The Hypothesis of the Unified Matrix of the Universe

Authors

  • Aybek Alikulov researcher Author

DOI:

https://doi.org/10.47451/phi2025-10-02

Keywords:

morphogenesis, self-organisation, fractal geometry, holism, universal laws of nature, morphological matrix, theory of complex systems

Abstract

The study is devoted to the development and substantiation of the hypothesis of the Unified Matrix of the Universe, which postulates the existence of a universal morphogenetic principle governing the self-organisation of natural and cosmic systems. According to this hypothesis, the evolution of matter obeys a single “Matrix” code manifested in forms and processes at all levels of organisation — from molecular structures and biological organisms to geophysical and astrophysical systems. The relevance of the work is determined by the need to integrate scientific and philosophical models of self-organisation, which are currently studied in isolation. Contemporary theories — synergetics, fractal geometry, and reaction-diffusion models — demonstrate similar regularities of order emerging from chaos, yet they do not explain their universality. The Matrix hypothesis proposes a unified metatheoretical language for describing these processes, thus contributing to overcoming the fragmentation of modern scientific knowledge. The novelty of the study lies in the formulation of a universal morphogenetic principle uniting Turing’s model, Mandelbrot’s fractal concept, the mechanical theory of tensegrity, and the philosophy of biological relativity. For the first time, these approaches are considered as various manifestations of a single structural logic operating across all scales of being. The subject of the study comprises processes of self-organisation and morphogenesis in natural, biological, and cosmic systems, while the object consists of universal morphogenetic invariants: symmetry, self-similarity, and fractality. The study aims to substantiate the existence of a single morphogenetic law ensuring the recurrence of forms and the stability of the Universe’s structure. To achieve this purpose, a complex of methods has been applied: systems and structural-functional analysis to identify hierarchies of self-organisation; modelling and a mathematical-symbolic approach to formalise universal regularities; the dialectical method to analyse the oppositions of symmetry and asymmetry; synergetic and fractal analysis to describe nonlinear processes of development; and hermeneutic and phenomenological approaches for the philosophical interpretation of the concepts of form and wholeness. The main content of the study covers three key areas. Firstly, Turing’s model of morphogenesis is analysed, explaining the transition from homogeneity to structural order as a result of the interaction between an activator and an inhibitor. Secondly, the phenomenon of fractal self-organisation is revealed, demonstrating that the principles of self-similarity and scale invariance are universal across all levels of matter. Thirdly, a holistic interpretation of morphogenesis is presented, integrating the mechanical, geometrical, and bioelectrical aspects of form formation. Collectively, these results confirm the existence of underlying regularities linking the micro-, meso-, and macro-levels of matter into a single structural system. As a result, a universal morphogenetic scheme has been substantiated, in which the process of development is described as a transition from a symmetric “circle-seed” state to a multilevel structure of “leaf-like unfolding”. The hypothesis has been confirmed through the analysis of reaction-diffusion, fractal, and mechanochemical models, as well as by interdisciplinary comparisons of biological, geophysical, and cosmological data. In conclusion, it is argued that the hypothesis of the Unified Matrix of the Universe forms a new paradigm of holistic morphogenesis, integrating the natural and human sciences. Its further development is associated with numerical modelling, morphometric and fractal analysis, and the philosophical integration of the concepts of symmetry, self-organisation, and form within the framework of a general metatheory of the morphology of being.

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References

Aon, M. A., Cortassa, S., & O’Rourke, B. (2004). Percolation and criticality in a mitochondrial network. Proceedings of the National Academy of Sciences, 101(13), 4447–4452. https://doi.org/10.1073/pnas.0307156101

Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of 1/f noise. Physical Review Letters, 59(4), 381–384. https://doi.org/10.1103/PhysRevLett.59.381

Ball, P. (2012). Shapes: Nature’s patterns – a tapestry in three parts. Oxford University Press.

Benítez, M., Hernández-Hernández, V., Newman, S. A., & Niklas, K. J. (2018). Dynamical patterning modules, biogeneric materials, and the evolution of multicellular plants. Frontiers in Plant Science, 9, 871. https://doi.org/10.3389/fpls.2018.00871

Bookstein, F. L. (1997). Morphometric tools for landmark data: Geometry and biology. Cambridge University Press.

Dassow, M. von, & Davidson, L. A. (2011). Physics and the canalization of morphogenesis: A grand challenge. Current Opinion in Genetics & Development, 21(4), 473–483. https://doi.org/10.1016/j.gde.2011.03.002

Forsström, O. (2022). Turing’s model for pattern formation [Thesis of Dessertation, KTH Royal Institute of Technology]. Stockholm. https://www.diva-portal.org/smash/get/diva2:1678936/FULLTEXT01.pdf

Gisiger, T. (2001). Scale invariance in biology: Coincidence or footprint of a universal mechanism? Biological Reviews, 76(2), 161–209. https://doi.org/10.1017/S1464793101005607

Goldberger, A. L., Peng, C.-K., & Lipsitz, L. A. (2002). What is physiologic complexity and how does it change with aging and disease? Neurobiology of Aging, 23(1), 23–26. https://doi.org/10.1016/S0197-4580(01)00266-4

Ingber, D. E. (2003a). Tensegrity I. Cell structure and hierarchical systems biology. Journal of Cell Science, 116(7), 1157–1173. https://doi.org/10.1242/jcs.00359

Ingber, D. E. (2003b). Tensegrity II. How structural networks influence cellular information processing networks. Journal of Cell Science, 116(8), 1397–1408. https://doi.org/10.1242/jcs.00360

Ivanov, P. Ch., & Bartsch, R. P. (2024). Fractal dynamics and network synchronization of complexity in physiology and medicine. Frontiers in Network Physiology, 4, 1379892. https://doi.org/10.3389/fnetp.2024.1379892

Jaeger, J., & DiFrisco, J. (2019). Beyond networks: Mechanism and process in evo-devo. Acta Biotheoretica, 67(3), 329–356. https://doi.org/10.1007/s10441-019-09366-6

Kondo, S., & Miura, T. (2010). Reaction-diffusion model as a framework for understanding biological pattern formation. Science, 329(5999), 1616–1620. https://doi.org/10.1126/science.1179047

Kurakin, A. (2011). Self-organization versus Watchmaker: Ambiguity of molecular recognition and design charts of living matter. Theoretical Biology and Medical Modelling, 8(1), 4. https://doi.org/10.1186/1742-4682-8-4

Landge, A. N., Jordan, B. M., Diego, X., & Müller, P. (2020). Pattern formation mechanisms of self-organizing reaction-diffusion systems. Developmental Biology, 460(1), 2–11. https://doi.org/10.1016/j.ydbio.2019.10.031

Levin, M. (2014). Endogenous bioelectric networks store non-genetic patterning information during development and regeneration. The Journal of Physiology, 592(11), 2295–2305. https://doi.org/10.1113/jphysiol.2014.271940

Lindenmayer, A. (1968). Mathematical models for cellular interaction in development. Journal of Theoretical Biology, 18(3), 280–299.

Longo, G., & Montevil, M. (2014). Perspectives on organisms: Biological time, symmetries and singularities. Springer.

Macfarlane, F. R., Chaplain, M. A. J., & Lorenzi, T. (2020). A hybrid discrete–continuum approach to model Turing pattern formation. Mathematical Biosciences and Engineering, 17(6), 7442–7479. https://doi.org/10.3934/mbe.2020381

Maini, Ph., Woolley, Th., Baker, R., Gaffney, E., & Seirin Lee, S. (2012). Turing’s model for biological pattern formation and the robustness problem. Interface Focus, 2, 487–496. https://doi.org/10.1098/rsfs.2011.0113

Mandelbrot, B. B. (1982). The fractal geometry of nature. W. H. Freeman and Company.

Marciniak-Czochra, A., Karch, G., & Suzuki, K. (2013). Instability of Turing patterns in reaction-diffusion–ODE systems. Journal of Mathemetical Biology, 74(3), 583–618. https://doi.org/10.1007/s00285-016-1035-z

Meinhardt, H. (2012). Turing’s theory of morphogenesis of 1952 and the subsequent discovery of the crucial role of local self-enhancement and long-range inhibition. Interface Focus, 2(4), 407–416.

Mercker, M., Köthe, A., & Marciniak-Czochra, A. (2013). Mechanochemical symmetry breaking in Hydra aggregates. Biophysical Journal, 105(5), 1075–1085. https://doi.org/10.1016/j.bpj.2013.07.038

Murray, J. D. (2003). Mathematical biology II: Spatial models and biomedical applications. Springer.

Newman, S. A. (2019). Inherency and homomorphy in the evolution of development. Current Opinion in Genetics & Development, 57, 1–8. https://doi.org/10.1016/j.gde.2019.06.005

Newman, S. A., & Bhat, R. (2008). Dynamical patterning modules: Physico-genetic determinants of morphological development and evolution. Physical Biology, 5(1), 015008. https://doi.org/10.1088/1478-3975/5/1/015008

Nicolis, G., & Prigogine, I. (1977). Self-organization in nonequilibrium systems: From dissipative structures to order through fluctuations. Wiley.

Noble, D. (2012). A theory of biological relativity: No privileged level of causation. Interface Focus, 2(1), 55–64. https://doi.org/10.1098/rsfs.2011.0067

Pietronero, L. (1987). The fractal structure of the universe: Correlations of galaxies and clusters and the average mass density. Physica A: Statistical Mechanics and Its Applications, 144(2–3), 257–284.

Scholes, N. S., & Isalan, M. (2014). A three-step framework for programming pattern formation in synthetic biology. Proceedings of the National Academy of Sciences, 111(1), 111–118. https://doi.org/10.1073/pnas.1322005111

Sporns, O. (2006). Small-world connectivity, motif composition, and complexity of fractal brain networks. BioSystems, 85(1), 55–64. https://doi.org/10.1016/j.biosystems.2006.02.008

Turcotte, D. L. (1997). Fractals and chaos in geology and geophysics. Cambridge University Press.

Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 237(641), 37–72. https://doi.org/10.1098/rstb.1952.0012

Werner, G. (2010). Fractals in the nervous system: Conceptual implications for theoretical neuroscience. Frontiers in Physiology, 1, 15. https://doi.org/10.3389/fphys.2010.00015

Werner, S. (2024). Holistic approaches to morphogenesis: Integration of physics, geometry, and biology. Biological Theory, 19(2), 105–122. https://doi.org/10.1007/s13752-024-00477-1

West, B. J. (2013). Fractal physiology and chaos in medicine. World Scientific.

Published

2025-12-10

How to Cite

The Hypothesis of the Unified Matrix of the Universe. (2025). European Scientific E-Journal, 39, 175–192. https://doi.org/10.47451/phi2025-10-02

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