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The coexistence between different types of templates has been the choice solution to the information crisis of prebiotic evolution, triggered by the finding that a single RNA-like template cannot carry enough information to code for any useful replicase. In principle, confining $d$ distinct templates of length $L$ in a package or protocell, whose survival depends on the coexistence of the templates it holds in, could resolve this crisis provided that $d$ is made sufficiently large. Here we review the prototypical package model of Niesert et al. 1981 which guarantees the greatest possible region of viability of the protocell population, and show that this model, and hence the entire package approach, does not resolve the information crisis. This is so because to secure survival the total information content of the protocell, $Ld$, must tend to a constant value that depends only on the spontaneous error rate per nucleotide of the template replication mechanism. As a result, an increase of $d$ must be followed by a decrease of $L$ to ensure the protocell viability, so that the net information gain is null.
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The coexistence between different informational molecules has been the preferred mode to circumvent the limitation posed by imperfect replication on the amount of information stored by each of these molecules. Here we reexamine a classic package model in which distinct information carriers or templates are forced to coexist within vesicles, which in turn can proliferate freely through binary division. The combined dynamics of vesicles and templates is described by a multitype branching process which allows us to write equations for the average number of the different types of vesicles as well as for their extinction probabilities. The threshold phenomenon associated to the extinction of the vesicle population is studied quantitatively using finite-size scaling techniques. We conclude that the resultant coexistence is too frail in the presence of parasites and so confinement of templates in vesicles without an explicit mechanism of cooperation does not resolve the information crisis of prebiotic evolution.
The post-genomic era has brought opportunities to bridge traditionally separate fields of early history of life and brought new insight into origin and evolution of biodiversity. According to distributions of codons in genome sequences, I found a relationship between the genetic code and the tree of life. This remote and profound relationship involves the origin and evolution of the genetic code and the diversification and expansion of genomes. Here, a prebiotic picture of the triplex nucleic acid evolution is proposed to explain the origin of the genetic code, where the transition from disorder to order in the origin of life might be due to the increasing stabilities of triplex base pairs. The codon degeneracy can be obtained in detail based on the coevolution of the genetic code with amino acids, or equivalently, the coevolution of tRNAs with aaRSs. This theory is based on experimental data such as the stability of triplex base pairs and the statistical features of genomic codon distributions. Several experimentally testable proposals have been developed. This study should be regarded as an exploratory attempt to reveal the early evolution of life based on sequence information in a statistical manner.
It is becoming widely accepted that very early in the origin of life, even before the emergence of genetic encoding, reaction networks of diverse small chemicals might have manifested key properties of life, namely self-propagation and adaptive evolution. To explore this possibility, we formalize the dynamics of chemical reaction networks within the framework of chemical ecosystem ecology. To capture the idea that life-like chemical systems are maintained out of equilibrium by fluxes of energy-rich food chemicals, we model chemical ecosystems in well-mixed containers that are subject to constant dilution by a solution with a fixed concentration of food chemicals. Modelling all chemical reactions as fully reversible, we show that seeding an autocatalytic cycle (AC) with tiny amounts of one or more of its member chemicals results in logistic growth of all member chemicals in the cycle. This finding justifies drawing an instructive analogy between an AC and the population of a biological species. We extend this finding to show that pairs of ACs can show competitive, predator-prey, or mutualistic associations just like biological species. Furthermore, when there is stochasticity in the environment, particularly in the seeding of ACs, chemical ecosystems can show complex dynamics that can resemble evolution. The evolutionary character is especially clear when the network architecture results in ecological precedence (survival of the first), which makes the path of succession historically contingent on the order in which cycles are seeded. For all its simplicity, the framework developed here is helpful for visualizing how autocatalysis in prebiotic chemical reaction networks can yield life-like properties. Furthermore, chemical ecosystem ecology could provide a useful foundation for exploring the emergence of adaptive dynamics and the origins of polymer-based genetic systems.
Cooperators forgo their interest to benefit others. Thus cooperation should not be favored by natural selection. It challenges the evolutionists, since cooperation is widespread. As one of the resolutions, information spreading has been revealed to play a key role in the emergence of cooperation. Individuals, however, are typically assumed to be passive in the information spreading. Here we assume that individuals are active to spread the information via self-recommendation. Individuals with higher intensities of self-recommendation are likely to have more neighbors. We find that i) eloquent cooperators are necessary to promote cooperation; ii) individuals need to be open to the self-recommendation to enhance cooperation level; iii) the cost-to-benefit ratio should be smaller than one minus the ratio between self-recommendation intensities of defector and cooperator, which qualitatively measures the viscosity of the population. Our results highlight the importance of active information spreading on cooperation.
Mutation and drift play opposite roles in genetics. While mutation creates diversity, drift can cause gene variants to disappear, especially when they are rare. In the absence of natural selection and migration, the balance between the drift and mutation in a well-mixed population defines its diversity. The Moran model captures the effects of these two evolutionary forces and has a counterpart in social dynamics, known as the Voter model with external opinion influencers. Two extreme outcomes of the Voter model dynamics are consensus and polarization, which correspond to low and high diversity in the Moran model. Here we use a Shannons information-theoretic approach to characterize the smooth transition between the ordered and disordered states of consensus and polarization in the Voter model. Mapping the Moran into the Voter model we extend the results to the mutation-drift balance and characterize the transition between low and high diversity in finite populations. Describing the population as a network of connected individuals we show that the transition between the two regimes depends on the geographic structure of the population and on the possible asymmetries in the mutation rates.
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