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.
