Cells of multicellular organisms communicate with each other through gap junctions composed of connexin proteins. The totality of gap junction communication among a group of cells is a Gap Junction Bioelectric Network (GJBN). Even though the rule governing a GJBN may be simple, its computations can be complex.
The Principle of Computational Equivalence (PCE) holds that Wolfram cellular automaton #110 is sufficiently complex to model the computations of a complex GJBN. The Principle of Computational Irreducibility (PCI) maintains that a complex GJBN cannot be modeled accurately and comprehensively by 'shortcut' equations. Instead, the system must be 'run' to observe its outcome.
It is proposed that aging and cancer are the result of chronic entropic dysregulation of the complex asymmetric GJBN modeled by cellular automaton #110 into a symmetric random network modeled by cellular automaton #30. Consequently, asymmetric morphogen gradients necessary for the geometric stability of the organism (morphostasis) are gradually lost, and the organism 'grows' old and develops cancer. The Hayflick limit, shortening of telomeres, and telomerase activity are not the proximate causes of aging and cancer.
Successful cancer prevention/treatment, and anti-aging or rejuvenation strategies will require a systems approach that maintains or restores the complex GJBN modeled by Wolfram cellular automaton #110.
Table of Contents
1. Introduction
2. Main Part
3. Discussion
4. Summary
Objectives and Topics
This paper explores the application of cellular automata, specifically Wolfram's rule #110, as a mathematical model to describe the bioelectric communication networks in multicellular organisms. It investigates how entropic dysregulation within these gap junction bioelectric networks serves as a fundamental mechanism underlying aging and the development of cancer, moving beyond simple biochemical explanations to a systems-based computational perspective.
- Application of Wolfram cellular automaton #110 to Gap Junction Bioelectric Networks (GJBN).
- Role of entropy in the dysregulation of bioelectric network stability and geometry.
- Computational interpretation of aging and cancer as disorders of self-organizing networks.
- Scale invariance and anisotropy in morphogen gradient modeling.
- Systems theory approach to understanding morphogenesis, tissue stability, and cellular behavior.
Excerpt from the Book
Introduction
Cells of multicellular organisms communicate with one another through gap junctions that control the movement of ions and other molecules from the cytoplasm of one cell to the cytoplasm of adjacent cells. Gap junctions are composed of connexin proteins. We define a Gap Junction Bioelectric Network (GJBN) as the totality of gap junction communication among a mass of cells.
Cellular automata are mathematical models of many natural phenomena, including biologic processes such as shell patterns in mollusks. Simple automaton rules can result in complexity, and there is no hierarchy of complexity once a threshold is reached. The threshold for complexity is low and rather easy to reach. More complex rules do not increase complexity. Wolfram cellular automaton #110 is the simplest such rule.
Cellular automaton #110 is complex, Turing complete, and capable of universal computation. It is bound by the Principle of Computational Equivalence (PCE), and the Principle of Computational Irreducibility (PCI). If the GJBN is a complex system, then the PCE holds that #110 is sufficiently complex to model it. The PCI teaches that there are no ‘shortcut’ formulas allowing one to calculate the future state of a complex automaton merely by plugging in a future time (Tfuture). Instead, one must ‘run’ the automaton to see its outcome. In the same way, GJBN modeled by cellular automaton #110 must be ‘run’ to determine the outcome of its computations.
Summary of Chapters
Introduction: Establishes the concept of Gap Junction Bioelectric Networks (GJBN) and proposes that they can be modeled by Wolfram’s cellular automaton #110 to explain complex biological behaviors.
Main Part: Details the fractal and scale-free nature of organisms, explaining how bioelectric signals and morphogen gradients function as computational processes that govern growth, differentiation, and tissue stability.
Discussion: Argues that aging and cancer should be viewed as symptoms of entropic dysregulation within the network, leading to a loss of geometric stability and the transition from complex, organized network states to randomized, inefficient states.
Summary: Reaffirms that treating biological organisms as computational systems rather than mere collections of biochemical reactions provides a robust framework for understanding the systemic roots of disease and the aging process.
Keywords
Gap Junction Bioelectric Network, GJBN, Wolfram cellular automaton #110, Computational Irreducibility, Morphogenesis, Entropy, Entropic dysregulation, Morphostasis, Vmem gradients, Morphogens, Aging, Cancer, Small-world networks, Scale invariance, Bioelectric code
Frequently Asked Questions
What is the core focus of this research?
The work focuses on interpreting the multicellular organism as a Gap Junction Bioelectric Network that can be effectively modeled using the computational rules of Wolfram’s cellular automaton #110.
What are the primary fields discussed in the paper?
The paper bridges biology, systems theory, and computer science, specifically covering cellular biology, bioelectricity, computational complexity, and the theory of aging.
What is the primary goal of the author?
The primary goal is to present a new theoretical framework for aging and cancer, suggesting they are caused by entropic dysregulation of the bioelectric network controlling the organism's geometry.
Which scientific methodology is employed?
The author uses computational modeling based on the Principle of Computational Equivalence and the Principle of Computational Irreducibility to simulate biological development and network failures.
What is covered in the main part of the document?
The main part details how Vmem gradients, morphogens, and gap junctions create a scale-free, fractal network that governs the development and maintenance of multicellular life forms.
How can this work be summarized by its keywords?
The work is defined by the intersection of bioelectric signaling (GJBN), computational complexity (automata theory), and the entropic degradation of biological systems (aging and cancer).
How does the author explain the difference between unicellular and multicellular aging?
The author notes that while unicellular organisms die due to direct damage, multicellular organisms age because they trade individual cell autonomy for group-based bio-stability, which can be disrupted by entropy.
What role does the 'Hayflick limit' play in the author's argument?
The author argues that the Hayflick limit is secondary; the primary cause of aging is the loss of organized network control (morphostasis) that keeps individual cell behavior in check.
How does entropic dysregulation affect the network?
Entropic damage causes the complex, asymmetric network to shift toward a random, symmetric state, leading to a loss of the information flow required to maintain proper tissue and organ morphology.
What practical applications does the author suggest?
The author suggests that future therapies could focus on restoring or maintaining the bioelectric network (GJBN) using electroceuticals or by removing senescent cells that propagate damaging information.
- Quote paper
- MD Dr. Marshall Goldberg (Author), 2018, The cellular automaton interpretation of aging and cancer, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/425564
