The major synthetic evolutionary transitions

Synthetic eco-evolutionary dynamics in simple molecular environment

The understanding of eco-evolutionary dynamics, and in particular the mechanism of coexistence of species, is still fragmentary and in need of test bench model systems. To this aim we developed a variant of SELEX in vitro selection to study the evolution of a population of ∼1015 single-strand DNA oligonucleotide 'individuals'. We begin with a seed of random sequences which we select via affinity capture from ∼1012 DNA oligomers of fixed sequence ('resources') over which they compete. At each cycle ('generation'), the ecosystem is replenished via PCR amplification of survivors. Massive parallel sequencing indicates that across generations the variety of sequences ('species') drastically decreases, while some of them become populous and dominate the ecosystem. The simplicity of our approach, in which survival is granted by hybridization, enables a quantitative investigation of fitness through a statistical analysis of binding energies. We find that the strength of individual resource binding dominates the selection in the first generations, while inter- and intra-individual interactions become important in later stages, in parallel with the emergence of prototypical forms of mutualism and parasitism.

Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology

A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity-the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.

A rubric for human-like agents and NeuroAI

Researchers across cognitive, neuro- and computer sciences increasingly reference 'human-like' artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often inconsistent. Contributed research ranges widely from mimicking behaviour, to testing machine learning methods as neurally plausible hypotheses at the cellular or functional levels, or solving engineering problems. However, it cannot be assumed nor expected that progress on one of these three goals will automatically translate to progress in others. Here, a simple rubric is proposed to clarify the scope of individual contributions, grounded in their commitments to human-like behaviour, neural plausibility or benchmark/engineering/computer science goals. This is clarified using examples of weak and strong neuroAI and human-like agents, and discussing the generative, corroborate and corrective ways in which the three dimensions interact with one another. The author maintains that future progress in artificial intelligence will need strong interactions across the disciplines, with iterative feedback loops and meticulous validity tests-leading to both known and yet-unknown advances that may span decades to come. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Multi-scale Chimerism: An experimental window on the algorithms of anatomical control

Despite the immense progress in genetics and cell biology, major knowledge gaps remain with respect to prediction and control of the global morphologies that will result from the cooperation of cells with known genomes. The understanding of cooperativity, competition, and synergy across diverse biological scales has been obscured by a focus on standard model systems that exhibit invariant species-specific anatomies. Morphogenesis of chimeric biological material is an especially instructive window on the control of biological growth and form because it emphasizes the need for prediction without reliance on familiar, standard outcomes. Here, we review an important and fascinating body of data from experiments utilizing DNA transfer, cell transplantation, organ grafting, and parabiosis. We suggest that these are all instances (at different levels of organization) of one general phenomenon: chimerism. Multi-scale chimeras are a powerful conceptual and experimental tool with which to probe the mapping between properties of components and large-scale anatomy: the laws of morphogenesis. The existing data and future advances in this field will impact not only the understanding of cooperation and the evolution of body forms, but also the design of strategies for system-level outcomes in regenerative medicine and swarm robotics.

Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

The fields of developmental biology, biomedicine, and artificial life are being revolutionized by advances in synthetic morphology. The next phase of synthetic biology and bioengineering is resulting in the construction of novel organisms (biobots), which exhibit not only morphogenesis and physiology but functional behavior. It is now essential to begin to characterize the behavioral capacity of novel living constructs in terms of their ability to make decisions, form memories, learn from experience, and anticipate future stimuli. These synthetic organisms are highly diverse, and often do not resemble familiar model systems used in behavioral science. Thus, they represent an important context in which to begin to unify and standardize vocabulary and techniques across developmental biology, behavioral ecology, and neuroscience. To facilitate the study of behavior in novel living systems, we present a primer on techniques from the behaviorist tradition that can be used to probe the functions of any organism – natural, chimeric, or synthetic – regardless of the details of their construction or origin. These techniques provide a rich toolkit for advancing the fields of synthetic bioengineering, evolutionary developmental biology, basal cognition, exobiology, and robotics.

Evolutionary dynamics of bipartite begomoviruses revealed by complete genome analysis

Several key evolutionary events marked the evolution of geminiviruses, culminating with the emergence of divided (bipartite) genomes represented by viruses classified in the genus Begomovirus. This genus represents the most abundant group of multipartite viruses, contributing significantly to the observed abundance of multipartite species in the virosphere. Although aspects related to virus-host interactions and evolutionary dynamics have been extensively studied, the bipartite nature of these viruses has been little explored in evolutionary studies. Here, we performed a parallel evolutionary analysis of the DNA-A and DNA-B segments of New World begomoviruses. A total of 239 full-length DNA-B sequences obtained in this study, combined with 292 DNA-A and 76 DNA-B sequences retrieved from GenBank, were analysed. The results indicate that the DNA-A and DNA-B respond differentially to evolutionary processes, with the DNA-B being more permissive to variation and more prone to recombination than the DNA-A. Although a clear geographic segregation was observed for both segments, differences in the genetic structure between DNA-A and DNA-B were also observed, with cognate segments belonging to distinct genetic clusters. DNA-B coding regions evolve under the same selection pressures than DNA-A coding regions. Together, our results indicate an interplay between reassortment and recombination acting at different levels across distinct subpopulations and segments.

Molecular phylogenetics and evolutionary analysis of a highly recombinant begomovirus, Cotton leaf curl Multan virus, and associated satellites

Cotton leaf curl Multan virus (CLCuMuV) and its associated satellites are a major part of the cotton leaf curl disease (CLCuD) caused by the begomovirus species complex. Despite the implementation of potential disease management strategies, the incessant resurgence of resistance-breaking variants of CLCuMuV imposes a continuous threat to cotton production. Here, we present a focused effort to map the geographical prevalence, genomic diversity, and molecular evolutionary endpoints that enhance disease complexity by facilitating the successful adaptation of CLCuMuV populations to the diversified ecosystems. Our results demonstrate that CLCuMuV populations are predominantly distributed in China, while the majority of alphasatellites and betasatellites exist in Pakistan. We demonstrate that together with frequent recombination, an uneven genetic variation mainly drives CLCuMuV and its satellite's virulence and evolvability. However, the pattern and distribution of recombination breakpoints greatly vary among viral and satellite sequences. The CLCuMuV, Cotton leaf curl Multan alphasatellite, and Cotton leaf curl Multan betasatellite populations arising from distinct regions exhibit high mutation rates. Although evolutionarily linked, these populations are independently evolving under strong purifying selection. These findings will facilitate to comprehensively understand the standing genetic variability and evolutionary patterns existing among CLCuMuV populations across major cotton-producing regions of the world.

Topological constraints in early multicellularity favor reproductive division of labor

Reproductive division of labor (e.g. germ-soma specialization) is a hallmark of the evolution of multicellularity, signifying the emergence of a new type of individual and facilitating the evolution of increased organismal complexity. A large body of work from evolutionary biology, economics, and ecology has shown that specialization is beneficial when further division of labor produces an accelerating increase in absolute productivity (i.e. productivity is a convex function of specialization). Here we show that reproductive specialization is qualitatively different from classical models of resource sharing, and can evolve even when the benefits of specialization are saturating (i.e. productivity is a concave function of specialization). Through analytical theory and evolutionary individual-based simulations, we demonstrate that reproductive specialization is strongly favored in sparse networks of cellular interactions that reflect the morphology of early, simple multicellular organisms, highlighting the importance of restricted social interactions in the evolution of reproductive specialization.