On the Applicability of Computational Intelligence in Transcription Network Modelling

On the Applicability of Computational Intelligence in Transcription Network Modelling

Abstract

On this manuscript, we report the proposal of the use of computational intelligence methods as a powerful and valuable source of mathematical tools for modelling gene expression networks. Gene expression networks are modelled via transcription networks. Transcription networks are graph-oriented mathematical-computational models that attempt to understand gene expression as simpler and smaller systems named network motifs. Each type of transcription network presents a peculiar set of network motifs. On the other hand, neural networks are graph-oriented mathematical models with roots on philosophy, physiology, neuroscience, physics, computer science and other scientific branches that and they have been promising as mathematical models for modeling nonparametric data and systems with hidden laws. On the presented manuscript, we run some simulations, discuss some literatures and finish out with some discussions on the promises for possible future achievements on the field. This work is novel on the sense that it proposes on a single methodology the junction of the fields of gene expression networks and neural networks and at the same time, we give the directions for possible intelligence-based systems; called on the literature “intelligent agents”. We do not solve simple examples or stop on some specific cases; those are left for personal achievements.

Key-words: Systems Biology; Neural Networks; Computational Intelligence; software engineering; bioinformatics; gene expression modelling; transcription networks.