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Adapting Cognition Models to Biomolecular Condensate Dynamics

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Essays on the Extended Evolutionary Synthesis

Part of the book series: SpringerBriefs in Evolutionary Biology ((BRIEFSEVOLUTION))

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Abstract

We argue that biomolecular condensates within cells represent ‘fossil survivals’ of early stages in evolutionary process that were precursors to membrane-separated systems. That is, within some highly condensed ‘prebiotic soup’, such condensates—coacervate systems—could assemble and reassemble as-needed under prevailing selection pressures. Thus, just as mitochondria and other membrane-bound organelles represent ‘fossils’ within eukaryotic cells, biomolecular condensates represent a (much) earlier generation of prebiotic systems. Formal development, generalized from an information-theoretic treatment of cognition and its dynamics, translates the language of ‘chemical reaction rate’ into that of ‘cognition rate’ in these pre-organelle systems. Explicit models suggest searching for groupoid symmetry-breaking and other phase transitions in the assembly, reassembly, and dynamic function of currently-observed biomolecular condensates.

The prebiotic organization of chemicals into compartmentalized ensembles is an essential step to understand the transition from inert molecules to living matter. Compartmentalization is indeed a central property of living systems... ...[D]ifferent compartments could have co-emerged, competed for the same resources, or collaborated to ‘survive’ until one population would have acquired a selective advantage making it thrive at the expense of the other populations. — Martin and Douliez (2021)

Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system.

— (Maturana and Varela 1980 , p. 13)

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References

  • Atlan H., and I. Cohen. 1998. Immune information, self-organization, and meaning. International Immunology 10:711–717.

    Article  CAS  PubMed  Google Scholar 

  • Brown, R. 1992. Out of line. Royal Institute Proceedings 64:207–243.

    Google Scholar 

  • Brown, R., P. Higgins, and R. Sivera. 2011. Nonabelian Algebraic Topology: Filtered Spaces, Crossed Complexes, Cubical Homotopy Groupoids. EMS tracts in mathematics, vol. 15.

    Google Scholar 

  • Cayron, C. 2006. Groupoid of orientational variants. Acta Crystalographica Section A A62:21040.

    Google Scholar 

  • Champagnat, N., R. Ferriere, and S. Meleard. 2006. Unifying evolutionary dynamics: from individual stochastic process to macroscopic models. Theoretical Population Biology 69:297–321.

    Article  PubMed  Google Scholar 

  • Cover, T., and J. Thomas. 2006. Elements of Information Theory, 2nd ed. New York: Wiley.

    Google Scholar 

  • CTC. 2021. http://www.ctc.cam.ac.uk/outreach/origins/cosmic_structures_one.php

  • de Groot, S., and P. Mazur. 1984. Nonequilibrium Thermodynamics. New York: Dover.

    Google Scholar 

  • Dembo, A., and O. Zeitouni, 1998. Large Deviations and Applications, 2nd ed. New York: Springer.

    Book  Google Scholar 

  • Diamond, D., A. Campbell, C. Park, J. Halonen, and P. Zoladz. 2007. The temporal dynamics model of emotional memory processing. Neural Plasticity 2007:60803. https://doi.org/10.1155/2007/60803

    Article  PubMed  PubMed Central  Google Scholar 

  • Dolan, B., W. Janke, D. Johnston, and M. Stathakopoulos. 2001. Thin Fisher zeros. Journal of Physics A 34:6211–6223.

    Article  Google Scholar 

  • Feynman, R. 2000. Lectures in Computation. Boulder: Westview Press.

    Google Scholar 

  • Fisher, M. 1965. Lectures in Theoretical Physics, Vol. 7. Boulder: University of Colorado Press.

    Google Scholar 

  • Gavrilets, S. 2010. High-dimensional fitness landscapes and speciation. In Evolution: The Extended Synthesis, ed. Massimo Pigliucci and Gerd B. Müller. Cambridge: MIT Press.

    Google Scholar 

  • Golubitsky, M., and I. Stewart. 2006. Nonlinear dynamics and networks: the groupoid formalism. Bulletin of the American Mathematical Society 43:305–364.

    Article  Google Scholar 

  • Hatcher, A. 2001. Algebraic Topology. New York: Cambridge University Press.

    Google Scholar 

  • Horsthemeke, W., and R. Lefever. 2006. Noise-Induced Transitions. Theory and Applications in Physics, Chemistry, and Biology, Vol. 15. New York: Springer.

    Google Scholar 

  • Hoyrup, M. 2013. Computability of the ergodic decomposition. Annals of Pure and Applied Logic 164:542–549.

    Article  Google Scholar 

  • Jolliffe, I. 2002. Principal Component Analysis. New York: Springer.

    Google Scholar 

  • Khinchin, A. 1957. Mathematical Foundations of Information Theory. New York: Dover.

    Google Scholar 

  • Laidler, K. 1987. Chemical Kinetics, 3rd ed. New York: Harper and Row.

    Google Scholar 

  • Landau, L., and E. Lifshitz. 2007. Statistical Physics, 3rd ed., Part 1. New York: Elsevier.

    Google Scholar 

  • Lin Y., J. Forman-Kay, and H. Chan. 2018. Theories for sequence-dependent phase behaviors of biomolecular condensates. Biochemistry 57:2499–2508. https://doi.org/10.1021/acs.biochem.8b00058

    Article  CAS  PubMed  Google Scholar 

  • Lyon, A., W. Peeples, and M. Rosen. 2021. A framework for understanding functions of biomolecular condensates on molecular cellular scales. Nature Reviews Molecular Cell Biology 22:215–235.

    Article  CAS  PubMed  Google Scholar 

  • Marshall, J. 2014. The genetic code. PNAS 111:5760.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Martin, E., et al. 2020. Valence and patterning of aromatic residues determine the phase behavior of prion-prionlike domains. Science 367:694–699. https://doi.org/10.1126/science.aaw8653

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Martin, N., and J. Douliez. 2021. Fatty acid vesicles and coacervates as model prebiotic protocells. ChemSystemsChem 3:e2100024.

    Article  CAS  Google Scholar 

  • Maturana, H., and F. Varela. 1980. Autopoiesis and Cognition: The Realization of the Living. Boston: Reidel.

    Book  Google Scholar 

  • Mezo I., and G. Keady. 2015. Some physical applications of generalized Lambert functions. arXiv:1505.01555v2 [math.CA] 22 Jun 2015.

    Google Scholar 

  • Nair, G., F. Fagnani, S. Zampieri, and R. Evans. 2007. Feedback control under data rate constraints: an overview. Proceedings of the IEEE 95:108137.

    Article  Google Scholar 

  • Peeples, W., and M. Rosen. 2021. Mechanistic dissection of increased enzymatic rate in a phase separated component. Nature Chemical Biology 17:693–702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pettini, M. 2007. Geometry and Topology in Hamiltonian Dynamics and Statistical Mechanics. New York: Springer.

    Book  Google Scholar 

  • Protter, P. 2005. Stochastic Integration and Differential Equations: A New Approach, 2nd ed. New York: Springer.

    Book  Google Scholar 

  • Ruelle, D. 1964. Cluster property of the correlation functions of classical gases. Reviews of Modern Physics 38:580–584.

    Article  Google Scholar 

  • Sang, D., T. Shu, C. Pantoja, A. Ibanez de Opakua, M. Zweckstetter, and L. Holt. 2022. Condensed-phase signaling can expand kinase specificity and respond to macromolecular crowding. Molecular Cell 82:P3693-3711.E10. https://doi.org/10.1016/j.molcel.2022.08.016

  • Stewart, I. 2017. Spontaneous symmetry-breaking in a network model for quadruped locomotion. International Journal of Bifurcation and Chaos 14:1730049.

    Article  Google Scholar 

  • Tian, C., L. Lin, and L. Zhang. 2017. Additive noise driven phase transitions in a predator-prey system. Applied Mathematical Modelling 46:423–432.

    Article  Google Scholar 

  • Van den Broeck, C., J. Parrondo, and R. Toral. 1994. Noise-induced nonequilibrium phase transition. Physical Review Letters 73:3395–3398.

    Article  Google Scholar 

  • Van den Broeck, C., J. Parrondo, R. Toral, and R. Kawai. 1997. Nonequilibrium phase transitions induced by multiplicative noise. Physical Review E 55:4084–4094.

    Article  Google Scholar 

  • Vernon, R., et al. 2018. Pi-Pi contacts are an overlooked protein feature relevant to phase separation. eLife 7:e31486. https://doi.org/10.7554/eLife.31486

    Article  PubMed  PubMed Central  Google Scholar 

  • Wallace, R. 2005. Consciousness: A Mathematical Treatment of the Global Neuronal Workspace Model. New York: Springer.

    Google Scholar 

  • Wallace, R. 2011a. On the evolution of homochirality. Comptes Rendus Biologies 334:263–268.

    Article  CAS  PubMed  Google Scholar 

  • Wallace, R. 2011b. Structure and dynamics of the ‘protein folding code’ inferred using Tlusty’s topological rate distortion approach. BioSystems 103:18–26.

    Article  CAS  PubMed  Google Scholar 

  • Wallace, R. 2012. Consciousness, crosstalk, and the mereological fallacy: an evolutionary perspective. Physics of Life Reviews 9:426–453.

    Article  PubMed  Google Scholar 

  • Wallace, R. 2014. A new formal perspective on ‘Cambrian Explosions’. Comptes Rendus Biologies 337:1–5.

    Article  PubMed  Google Scholar 

  • Wallace, R. 2015. An Ecosystem Approach to Economic Stabilization: Escaping the Neoliberal Wilderness. New York: Routledge.

    Book  Google Scholar 

  • Wallace, R. 2017. Computational Psychiatry: A Systems Biology Approach to the Epigenetics of Mental Disorders. New York: Springer.

    Book  Google Scholar 

  • Wallace, R. 2018. New statistical models of nonergodic cognitive systems and their pathologies. Journal of Theoretical Biology 436:72–78.

    Article  CAS  PubMed  Google Scholar 

  • Wallace, R. 2020a. On the variety of cognitive temperatures and their symmetry-breaking dynamics. Acta Biotheoretica 68:421–439. https://doi.org/10.1007/s10441-019-09375-7

    Article  PubMed  Google Scholar 

  • Wallace, R. 2020b. Cognitive Dynamics on Clausewitz Landscapes: The Control and Directed Evolution of Organized Conflict. New York: Springer.

    Book  Google Scholar 

  • Wallace, R. 2020c. Signal transduction in cognitive systems: origin and dynamics of the inverted-U/U dose-response relation. Journal of Theoretical Biology 504:110377.

    Article  PubMed  Google Scholar 

  • Wallace, R. 2021a. How AI founders on adversarial landscapes of fog and friction. Journal of Defense Modeling and Simulation 19. https://doi.org/10.1177/1548512920962227

  • Wallace, R. 2021b. Toward a formal theory of embodied cognition. BioSystems 202:104356.

    Article  PubMed  Google Scholar 

  • Wallace, R. 2021c. Embodied cognition and its pathologies: the dynamics of institutional failure on wickedly hard problems. Communications in Nonlinear Science and Numerical Simulation 95:105616.

    Article  Google Scholar 

  • Wallace, R. 2022. Major transitions as groupoid symmetry-breaking in nonergodic prebiotic, biological and social information systems. Acta Biotheoretica 70:27. https://doi.org/10.1007/s10441-022-09451-5

    Article  PubMed  Google Scholar 

  • Wang, J., et al. 2018. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174:688–699.e616. https://doi.org/10.1016/j.cell.2018.06.006

  • Weinstein, A. 1996. Groupoids: unifying internal and external symmetry. Notices of the American Mathematical Association 43:744–752.

    Google Scholar 

  • Yi, S., P.W. Nelson, and A.G. Ulsoy. 2010. Time-Delay Systems: Analysis and Control Using the Lambert W Function. New Jersey: World Scientific.

    Book  Google Scholar 

Download references

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Wallace, R. (2023). Adapting Cognition Models to Biomolecular Condensate Dynamics. In: Essays on the Extended Evolutionary Synthesis. SpringerBriefs in Evolutionary Biology. Springer, Cham. https://doi.org/10.1007/978-3-031-29879-0_7

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