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Open AccessArticle

Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience

1
Physiology Department, Morehouse School of Medicine, Atlanta, GA 30310, USA
2
Seftec, Incorporated, Atlanta, GA 30318 USA
Computation 2017, 5(3), 32; https://doi.org/10.3390/computation5030032
Received: 9 March 2017 / Revised: 25 June 2017 / Accepted: 26 June 2017 / Published: 4 July 2017
Much of biology-inspired computer science is based on the Central Dogma, as implemented with genetic algorithms or evolutionary computation. That 60-year-old biological principle based on the genome, transcriptome and proteasome is becoming overshadowed by a new paradigm of complex ordered associations and connections between layers of biological entities, such as interactomes, metabolomics, etc. We define a new hierarchical concept as the “Connectosome”, and propose new venues of computational data structures based on a conceptual framework called “Grand Ensemble” which contains the Central Dogma as a subset. Connectedness and communication within and between living or biology-inspired systems comprise ensembles from which a physical computing system can be conceived. In this framework the delivery of messages is filtered by size and a simple and rapid semantic analysis of their content. This work aims to initiate discussion on the Grand Ensemble in network biology as a representation of a Persistent Turing Machine. This framework adding interaction and persistency to the classic Turing-machine model uses metrics based on resilience that has application to dynamic optimization problem solving in Genetic Programming. View Full-Text
Keywords: biology-inspired computing; genetic programming; dynamic optimization; Grand Ensemble; Persistent Turing Machine; resilience biology-inspired computing; genetic programming; dynamic optimization; Grand Ensemble; Persistent Turing Machine; resilience
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Seffens, W. Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience. Computation 2017, 5, 32.

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