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The Origin and Evolution of Information Handling (2404.04374v4)

Published 5 Apr 2024 in physics.bio-ph, cs.IT, cs.NE, math.IT, nlin.AO, and q-bio.PE

Abstract: A major challenge when describing the origin of life is to explain "how instructional information control systems emerge naturally and spontaneously from mere molecular dynamics". So far, no one has clarified how information control emerged ab initio and how primitive control mechanisms in life might have evolved, becoming increasingly refined. Based on recent experimental results showing that chemical computation does not require the presence of life-related chemistry, we elucidate the origin and early evolution of information handling by chemical automata, from information processing (computation) to information storage (memory) and information transmission (communication) and later digital messengers, covering at the same time its syntactic, semantic and pragmatic flavors. In contrast to other theories that assume the existence of initial complex structures, our representation starts from trivial self-replicators whose interaction leads to the arising of more powerful molecular machines. By describing precisely the primordial transitions in chemistry-based computation, our framework is capable of explaining the above-mentioned gaps and can be translated to other models of computation, which allow us to explore biological phenomena at multiple spatial and temporal scales. Being compatible with the free energy principle, we have developed a computational enactivist theoretical framework that could be able to describe from the origin of life to high-level cognition, as if it were a purely constructivist narrative. At the end of our manuscript, we propose some ways to extend our ideas, including experimental validation of our theory (both in vitro and in silico).

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Authors (3)
  1. Amahury Jafet López-Díaz (2 papers)
  2. Hiroki Sayama (71 papers)
  3. Carlos Gershenson (68 papers)

Summary

  • The paper demonstrates a sequential emergence from finite-state automata to Turing machine-like behaviors in pre-life chemical interactions.
  • The study uses reaction network models, including bimolecular reactions and pH dynamics, to reveal how simple chemical processes develop memory and complexity.
  • The research bridges theoretical models with experimental observations, outlining future in vitro and in silico directions for studying life's computational origins.

Exploring the Computational Underpinnings of Life's Origin

Introduction

The paper conducted by Lopez-Diaz, Sayama, and Gershenson explores one of the most foundational enigmas of biochemistry and systems biology: the emergence of life and the subsequent evolution of information handling capabilities within prebiotic chemistries. Through the lens of computational theory, the research meticulously constructs a paradigm that elucidates the sequential and hierarchical emergence of computational capabilities, ranging from simple finite state automata to complex Turing machine-like behaviors, within the context of pre-life chemical interactions.

Computational Foundations of Life

Finite-State Automata and the Onset of Information Processing

The paper commences by conceptualizing the planet as a nascent computational landscape, wherein elementary molecular interactions forge the cradle for life's computational ascendancy. The authors argue that the simplest non-living chemical interactions can be interpreted as finite-state automata (FSAs), primarily engaged in rudimentary information processing tasks. The illustrative use of bimolecular reactions underscores the transition from mere chemical interactions to the instantiation of computational rudiments.

Transition to Push-Down Automata: Emergence of Memory

Building on the foundation of FSAs, the paper proceeds to explore how complex reaction networks, akin to pH-reaction networks, evolve to exhibit behaviors characteristic of push-down automata (PDAs). This leap in computational complexity introduces the capability for information storage or memory, laying the groundwork for evolutionarily robust systems that can adapt to environmental vicissitudes through more sophisticated information handling.

The Evolution Toward Turing Machines

The advent of Turing machine-like capabilities within chemical systems marks a zenith in the paper's narrative. Employing the Belousov-Zhabotinsky reaction as a paradigm, the authors delineate how certain reaction networks could instantiate the behaviors of Linear Bounded Automata (LBAs). This conceptual leap signifies not merely an increase in computational complexity but also the emergence of information transmission faculties, heralding the onset of systems capable of richer, more complex forms of information handling.

Theoretical and Practical Implications

Bridging Theoretical Models and Experimental Validation

The paper does not merely rest within the theoretical domain; it ambitiously entwines these computational theories with experimental methodologies, inviting a compelling dialogue between theoretical assertions and empirical substantiation. The reference to experiments by Duenas-Díez and Perez-Mercader as a springboard for their theoretical propositions demonstrates a rigorous approach to grounding theoretical insights in empirical observations.

Speculation on Future Research Directions

Looking forward, the paper posits intriguing avenues for future research, notably the exploration of biological phenomena across various spatiotemporal scales through the computational lens elucidated herein. Furthermore, the call for in vitro and in silico validation underlines the authors' commitment to moving beyond speculative models towards a more tangible, experimentally grounded understanding of life's computational genesis.

Conclusion

This research paper traces a compelling narrative from the simple computational machinations of chemical interactions to the threshold of life’s complex information processing faculties. By framing the emergence of life within a computational paradigm, the authors not only provide a novel vantage point on the origin of life but also extend a methodological bridge towards understanding the evolution of computational capabilities in nature. As such, the implications of this work span both the theoretical realms, offering a fresh conceptual framework for life's origin, and the practical, proposing a roadmap for future experimental explorations of life's computational underpinnings.