  Gene Regulatory Networks are networks of interactions in biological organisms
responsible for determining the production levels of proteins and peptides.
Proteins are workers of a cell factory, and their production defines the goal
of a cell and its development. Various attempts have been made to model such
networks both to understand these biological systems better and to use
inspiration from understanding them to solve computational problems. In this
work, a biologically more realistic model for gene regulatory networks is
proposed, which incorporates Cellular Automata and Artificial Chemistry to
model the interactions between regulatory proteins called the Transcription
Factors and the regulatory sites of genes. The result of this work shows
complex dynamics close to what can be observed in nature. Here, an analysis of
the impact of the initial states of the system on the produced dynamics is
performed, showing that such evolvable models can be directed towards producing
desired protein dynamics.
