ReviewTheoretical biology & medical modelling2005
Three subsets of sequence complexity and their relevance to biopolymeric information.
Review in Theoretical biology & medical modelling, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Assessing, Testing and Estimating the Amount of Fine-Tuning by Means of Active Information.Entropy (Basel, Switzerland) · 2022Article
- Foldamer hypothesis for the growth and sequence differentiation of prebiotic polymers.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Redundancy of the genetic code enables translational pausing.Frontiers in genetics · 2014Review
- Prebiotically plausible mechanisms increase compositional diversity of nucleic acid sequences.Nucleic acids research · 2012Article
- Dichotomy in the definition of prescriptive information suggests both prescribed data and prescribed algorithms: biosemiotics applications in genomic systems.Theoretical biology & medical modelling · 2012Review
- Is life unique?Life (Basel, Switzerland) · 2011Review
- Thermodynamic perspectives on genetic instructions, the laws of biology and diseased states.Comptes rendus biologies · 2011Article
- The Universal Plausibility Metric (UPM) & Principle (UPP).Theoretical biology & medical modelling · 2009Article
- The capabilities of chaos and complexity.International journal of molecular sciences · 2009Review
- Estimating the fraction of non-coding RNAs in mammalian transcriptomes.Bioinformatics and biology insights · 2008Article
- Measuring the functional sequence complexity of proteins.Theoretical biology & medical modelling · 2007Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic algorithms instruct sophisticated biological organization. Three qualitative kinds of sequence complexity exist: random (RSC), ordered (OSC), and functional (FSC). FSC alone provides algorithmic instruction. Random and Ordered Sequence Complexities lie at opposite ends of the same bi-directional sequence complexity vector. Randomness in sequence space is defined by a lack of Kolmogorov algorithmic compressibility. A sequence is compressible because it contains redundant order and patterns. Law-like cause-and-effect determinism produces highly compressible order. Such forced ordering precludes both information retention and freedom of selection so critical to algorithmic programming and control. Functional Sequence Complexity requires this added programming dimension of uncoerced selection at successive decision nodes in the string. Shannon information theory measures the relative degrees of RSC and OSC. Shannon information theory cannot measure FSC. FSC is invariably associated with all forms of complex biofunction, including biochemical pathways, cycles, positive and negative feedback regulation, and homeostatic metabolism. The algorithmic programming of FSC, not merely its aperiodicity, accounts for biological organization. No empirical evidence exists of either RSC of OSC ever having produced a single instance of sophisticated biological organization. Organization invariably manifests FSC rather than successive random events (RSC) or low-informational self-ordering phenomena (OSC).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.