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

• Main Author: William Seffens
• Format: Article
• Language: English
• Published: MDPI AG 2017-07-01
• Series: Computation
• Subjects: biology-inspired computing; genetic programming; dynamic optimization; Grand Ensemble; Persistent Turing Machine; resilience
• Online Access: https://www.mdpi.com/2079-3197/5/3/32

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.