Agent-based modeling of the COVID-19 pandemic in Florida.

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like "Should we lockdown?" During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.

Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.

We evaluate approaches to vaccine distribution using an agent-based model of human activity and COVID-19 transmission calibrated to detailed trends in cases, hospitalizations, deaths, seroprevalence, and vaccine breakthrough infections in Florida, USA. We compare the incremental effectiveness for four different distribution strategies at four different levels of vaccine supply, starting in late 2020 through early 2022. Our analysis indicates that the best strategy to reduce severe outcomes would be to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the intermediate vaccine availability assumptions and relatively modest compared to a simple mass vaccination approach under high vaccine availability. Ring vaccination, while generally the most effective strategy for reducing infections, ultimately proved least effective at preventing deaths. We also consider using age group as a practical surrogate measure for actual disease-risk targeting; this approach also outperforms both simple mass distribution and ring vaccination. We find that quantitative effectiveness of a strategy depends on whether effectiveness is assessed after the alpha, delta, or omicron wave. However, these differences in absolute benefit for the strategies do not change the ranking of their performance at preventing severe outcomes across vaccine availability assumptions.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates.

AbstractThe joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the "nominal" distribution of possible changes, prior to mutation or selection; (2) the "de novo" distribution of realized mutations; and (3) the "fixed" distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong-selection/weak-mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

Mutation bias and the predictability of evolution.

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.

We evaluate approaches to vaccine distribution using an agent-based model of human activity and COVID-19 transmission calibrated to detailed trends in cases, hospitalizations, deaths, seroprevalence, and vaccine breakthrough infections in Florida, USA. We compare the incremental effectiveness for four different distribution strategies at four different levels of vaccine availability, reflecting different income settings' historical COVID-19 vaccine distribution. Our analysis indicates that the best strategy to reduce severe outcomes is to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the middle-income-country availability assumptions, and relatively modest compared to a simple mass vaccination approach for rapid, high levels of vaccine availability. Ring vaccination, while generally the most effective strategy for reducing infections, ultimately proved least effective at preventing deaths. We also consider using age group as a practical, surrogate measure for actual disease-risk targeting; this approach still outperforms both simple mass distribution and ring vaccination. We also find that the magnitude of strategy effectiveness depends on when assessment occurs (e.g., after delta vs. after omicron variants). However, these differences in absolute benefit for the strategies do not change the ranking of their performance at preventing severe outcomes across vaccine availability assumptions.

Mutation bias shapes the spectrum of adaptive substitutions.

Evolutionary adaptation often occurs by the fixation of beneficial mutations. This mode of adaptation can be characterized quantitatively by a spectrum of adaptive substitutions, i.e., a distribution for types of changes fixed in adaptation. Recent work establishes that the changes involved in adaptation reflect common types of mutations, raising the question of how strongly the mutation spectrum shapes the spectrum of adaptive substitutions. We address this question with a codon-based model for the spectrum of adaptive amino acid substitutions, applied to three large datasets covering thousands of amino acid changes identified in natural and experimental adaptation in Saccharomyces cerevisiae, Escherichia coli, and Mycobacterium tuberculosis Using species-specific mutation spectra based on prior knowledge, we find that the mutation spectrum has a proportional influence on the spectrum of adaptive substitutions in all three species. Indeed, we find that by inferring the mutation rates that best explain the spectrum of adaptive substitutions, we can accurately recover the species-specific mutation spectra. However, we also find that the predictive power of the model differs substantially between the three species. To better understand these differences, we use population simulations to explore the factors that influence how closely the spectrum of adaptive substitutions mirrors the mutation spectrum. The results show that the influence of the mutation spectrum decreases with increasing mutational supply ([Formula: see text]) and that predictive power is strongly affected by the number and diversity of beneficial mutations.

Avoidance of Self during CRISPR Immunization.

The battle between microbes and their viruses is ancient and ongoing. Clustered regularly interspaced short palindromic repeat (CRISPR) immunity, the first and, to date, only form of adaptive immunity found in prokaryotes, represents a flexible mechanism to recall past infections while also adapting to a changing pathogenic environment. Critical to the role of CRISPR as an adaptive immune mechanism is its capacity for self versus non-self recognition when acquiring novel immune memories. Yet, CRISPR systems vary widely in both how and to what degree they can distinguish foreign from self-derived genetic material. We document known and hypothesized mechanisms that bias the acquisition of immune memory towards non-self targets. We demonstrate that diversity is the rule, with many widespread but no universal mechanisms for self versus non-self recognition.

Phylotastic: Improving Access to Tree-of-Life Knowledge With Flexible, on-the-Fly Delivery of Trees.

A comprehensive phylogeny of species, i.e., a tree of life, has potential uses in a variety of contexts, including research, education, and public policy. Yet, accessing the tree of life typically requires special knowledge, complex software, or long periods of training. The Phylotastic project aims make it as easy to get a phylogeny of species as it is to get driving directions from mapping software. In prior work, we presented a design for an open system to validate and manage taxon names, find phylogeny resources, extract subtrees matching a user's taxon list, scale trees to time, and integrate related resources such as species images. Here, we report the implementation of a set of tools that together represent a robust, accessible system for on-the-fly delivery of phylogenetic knowledge. This set of tools includes a web portal to execute several customizable workflows to obtain species phylogenies (scaled by geologic time and decorated with thumbnail images); more than 30 underlying web services (accessible via a common registry); and code toolkits in R and Python (allowing others to develop custom applications using Phylotastic services). The Phylotastic system, accessible via http://www.phylotastic.org, provides a unique resource to access the current state of phylogenetic knowledge, useful for a variety of cases in which a tree extracted quickly from online resources (as distinct from a tree custom-made from character data) is sufficient, as it is for many casual uses of trees identified here.

The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function.

An underexplored question in evolutionary genetics concerns the extent to which mutational bias in the production of genetic variation influences outcomes and pathways of adaptive molecular evolution. In the genomes of at least some vertebrate taxa, an important form of mutation bias involves changes at CpG dinucleotides: if the DNA nucleotide cytosine (C) is immediately 5' to guanine (G) on the same coding strand, then-depending on methylation status-point mutations at both sites occur at an elevated rate relative to mutations at non-CpG sites. Here, we examine experimental data from case studies in which it has been possible to identify the causative substitutions that are responsible for adaptive changes in the functional properties of vertebrate haemoglobin (Hb). Specifically, we examine the molecular basis of convergent increases in Hb-O2 affinity in high-altitude birds. Using a dataset of experimentally verified, affinity-enhancing mutations in the Hbs of highland avian taxa, we tested whether causative changes are enriched for mutations at CpG dinucleotides relative to the frequency of CpG mutations among all possible missense mutations. The tests revealed that a disproportionate number of causative amino acid replacements were attributable to CpG mutations, suggesting that mutation bias can influence outcomes of molecular adaptation. This article is part of the theme issue 'Convergent evolution in the genomics era: new insights and directions'.

Why we don't want another "Synthesis".

High-level debates in evolutionary biology often treat the Modern Synthesis as a framework of population genetics, or as an intellectual lineage with a changing distribution of beliefs. Unfortunately, these flexible notions, used to negotiate decades of innovations, are now thoroughly detached from their historical roots in the original Modern Synthesis (OMS), a falsifiable scientific theory. The OMS held that evolution can be adequately understood as a process of smooth adaptive change by shifting the frequencies of small-effect alleles at many loci simultaneously, without the direct involvement of new mutations. This shifting gene frequencies theory was designed to support a Darwinian view in which the course of evolution is governed by selection, and to exclude a mutation-driven view in which the timing and character of evolutionary change may reflect the timing and character of events of mutation. The OMS is not the foundation of current thinking, but a special case of a broader conception that includes (among other things) a mutation-driven view introduced by biochemists in the 1960s, and now widely invoked. This innovation is evident in mathematical models relating the rate of evolution directly to the rate of mutation, which emerged in 1969, and now represent a major branch of theory with many applications. In evo-devo, mutationist thinking is reflected by a concern for the "arrival of the fittest". Though evolutionary biology is not governed by any master theory, and incorporates views excluded from the OMS, the recognition of these changes has been hindered by woolly conceptions of theories, and by historical accounts, common in the evolutionary literature, that misrepresent the disputes that defined the OMS. REVIEWERS: This article was reviewed by W. Ford Doolittle, Eugene Koonin and J. Peter Gogarten.

Mutational Biases Influence Parallel Adaptation.

While mutational biases strongly influence neutral molecular evolution, the role of mutational biases in shaping the course of adaptation is less clear. Here we consider the frequency of transitions relative to transversions among adaptive substitutions. Because mutation rates for transitions are higher than those for transversions, if mutational biases influence the dynamics of adaptation, then transitions should be overrepresented among documented adaptive substitutions. To test this hypothesis, we assembled two sets of data on putatively adaptive amino acid replacements that have occurred in parallel during evolution, either in nature or in the laboratory. We find that the frequency of transitions in these data sets is much higher than would be predicted under a null model where mutation has no effect. Our results are qualitatively similar even if we restrict ourself to changes that have occurred, not merely twice, but three or more times. These results suggest that the course of adaptation is biased by mutation.

On the Causes of Evolutionary Transition:Transversion Bias.

A pattern in which nucleotide transitions are favored several fold over transversions is common in molecular evolution. When this pattern occurs among amino acid replacements, explanations often invoke an effect of selection, on the grounds that transitions are more conservative in their effects on proteins. However, the underlying hypothesis of conservative transitions has never been tested directly. Here we assess support for this hypothesis using direct evidence: the fitness effects of mutations in actual proteins measured via individual or paired growth experiments. We assembled data from 8 published studies, ranging in size from 24 to 757 single-nucleotide mutations that change an amino acid. Every study has the statistical power to reveal significant effects of amino acid exchangeability, and most studies have the power to discern a binary conservative-vs-radical distinction. However, only one study suggests that transitions are significantly more conservative than transversions. In the combined set of 1,239 replacements (544 transitions, 695 transversions), the chance that a transition is more conservative than a transversion is 53 % (95 % confidence interval 50 to 56) compared with the null expectation of 50 %. We show that this effect is not large compared with that of most biochemical factors, and is not large enough to explain the several-fold bias observed in evolution. In short, the available data have the power to verify the "conservative transitions" hypothesis if true, but suggest instead that selection on proteins plays at best a minor role in the observed bias.

Modeling evolution using the probability of fixation: history and implications.

Many models of evolution calculate the rate of evolution by multiplying the rate at which new mutations originate within a population by a probability of fixation. Here we review the historical origins, contemporary applications, and evolutionary implications of these "origin-fixation" models, which are widely used in evolutionary genetics, molecular evolution, and phylogenetics. Origin-fixation models were first introduced in 1969, in association with an emerging view of "molecular" evolution. Early origin-fixation models were used to calculate an instantaneous rate of evolution across a large number of independently evolving loci; in the 1980s and 1990s, a second wave of origin-fixation models emerged to address a sequence of fixation events at a single locus. Although origin fixation models have been applied to a broad array of problems in contemporary evolutionary research, their rise in popularity has not been accompanied by an increased appreciation of their restrictive assumptions or their distinctive implications. We argue that origin-fixation models constitute a coherent theory of mutation-limited evolution that contrasts sharply with theories of evolution that rely on the presence of standing genetic variation. A major unsolved question in evolutionary biology is the degree to which these models provide an accurate approximation of evolution in natural populations.

Mendelian-mutationism: the forgotten evolutionary synthesis.

According to a classical narrative, early geneticists, failing to see how Mendelism provides the missing pieces of Darwin's theory, rejected gradual changes and advocated an implausible yet briefly popular view of evolution-by-mutation; after decades of delay (in which synthesis was prevented by personal conflicts, disciplinary rivalries, and anti-Darwinian animus), Darwinism emerged on a new Mendelian basis. Based on the works of four influential early geneticists - Bateson, de Vries, Morgan and Punnett -, and drawing on recent scholarship, we offer an alternative that turns the classical view on its head. For early geneticists, embracing discrete inheritance and the mutation theory (for the origin of hereditary variation) did not entail rejection of selection, but rejection of Darwin's non-Mendelian views of heredity and variation, his doctrine of naturanon facitsaltum, and his conception of "natural selection" as a creative force that shapes features out of masses of infinitesimal differences. We find no evidence of a delay in synthesizing mutation, rules of discrete inheritance, and selection in a Mendelian-Mutationist Synthesis. Instead, before 1918, early geneticists had conceptualized allelic selection, the Hardy-Weinberg equilibrium, the evolution of a quantitative trait under selection, the probability of fixation of a new mutation, and other key innovations. Contemporary evolutionary thinking seems closer to their more ecumenical view than to the restrictive mid-twentieth-century consensus known as the Modern Synthesis.

Phylotastic! Making tree-of-life knowledge accessible, reusable and convenient.

BACKGROUND: Scientists rarely reuse expert knowledge of phylogeny, in spite of years of effort to assemble a great "Tree of Life" (ToL). A notable exception involves the use of Phylomatic, which provides tools to generate custom phylogenies from a large, pre-computed, expert phylogeny of plant taxa. This suggests great potential for a more generalized system that, starting with a query consisting of a list of any known species, would rectify non-standard names, identify expert phylogenies containing the implicated taxa, prune away unneeded parts, and supply branch lengths and annotations, resulting in a custom phylogeny suited to the user's needs. Such a system could become a sustainable community resource if implemented as a distributed system of loosely coupled parts that interact through clearly defined interfaces. RESULTS: With the aim of building such a "phylotastic" system, the NESCent Hackathons, Interoperability, Phylogenies (HIP) working group recruited 2 dozen scientist-programmers to a weeklong programming hackathon in June 2012. During the hackathon (and a three-month follow-up period), 5 teams produced designs, implementations, documentation, presentations, and tests including: (1) a generalized scheme for integrating components; (2) proof-of-concept pruners and controllers; (3) a meta-API for taxonomic name resolution services; (4) a system for storing, finding, and retrieving phylogenies using semantic web technologies for data exchange, storage, and querying; (5) an innovative new service, DateLife.org, which synthesizes pre-computed, time-calibrated phylogenies to assign ages to nodes; and (6) demonstration projects. These outcomes are accessible via a public code repository (GitHub.com), a website (http://www.phylotastic.org), and a server image. CONCLUSIONS: Approximately 9 person-months of effort (centered on a software development hackathon) resulted in the design and implementation of proof-of-concept software for 4 core phylotastic components, 3 controllers, and 3 end-user demonstration tools. While these products have substantial limitations, they suggest considerable potential for a distributed system that makes phylogenetic knowledge readily accessible in computable form. Widespread use of phylotastic systems will create an electronic marketplace for sharing phylogenetic knowledge that will spur innovation in other areas of the ToL enterprise, such as annotation of sources and methods and third-party methods of quality assessment.

Sharing and re-use of phylogenetic trees (and associated data) to facilitate synthesis.

BACKGROUND: Recently, various evolution-related journals adopted policies to encourage or require archiving of phylogenetic trees and associated data. Such attention to practices that promote sharing of data reflects rapidly improving information technology, and rapidly expanding potential to use this technology to aggregate and link data from previously published research. Nevertheless, little is known about current practices, or best practices, for publishing trees and associated data so as to promote re-use. FINDINGS: Here we summarize results of an ongoing analysis of current practices for archiving phylogenetic trees and associated data, current practices of re-use, and current barriers to re-use. We find that the technical infrastructure is available to support rudimentary archiving, but the frequency of archiving is low. Currently, most phylogenetic knowledge is not easily re-used due to a lack of archiving, lack of awareness of best practices, and lack of community-wide standards for formatting data, naming entities, and annotating data. Most attempts at data re-use seem to end in disappointment. Nevertheless, we find many positive examples of data re-use, particularly those that involve customized species trees generated by grafting to, and pruning from, a much larger tree. CONCLUSIONS: The technologies and practices that facilitate data re-use can catalyze synthetic and integrative research. However, success will require engagement from various stakeholders including individual scientists who produce or consume shareable data, publishers, policy-makers, technology developers and resource-providers. The critical challenges for facilitating re-use of phylogenetic trees and associated data, we suggest, include: a broader commitment to public archiving; more extensive use of globally meaningful identifiers; development of user-friendly technology for annotating, submitting, searching, and retrieving data and their metadata; and development of a minimum reporting standard (MIAPA) indicating which kinds of data and metadata are most important for a re-useable phylogenetic record.

Population diversity of ORFan genes in Escherichia coli.

The origin and evolution of "ORFans" (suspected genes without known relatives) remain unclear. Here, we take advantage of a unique opportunity to examine the population diversity of thousands of ORFans, based on a collection of 35 complete genomes of isolates of Escherichia coli and Shigella (which is included phylogenetically within E. coli). As expected from previous studies, ORFans are shorter and AT-richer in sequence than non-ORFans. We find that ORFans often are very narrowly distributed: the most common pattern is for an ORFan to be found in only one genome. We compared within-species population diversity of ORFan genes with those of two control groups of non-ORFan genes. Patterns of population variation suggest that most ORFans are not artifacts, but encode real genes whose protein-coding capacity is conserved, reflecting selection against nonsynonymous mutations. Nevertheless, nonsynonymous nucleotide diversity is higher than for non-ORFans, whereas synonymous diversity is roughly the same. In particular, there is a several-fold excess of ORFans in the highest decile of diversity relative to controls, which might be due to weaker purifying selection, positive selection, or a subclass of ORFans that are decaying.

Constructive neutral evolution: exploring evolutionary theory's curious disconnect.

UNLABELLED: Constructive neutral evolution (CNE) suggests that neutral evolution may follow a stepwise path to extravagance. Whether or not CNE is common, the mere possibility raises provocative questions about causation: in classical neo-Darwinian thinking, selection is the sole source of creativity and direction, the only force that can cause trends or build complex features. However, much of contemporary evolutionary genetics departs from the conception of evolution underlying neo-Darwinism, resulting in a widening gap between what formal models allow, and what the prevailing view of the causes of evolution suggests. In particular, a mutationist conception of evolution as a 2-step origin-fixation process has been a source of theoretical innovation for 40 years, appearing not only in the Neutral Theory, but also in recent breakthroughs in modeling adaptation (the "mutational landscape" model), and in practical software for sequence analysis. In this conception, mutation is not a source of raw materials, but an agent that introduces novelty, while selection is not an agent that shapes features, but a stochastic sieve. This view, which now lays claim to important theoretical, experimental, and practical results, demands our attention. CNE provides a way to explore its most significant implications about the role of variation in evolution. REVIEWERS: Alex Kondrashov, Eugene Koonin and Johann Peter Gogarten reviewed this article.

NeXML: rich, extensible, and verifiable representation of comparative data and metadata.

In scientific research, integration and synthesis require a common understanding of where data come from, how much they can be trusted, and what they may be used for. To make such an understanding computer-accessible requires standards for exchanging richly annotated data. The challenges of conveying reusable data are particularly acute in regard to evolutionary comparative analysis, which comprises an ever-expanding list of data types, methods, research aims, and subdisciplines. To facilitate interoperability in evolutionary comparative analysis, we present NeXML, an XML standard (inspired by the current standard, NEXUS) that supports exchange of richly annotated comparative data. NeXML defines syntax for operational taxonomic units, character-state matrices, and phylogenetic trees and networks. Documents can be validated unambiguously. Importantly, any data element can be annotated, to an arbitrary degree of richness, using a system that is both flexible and rigorous. We describe how the use of NeXML by the TreeBASE and Phenoscape projects satisfies user needs that cannot be satisfied with other available file formats. By relying on XML Schema Definition, the design of NeXML facilitates the development and deployment of software for processing, transforming, and querying documents. The adoption of NeXML for practical use is facilitated by the availability of (1) an online manual with code samples and a reference to all defined elements and attributes, (2) programming toolkits in most of the languages used commonly in evolutionary informatics, and (3) input-output support in several widely used software applications. An active, open, community-based development process enables future revision and expansion of NeXML.