Applied informatics: A paradigm shift in artificial embryonic systems development

As it is well known, the synthesis of sophisticated cellular biology processes in artificial silicon structures is quoted as one of today's most highly ranked scientific challenges. However, the failure of most approaches which try to faithfully model cellular biology represents a major impediment which inhibits rapid progress in artificial biological structures development. In other words, the main obstacle seems to be the impossibility to full comprehend biological cell processes evolution and growth in their entirety and especially because of the lack of a precise and authentically models being to reproduce with high fidelity cellular and embryonic phenomena's. In an attempt to devise a model which more closely mimics cellular biology, this paper discusses an original artificial organism model, developed upon a two-layer coarse-fine-grid network approach. The strength of this approach is that it endeavors to capture the complexity of both the cellular networks as well as that of the biological cell itself, by implementing the internal biological phenomena of an organism into a two different network topology hardware layer. In essence, this model not only keeps the full advantages of previously created models that enable the implementation of similar self-replicating or self-repairing, abilities akin to those expressed by its cellular equivalents in nature, but there (according to the nature of cell biology) the inherent need of artificial cell structures to fulfill the entire role of a biological cell in the network is also expressed. At this stage of the research, the main goal it was to demonstrate that the chosen configuration operates correctly and supports the modeling assumptions. Not least of all, such architectures are suitable to deliver useful information about a wide range of imitated cellular processes in a short time and could become a helpful framework for researchers to confirm theoretical principles and experimental work quickly, with the full advantages of an infinite number of repeatability and programmability of biological processes.