{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:41Z","timestamp":1760242421118,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,7,4]],"date-time":"2017-07-04T00:00:00Z","timestamp":1499126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["8U54MD007588","G12MD007602","P30 HL107238"],"award-info":[{"award-number":["8U54MD007588","G12MD007602","P30 HL107238"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>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 \u201cConnectosome\u201d, and propose new venues of computational data structures based on a conceptual framework called \u201cGrand Ensemble\u201d 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.<\/jats:p>","DOI":"10.3390\/computation5030032","type":"journal-article","created":{"date-parts":[[2017,7,4]],"date-time":"2017-07-04T10:31:57Z","timestamp":1499164317000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience"],"prefix":"10.3390","volume":"5","author":[{"given":"William","family":"Seffens","sequence":"first","affiliation":[{"name":"Physiology Department, Morehouse School of Medicine, Atlanta, GA 30310, USA"},{"name":"Seftec, Incorporated, Atlanta, GA 30318 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Talbi, E.-G. 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