Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience
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 associati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/5/3/32 |
| _version_ | 1817998394178666496 |
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| author | William Seffens |
| author_facet | William Seffens |
| author_sort | William Seffens |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T02:52:49Z |
| format | Article |
| id | doaj.art-c2c426909002463d8fda853ce16719c7 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-04-14T02:52:49Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-c2c426909002463d8fda853ce16719c72022-12-22T02:16:14ZengMDPI AGComputation2079-31972017-07-01533210.3390/computation5030032computation5030032Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for ResilienceWilliam Seffens0Physiology Department, Morehouse School of Medicine, Atlanta, GA 30310, USAMuch 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.https://www.mdpi.com/2079-3197/5/3/32biology-inspired computinggenetic programmingdynamic optimizationGrand EnsemblePersistent Turing Machineresilience |
| spellingShingle | William Seffens Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience Computation biology-inspired computing genetic programming dynamic optimization Grand Ensemble Persistent Turing Machine resilience |
| title | Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience |
| title_full | Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience |
| title_fullStr | Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience |
| title_full_unstemmed | Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience |
| title_short | Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience |
| title_sort | anomalous diffusion within the transcriptome as a bio inspired computing framework for resilience |
| topic | biology-inspired computing genetic programming dynamic optimization Grand Ensemble Persistent Turing Machine resilience |
| url | https://www.mdpi.com/2079-3197/5/3/32 |
| work_keys_str_mv | AT williamseffens anomalousdiffusionwithinthetranscriptomeasabioinspiredcomputingframeworkforresilience |