Abstract

Biosemiotic entropy involves the deterioration of biological sign systems. The genome is a coded sign system that is connected to phenotypic outputs through the interpretive functions of the tRNA/ribosome machinery. This symbolic sign system (semiosis) at the core of all biology has been termed “biosemiosis”. Layers of biosemiosis and cellular information management are analogous in varying degrees to the semiotics of computer programming, spoken, and written human languages. Biosemiotic entropy — an error or deviation from a healthy state — results from errors in copying functional information (mutations) and errors in the appropriate context or quantity of gene expression (epigenetic imbalance). The concept of biosemiotic entropy is a deeply imbedded assumption in the study of cancer biology. Cells have a homeostatic, preprogrammed, ideal or healthy state that is rooted in genomics, strictly orchestrated by epigenetic regulation, and maintained by DNA repair mechanisms. Cancer is an eminent illustration of biosemiotic entropy, in which the corrosion of genetic information via substitutions, deletions, insertions, fusions, and aberrant regulation results in malignant phenotypes. However, little attention has been given to explicitly outlining the paradigm of biosemiotic entropy in the context of cancer. Herein we distill semiotic theory (from the familiar and well understood spheres of human language and computer code) to draw analogies useful for understanding the operation of biological semiosis at the genetic level. We propose that the myriad checkpoints, error correcting mechanisms, and immunities are all systems whose primary role is to defend against the constant pressure of biosemiotic entropy, which malignancy must shut down in order to achieve advanced stages. In lieu of the narrower tumor suppressor/oncogene model, characterization of oncogenesis into the biosemiotic framework of sign, index, or object entropy may allow for more effective explanatory hypotheses for cancer diagnosis, with consequence in improving profiling and bettering therapeutic outcomes.

Highlights

  • Cancer is a disease characterized by the breakdown of cellular systems that exist to maintain, regulate and replicate genetic information

  • While we focus on genetics as the heart of biosemiosis, higher levels of symbolic biological communication have been noted such as cell signaling cascades, neural networks, and the immune system [13]

  • Some of the formality is new, the biosemiotic perspective articulates many concepts that are implicit in the field [2]

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Summary

Introduction

Cancer is a disease characterized by the breakdown of cellular systems that exist to maintain, regulate and replicate genetic information. Human cells can be studied from numerous vantage points, the most fundamental genetic system is one of information flow: communication using signs, codes and signals (biological semiosis, or biosemiosis) [1,2]. Malignant growth is the result of the accumulation of random alterations from within, leading to a fundamental breakdown in cell cycle control in a process analogous to increasing entropy on a cellular level (though, from the cancer’s point of view, increasing fitness [6]), often with lethal consequences at the organismal level. It comes as no surprise that cancer is classified as the degradation of biological information and communication systems, at both the genetic and epigenetic levels

What Is Biosemiosis?
Why Is It Important to Recognize a Living Organism as a Semiotic System?
Semiosis
Similarities
Differences in Event Instantiation
Differences in Plasticity
Differences
Breaking Points in Biosemiosis
Distinguishing Biosemiotic Entropy as Applied to Cancer
Entropic Pressure Points Cause Cancer
Direct Changes in Factors Regulating the Epigenome
Genomic Entropy in Genes Affecting Epigenetic Factors
Genetic Entropy in Non-Protein Coding Elements
Fighting Biosemiotic Entropy To Maintain Homeostasis
Various Levels of Homeostasis
Entropy at the Population Level
Germline Predispositions towards Somatic Biosemiotic Entropy
Findings
Conclusions
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