive functions; and third, a DIN model can be developed for cross-level dynamic interactions, e.g. between GRN and brain signals measured as LFP/EEG. We conclude with introducing brain-gene ontology integrated environment for representing and modelling brain-gene dynamic relations.