Two different template replicators coexisting in the same protocell: stochastic simulation of an extended chemoton model

The limits of metabolic heredity in protocells

The universal core of metabolism could have emerged from thermodynamically favoured prebiotic pathways at the origin of life. Starting with H 2 and CO 2 , the synthesis of amino acids and mixed fatty acids, which self-assemble into protocells, is favoured under warm anoxic conditions. Here, we address whether it is possible for protocells to evolve greater metabolic complexity, through positive feedbacks involving nucleotide catalysis. Using mathematical simulations to model metabolic heredity in protocells, based on branch points in protometabolic flux, we show that nucleotide catalysis can indeed promote protocell growth. This outcome only occurs when nucleotides directly catalyse CO 2 fixation. Strong nucleotide catalysis of other pathways (e.g. fatty acids and amino acids) generally unbalances metabolism and slows down protocell growth, and when there is competition between catalytic functions cell growth collapses. Autocatalysis of nucleotide synthesis can promote growth but only if nucleotides also catalyse CO 2 fixation; autocatalysis alone leads to the accumulation of nucleotides at the expense of CO 2 fixation and protocell growth rate. Our findings offer a new framework for the emergence of greater metabolic complexity, in which nucleotides catalyse broad-spectrum processes such as CO 2 fixation, hydrogenation and phosphorylation important to the emergence of genetic heredity at the origin of life.

Toward a theory of evolution as multilevel learning

Modern evolutionary theory gives a detailed quantitative description of microevolutionary processes that occur within evolving populations of organisms, but evolutionary transitions and emergence of multiple levels of complexity remain poorly understood. Here, we establish the correspondence among the key features of evolution, learning dynamics, and renormalizability of physical theories to outline a theory of evolution that strives to incorporate all evolutionary processes within a unified mathematical framework of the theory of learning. According to this theory, for example, replication of genetic material and natural selection readily emerge from the learning dynamics, and in sufficiently complex systems, the same learning phenomena occur on multiple levels or on different scales, similar to the case of renormalizable physical theories.

We apply the theory of learning to physically renormalizable systems in an attempt to outline a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. It is shown that these cornerstone phenomena of biology emerge from the fundamental features of learning dynamics such as the existence of a loss function, which is minimized during learning. We then sketch the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back propagation) and predicting (forward propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution (in particular, the origin of life), have to be analyzed in the thermodynamic limit, which is described in detail in the paper by Vanchurin et al. [V. Vanchurin, Y. I. Wolf, E. V. Koonin, M. I. Katsnelson, Proc. Natl. Acad. Sci. U.S.A. 119, 10.1073/pnas.2120042119 (2022)].

Ecology and Evolution in the RNA World Dynamics and Stability of Prebiotic Replicator Systems

As of today, the most credible scientific paradigm pertaining to the origin of life on Earth is undoubtedly the RNA World scenario. It is built on the assumption that catalytically active replicators (most probably RNA-like macromolecules) may have been responsible for booting up life almost four billion years ago. The many different incarnations of nucleotide sequence (string) replicator models proposed recently are all attempts to explain on this basis how the genetic information transfer and the functional diversity of prebiotic replicator systems may have emerged, persisted and evolved into the first living cell. We have postulated three necessary conditions for an RNA World model system to be a dynamically feasible representation of prebiotic chemical evolution: (1) it must maintain and transfer a sufficient diversity of information reliably and indefinitely, (2) it must be ecologically stable and (3) it must be evolutionarily stable. In this review, we discuss the best-known prebiotic scenarios and the corresponding models of string-replicator dynamics and assess them against these criteria. We suggest that the most popular of prebiotic replicator systems, the hypercycle, is probably the worst performer in almost all of these respects, whereas a few other model concepts (parabolic replicator, open chaotic flows, stochastic corrector, metabolically coupled replicator system) are promising candidates for development into coherent models that may become experimentally accessible in the future.

Prebiotic selection for motifs in a model of template-free elongation of polymers within compartments

The transition from prelife where self-replication does not occur, to life which exhibits self-replication and evolution, has been a subject of interest for many decades. Membranes, forming compartments, seem to be a critical component of this transition as they provide several concurrent benefits. They maintain localized interactions, generate electro-chemical gradients, and help in selecting cooperative functions as they arise. These functions pave the way for the emergence and maintenance of simple metabolic cycles and polymers. In the context of origin of life, evolution of information-carrying molecules and RNA based enzymes within compartments has been subject to intensive theoretical and experimental research. Hence, many experimental efforts aim to produce compartments that contain elongating polynucleotides (also referred to as protocells), which store information and perform catalysis. Despite impressive experimental progress, we are still relatively ignorant about the dynamics by which elongating polynucleotides can produce more sophisticated behaviors. Here we perform computer simulations to couple information production through template-free elongation of polymers with dividing compartments. We find that polymers with a simple ability—biasing the concentration of monomers within their own compartment—can acquire a selective advantage in prelife. We further investigate whether such a mechanism allows for cooperative dynamics to dominate over purely competitive ones. We show that under this system of biased monomer addition, even without template-directed self-replication, genetic motifs can emerge, compete, cooperate, and ultimately survive within the population.

Synthetic transitions: towards a new synthesis

The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of non-genetic information, language or even consciousness. Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. In both cases, transitions can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of major transitions, both natural and artificial. Here, we review some advances made at the crossroads between statistical physics, artificial life, synthetic biology and evolutionary robotics.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

Evolution of the first genetic cells and the universal genetic code: a hypothesis based on macromolecular coevolution of RNA and proteins

A qualitative hypothesis based on coevolution of protein and nucleic acid macromolecules was developed to explain the evolution of the first genetic cells, from the likely organic chemical-rich environment of early earth, through to the Last Universal Common Ancestor (LUCA). The evolution of the first genetic cell was divided into three phases, proto-genetic cells I, II and III, and the transition to each milestone is described, based on development of chemical cross-catalysis, bio-cross-catalysis, and the universal genetic code, respectively. Selection of macromolecular properties of both peptides and nucleic acids, in response to environmental factors, was likely to be a key aspect of early evolution. The development of hereditable nucleic acids with various key functions; translation, transcription and replication, is described. These functions are envisaged to have coevolved with protein enzymes, from simple organic precursors. Genetically heritable nucleotides may have developed after the local earth environment had cooled below 63 °C. Around this temperature G-C bases would have been preferentially utilized for nucleotide synthesis. Under these conditions RNA type nucleotides were then likely selected from a range of different types of nucleotide backbones through template-based synthesis. Initial development of the genetic coding system was simplified by the availability of proto-messenger RNA sequences that contained only G and C bases, and the need to encode only four amino acids. The step-wise addition of further amino acids to the code was predicted to parallel the growing metabolic complexity of the proto-genetic cell. On completion of this evolutionary process the proto-genetic cell is envisaged to have become the LUCA, the last common ancestor of bacteria, eukaryote and archaea domains. Key issues addressed by the model include: (a) the transition from non-hereditable random sequences of peptides and nucleic acids to specific proteins coded by hereditable nucleotide sequences, (b) the origin of homochiral amino acids and sugars, and (c) the mutation limits on the sizes of early nucleic acid genomes. The first genome was limited to a size of about 200 base pairs.