Search for: All records

Prior research on evolutionary mechanisms during the origin of life has mainly assumed the existence of populations of discrete entities with information encoded in genetic polymers. Recent theoretical advances in autocatalytic chemical ecology establish a broader evolutionary framework that allows for adaptive complexification prior to the emergence of bounded individuals or genetic encoding. This framework establishes the formal equivalence of cells, ecosystems and certain localized chemical reaction systems as autocatalytic chemical ecosystems (ACEs): food-driven (open) systems that can grow due to the action of autocatalytic cycles (ACs). When ACEs are organized in meta-ecosystems, whether they be populations of cells or sets of chemically similar environmental patches, evolution, defined as change in AC frequency over time, can occur. In cases where ACs are enriched because they enhance ACE persistence or dispersal ability, evolution is adaptive and can build complexity. In particular, adaptive evolution can explain the emergence of self-bounded units (e.g. protocells) and genetic inheritance mechanisms. Recognizing the continuity between ecological and evolutionary change through the lens of autocatalytic chemical ecology suggests that the origin of life should be seen as a general and predictable outcome of driven chemical ecosystems rather than a phenomenon requiring specific, rare conditions.

 

Identifying conditions that promote egalitarian major transitions, where unlike replicating units unite to form a higher-level unit, is an open problem with far-reaching implications. We propose that egalitarian major transitions can only begin in ecological communities that are conducive to them. To formalize this idea, we introduce the concept of “transition-ability”, which describes the extent to which a community is poised to undergo an egalitarian major transition. We hypothesize that transitionability is a property of ecological interaction networks, which represent the set of pairwise interactions among members of a community. Using a digital artificial ecology that simulates interactions between species based on a static interaction network, we test the transition-ability of interaction networks created by a range of graph-generation techniques, as well as some real-world ecological networks. To measure the extent to which a community is moving towards a major transition, we quantify the increase in community-level fitness relative to individual-level fitness across five different fitness proxies. We find that some network generation protocols produce more transitionable networks than others. In particular, interaction strengths (i.e. edge weights) have a substantial impact on transitionability, despite receiving low attention in the literature. 

The problem of identifying conditions that enable major evolutionary transitions, in which distinct units come together to form a new higher level unit, is a complex and difficult topic spanning many disciplines. Here, we approach this problem from the perspective of the origin of life, which allows us to make the simplifying assumption that the lower-level units are not also evolving. This assumption lets us focus on identifying environmental factors that promote egalitarian major transitions in general and the origin of life specifically. To study this question, we build a simple artificial ecology model. We quantify major-transition-like dynamics using a maximum likelihood approach and a set of null models predicting the behavior of our system under various dynamics. Ultimately, we find that, even in a maximally simple artificial ecology model, we are able to observe evidence of community-level selection and thus the beginnings of a major evolutionary transition. The regions of parameter space that promote community-level selection vary based on species interactions but we observe consistent trends. 

Phylogenies provide direct accounts of the evolutionary trajectories behind evolved artifacts in genetic algorithm and artificial life systems. Phylogenetic analyses can also enable insight into evolutionary and ecological dynamics such as selection pressure and frequency-dependent selection. Traditionally, digital evolution systems have recorded data for phylogenetic analyses through perfect tracking where each birth event is recorded in a centralized data structure. This approach, however, does not easily scale to distributed computing environments where evolutionary individuals may migrate between a large number of disjoint processing elements. To provide for phylogenetic analyses in these environments, we propose an approach to enable phylogenies to be inferred via heritable genetic annotations rather than directly tracked. We introduce a “hereditary stratigraphy” algorithm that enables efficient, accurate phylogenetic reconstruction with tunable, explicit trade-offs between annotation memory footprint and reconstruction accuracy. In particular, we demonstrate an approach that enables estimation of the most recent common ancestor (MRCA) between two individuals with fixed relative accuracy irrespective of lineage depth while only requiring logarithmic annotation space complexity with respect to lineage depth. This approach can estimate, for example, MRCA generation of two genomes within 10% relative error with 95% confidence up to a depth of a trillion generations with genome annotations smaller than a kilobyte. We also simulate inference over known lineages, recovering up to 85.70% of the information contained in the original tree using 64-bit annotations. 

Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs. Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective. Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations. In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications. While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration. 

Short Abstract for evolutionary computing community on selection schemes working for directed evolution experiments in microbes. 

In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research.