In the study of biology, we seek simplicity to foster understanding. This quest is driven not only by philosophical desire, but also empirical necessity. Nature brings us immeasurable complexity at every conceivable level of organization — ecological communities are composed of interacting networks of dynamically evolving players; populations of species are defined by membership in closely interacting groups competing for shared resources; cell behavior is governed by a myriad of overlapping and intertwined biochemical pathways and cell-cell interactions; gene regulation is carried out by a web of interacting transcription factors, modified by the cellular environment and epigenetic landscape. All are complex interwoven products of evolution, subject to continual change not by a grand designer but by tinkering and chance. To gain insights into this complexity, our solution as biologists has been to simplify, and this has led to great successes. But, as we will argue below, it has also come with a cost.

The first key level of simplification has been to separate function from evolutionary history, and to organize the study of biology into these two distinct pursuits: ‘How do things work?’ and ‘How did they get that way?’. In the pursuit of functional understanding, the next step has been to simplify the system by isolating the organism from its environmental context. The tool of the trade is a lab-bred model organism — reared on defined growth media, isolated from its natural biotic and abiotic environments, stripped of its natural genetic heterozygosity, and removed from daily and seasonal fluctuations. Humanized mouse models — specialized pure-breeding lines carrying human genes, cells, tissues, or organs — serve as a canonical example of the quest to reduce the complexity of a biological system by isolating its individual components.

Simplification has, likewise, been a key to success in evolutionary biology. It is achieved in a different way, however, by separating the study of processes acting on populations over ecological timescales from the consequences of these processes as revealed on phylogenetic timescales. This separation of timescales has been justified by the belief that ecological change can be rapid — wildfires are an extreme example — whereas evolutionary change in response to an ecological challenge, such as increasingly frequent occurrence of wildfire, is a slow and gradual process. As such, the study of evolution has primarily concerned itself with the inference of history. This principle operates even in population genetics, which in theory bridges ecological processes that govern the fitness of individuals, with evolutionary changes in phenotypes of species driven by adaptation. Most of its machinery aims at making inferences about ostensibly slow evolutionary processes from time-frozen snapshots of genetic variation, with the ecological processes being of relevance only in their long-term average behavior. In ecology, this separation of timescales allows the projection of dynamics from the measurement of contemporary interactions, a tactic that sets aside evolutionary processes. The perceived difference in timescales over which ecological and evolutionary processes act has led to the de facto intellectual divorce of the two — their apparent marriage in the many academic departments entitled ‘Ecology and Evolution’ persisting primarily in name alone.

We contend that while powerful, this current modality of experimental simplification — to a single lab strain in a single environment, or to a single species, or to a static timeframe — handicaps our ability to comprehend nature as it actually exists. Biological entities do not exist in isolation but rather have evolved, and continue to evolve, while enmeshed in a network of ever-changing biotic and abiotic interactions. Even our own human body is, in fact, an entire ecosystem of interacting species and evolving somatic lineages (Ley et al., 2006; Martincorena, 2019). As each of these multiple entities evolves, so too does the network of their interactions (e.g. Fiegna et al., 2015; Urban and Skelly, 2006). Function in these systems is intimately linked to eco-evolutionary dynamics and the eco-evolutionary dynamics are, of course, intimately linked to function. There is no separation of timescales, just as there is no separation of the fields of inquiry. It is all one thing, not merely as a philosophical stance but as an entirely practical matter.

While we fully embrace the goal of simplicity of understanding, we argue that this simplicity must come from a source other than system simplification and isolation at the initiation of inquiry. Indeed, stripping experimental systems from their essential eco-evolutionary context not only makes the resulting functional insights less relevant, it can make them positively misleading. We argue that this approach runs the risk of simplifying away the very phenomena we strive to understand; in essence, eliminating the subject of inquiry before the inquiry even begins.

Our aim here is not to engage in an age-old debate between reductionism and holism, nor argue for more context and nuance for their own sake. Instead, we argue that specifically now, with the recent advances in high-throughput measurement and high-performance computing, we might finally be in a position to interrogate biological systems in their fuller eco-evolutionary context, with the goal of exposing the hidden regularities and arrive at an emergent simplicity of understanding. It is essential to embrace this challenge head-on if we are to understand biological function in the appropriate and relevant context, which we furthermore argue is necessary for any practical application.

We have suggested that failure to consider eco-evolutionary context can lead us astray in the understanding of biological function. Is there any evidence that it in fact does? We believe that the answer is yes at multiple levels, from individual genes to complex communities of organisms. Indeed, even a cursory survey provides multiple compelling examples. At the lowest level of biological organization, the function of a single gene cannot be understood without regard to environmental context. The fact that approximately 35% of genes in E. coli, perhaps the best studied organism in the lab, have no known function (Ghatak et al., 2019) is certainly not due to lack of effort, but speaks to the difficulty of understanding organisms in isolation. The function of genes that are recalcitrant to mutational knockout analysis in the laboratory can be revealed under their natural biotic conditions (Barbaric et al., 2007; Chanin et al., 2019; Cruz et al., 2016; Hutchison et al., 2016; Lin et al., 2020; Richter et al., 2009; Ruff et al., 2015). It stands to reason that the full elucidation of gene function, regulatory architecture and developmental robustness will require investigation in an organism’s natural environment.

Similarly, the genetic architecture of phenotypic variation cannot be easily understood outside of the appropriate environmental context. For example, the key developmental genes underlying flowering in Arabidopsis thaliana have been carefully dissected in greenhouse and growth chamber experiments. However, when this same species is grown in natural conditions, GWAS experiments reveal that natural variation in flowering is in fact largely due to variation in circadian clock genes (Brachi et al., 2010). It should therefore not be surprising that selective breeding programs performed in one environment fail to improve the yield of crops grown in another environment (Barrero Farfan et al., 2013). In these and many other examples, such as the difficulty of transferring polygenic scores in human GWAS (e.g. Rosenberg et al., 2019), the context matters enough that it changes the answer in entirely practical ways.

We also know that biotic interactions can be key. For instance, microorganisms often require the presence of their ecological partners in order to grow, a suggestion that was made at the dawn of microbiology (Winogradsky, 1935). This is true even for low-diversity enrichment communities formed in minimal media, where the metabolic activities of all co-occurring species dramatically transform the shared environment, thus permitting the growth of taxa that would otherwise be excluded (Estrela et al., 2020; Goldford et al., 2018). Studies have also found that our ability to isolate and cultivate soil bacteria is dramatically enhanced by embedding culture chambers in soil, further emphasizing the challenges that exist when we separate an individual species from its biotic context (Chaudhary et al., 2019; Kaeberlein et al., 2002; Nichols et al., 2010). Of course, the biotic context impacts not only survival. There is growing evidence, for example, that many human pathogens only become virulent when in the company of other community members (Hajishengallis and Lamont, 2016; Wiles and Guillemin, 2019). In a similar vein, mycorrhizal fungi rely on associated bacteria in order to provide services to their host plants (Minerdi et al., 2008). In all these examples, species studied in isolation would fail to display the defining functional phenotypes of interest.

Further, one might think that a functional and eco-evolutionary understanding of species interactions could be gleaned from a study of pairwise interactions between two species, such as a host and a pathogen. But, yet again, careful dissection of these interactions in Arabidopsis thaliana has revealed that these host-pathogen dynamics are driven by a diffuse web of many interactions, including co-occurring host species sharing interacting pathogen species (Karasov et al., 2014). The functional repercussions of this can be profound; for example, an investigation of the evolution of immunity has revealed that humans who live in regions of high pathogen diversity carry alleles with a broader efficacy, as has also been predicted by theory (Manczinger et al., 2019; Nourmohammad et al., 2016). In multispecies microbial consortia, important community functions are often the result of higher-than-pairwise functional interactions (Sanchez-Gorostiaga et al., 2019), and the evolution of species traits is tightly constrained by the network of interactions with co-evolving species (Lawrence et al., 2012; Scheuerl et al., 2020). Lastly, we can see that drivers of species interactions can be found at even higher levels of complexity. For example, Parker et al., 2015 found that disease pressure on host species of a grassland community were predicted not only by the abundance of the one host species of interest, a phenomenon that is predictable by two-species models, but also by the phylogenetic diversity of the entire plant community. The importance of community context in this last example is clear and compelling.

These few examples reveal the perils of ignoring ecological and environmental context. The same is true when we ignore evolutionary change, especially when it occurs on the same timescale as the functional and ecological dynamics in which we are interested. Of course, this has been long recognized in the burgeoning field of eco-evolutionary dynamics (Becks et al., 2012; Hairston et al., 2005; Pelletier et al., 2009; Post and Palkovacs, 2009). Here, we emphasize that these dynamics impinge not only on questions of ecology and evolution but also on biological function, as illustrated below (for more examples, see Box 1).

One recent, compelling example revealed that negative frequency-dependent competition amongst strains of malaria, and the selection that follows from this competition, establish a pattern of limiting similarity that makes the community more robust to perturbations (He et al., 2021). This demonstrates how ecological and evolutionary processes jointly determine community resilience, an important aspect of community function. In this example, the applied relevance is clear - interventions to control malaria must be more prolonged than currently implemented.

The relevance of eco-evolutionary dynamics is especially clear when we consider cancer, which we now understand to be a process of the evolution of competing somatic lineages. Cancer dynamics depend in crucial ways on the ecological dynamics of the complex cellular communities inside the body, involving interactions and cross signaling among multiple dividing, dying and differentiating cell types such as stroma, tumor, and immune cells (Rodrigues et al., 2021). The current interest in studying cancer within the richer biological context of organoids and genetically modified mouse models reflects the realization that cancer is unlikely to be fully understood by studying cell lines in test tubes, instead requiring consideration of the community of interacting and coevolving cells and cellular lineages. We also know now that even targeted cancer therapies impact tumor growth by affecting not just the tumor cells but also the immune system (Deng et al., 2018; Goel et al., 2017). The same is true for the cancer therapies aimed at the immune system that work very differently depending on the genotype of the tumor (Song et al., 2020).

Similarly, the etiology of many viral diseases is at its core an eco-evolutionary process that connects evolutionary dynamics of large viral populations to the shifting ecological landscape of the immune system. The appropriate scale may be within the body (e.g. HIV), or across population(s), and in relation to dynamics that may involve communities of interacting species (e.g. dengue, flu, SARS-CoV-2; Gibb et al., 2020; Gruber, 2017). The ability of HIV to evolve sufficiently quickly to outrun the immune system and maintain resistance is not the context of function - it is function, at least in the sense that this is what causes the disease of immune deficiency. Along these same lines, the ability of viruses to jump from one species to another, evolve an ability to infect, and then spread, is currently responsible for shutting down the economies of the whole world. Again, evolution is the ultimate cause of these problems, not just some extra context and nuance.

Given this vast complexity, and accepting that it often matters in key ways, how are we to make progress? If it is not effective to simplify our systems at the outset, must we give up the aspiration for simplicity of explanation? As we argue below, not necessarily.

First, complexity at one level of organization in no way precludes simplicity of explanation at a higher level. In fact, microscopically, all biological systems are impossibly complex. Predictable properties can only arise at some higher levels of organization: that is, simplicity is always emergent. Even something as ‘trivial’ as the exponential growth law of a population summarizes a tremendous complexity of microscopic processes of growth and reproduction. The exponential growth is not a property of any member of the population; it is a property of the entire population. Similarly, the enzymatic kinetics of Michaelis and Menten are immeasurably simpler than the microscopic laws of quantum chemistry ‘under the hood’, and our description of cell physiology rarely invokes the latter. At the next level of organization, the simplicity of quantitative phenomenological growth laws (Schaechter et al., 1958) has been ‘a source of wonder and inspiration’ (Scott and Hwa, 2011). Despite the immense underlying complexity of genetic and metabolic regulation, growth laws can be largely explained by a simple, coarse-grained argument of proteome allocation into ribosomal vs. non-ribosomal components (Basan et al., 2015; Scott et al., 2010). At higher levels of organization, organismal traits may be predicted from the laws of mendelian genetics, which were originally derived without requiring any knowledge of the molecular or cellular processes from which they emerge and well before we even knew that DNA was the molecular carrier of genetic information. No physiological or molecular knowledge is required either to calculate genotype frequencies under Hardy-Weinberg equilibrium, or the effects of drift and selection on allele frequency. Importantly, these and other fundamental results of population genetics describe emergent population-level properties, which lack meaning at the level of a single individual organism. Throughout the history of biology, our successes were never about simplifying away the complexity of a system, but about finding the appropriate coarse-graining.

Can simplicity of functional understanding arise also in systems of interacting and co-evolving populations and species? We suggest yes, partly based on conceptual argument and partly as a matter of empirical evidence. Consider the assembly of microbial communities, which typically consist of hundreds or thousands of taxa. At first glance, it would seem impossibly complex. And yet, recent studies illustrate that microbial ecosystems represent an illuminating example of predictable coarse-grained organization. For instance, the microbiomes of different individual bromeliad plants contain highly similar fractions of their metagenomes devoted to respiration, fermentation, methanogenesis, and other metabolic functions despite varying widely in the species they contain (Louca et al., 2017; Louca et al., 2016). A logical conclusion from this observation is that predictable, species-level compositions are not required for generating predictable dynamics at the level of metabolites. Such emergent simplicity arises generically in consumer-resource models (Goldford et al., 2018; Marsland et al., 2019), and it is also exhibited by enrichment communities assembled in simple synthetic environments, where ecological sources of variation (e.g. migration, abiotic factors, bottlenecks, etc.) can be controlled (Estrela et al., 2020; Goldford et al., 2018). In minimal sugar-limited environments, for example, two dominant metabolic groups are consistently found: One group is made by respiro-fermentative bacteria, which metabolize the lion's share of the sugar, while the second group is made by respirative bacteria, which specialize on the organic acids that are secreted as overflow metabolic byproducts by the former. The ratios of cell counts in each of these two metabolic groups are quantitatively consistent across communities that were started from different species pools in this system (Estrela et al., 2020; Goldford et al., 2018; Marsland et al., 2019). A similar metabolic organization has been found to repeatedly arise in experimental evolution studies that started from a single isogenic strain (Good et al., 2017; Rozen and Lenski, 2000; Treves et al., 1998). For instance, Rosenzweig and colleagues (Kinnersley et al., 2009; Rosenzweig et al., 1994) found that after only 700 generations in a glucose-limited chemostat, an initially isogenic population of E. coli had diversified into an ecosystem composed of four derived strains. One strain had adapted by increasing the rate of glucose uptake, while the others had specialized in the increased overflow metabolic secretions of the glucose specialist.

We highlight this example because it demonstrates elegantly that both top-down selection among a diverse pool of taxa and bottom-up community assembly via evolution from a single genotype lead to communities that predictably assemble into convergent functional structures. This reproducibility can be explained from a metabolic tradeoff that is fundamentally conserved across all kingdoms of life, where fast-growth in sugars is associated with the secretion of overflow metabolites into the environment. The amount of these byproducts produced per unit glucose consumed is governed by the glucose uptake rate: the faster a bacterium metabolizes glucose, the larger the amount of byproducts it releases (Basan et al., 2015). This leads to a tight covariation between the magnitude of both, even for isolates that belong to different genera (Estrela et al., 2020). Genome-scale metabolic models can correctly estimate the ratio of both metabolic groups by making such simple assumptions.

In these examples, predictability was enabled by the fact that metabolism is highly structured, only some solutions are sensible or mathematically possible, and bacterial taxa have been coevolving with each other for eons, making microbial communities very nonrandom. In a similar way, biochemical, physiological, and physical constraints, combined with (co)evolution, might often lead to predictable and, hence, potentially understandable outcomes in other microbial and non-microbial systems. It is our contention and hope that co-adaptation and co-evolution, especially when it comes to functionally important community or individual traits, will be governed by a limited number of driving forces that, when revealed, will allow for a simpler coarse-grained understanding to emerge.

So what are we proposing? As mentioned at the outset, we do not aim to rehash previous discussions on holism vs reductionism. This is not the conversation we are trying to have: the limitations of a reductionist approach are well established, as is the fact that the tremendous progress the biological sciences have experienced over the past centuries, both fundamental and applied, has been due almost entirely to this very approach. Our aim is not to argue that attempts to reconstruct the whole from pieces is futile. Neither are we asking for more and more complexity for complexity’s sake. Instead, we argue two points.

First, we must remember that ‘what the pieces do’ may not be clear when the whole is extracted from its biological context. This, of course, is not a new insight - we all know it to be true. When we design our experiments around simplified environments, it is not out of a belief that the natural context doesn't matter, but as a matter of feasibility. Our point here is that the time is ripe to act upon this knowledge, and eliminate the barriers of technological convenience. This is essential because the tools that we use shape the research that we do, and as we get better and better sequencing, for example, the comparative lack of tools for high-throughput phenotyping is becoming increasingly a problem. The danger is to keep zooming in on the increasingly microscopic details of species in isolation, where the existing street lights shine brightest. We therefore call for redoubling our efforts to develop new technology for high-throughput functional characterization of organisms in natural or semi-natural habitats of growing eco-evolutionary context (Mallard et al., 2020).

Second, if we are to find the right level of coarse graining, it is imperative to pick model systems of ‘appropriate’ contextual richness and with sufficient replication. But how much is enough? At the outset, we cannot know. Thus, we're advocating for model systems that can be integrated into an axis of tunable contextual richness: lab, semi-natural, and natural. As we increase the size and complexity of experimental systems in a controlled manner, we will search for emergent properties that only reveal themselves at higher levels of organization (and at the appropriate coarse-graining). We can attempt to search for these emergent properties in a systematic manner, rather than discovering them by serendipity. Such a concerted effort will benefit from looking across a diverse array of organisms because only then will consistent, and likely robust, emergent patterns become recognizable. This experimental methodology would go hand in hand with the recent calls in theoretical literature for models of ‘appropriate complexity’ (Getz et al., 2018). To make our proposal more concrete, we present a few representative areas in functional evolutionary biology (Box) where such an approach may be applied and plausibly push the field forward.

Finding the appropriate level of coarse-graining remains a matter of art, an art that becomes harder and harder as the systems become more complex. One exciting possibility is that machine learning and AI methods for performing automated variable selection for learning sparse predictive models will prove effective. However, there are no easy solutions here. Just like more data are not yet progress, a bigger computer alone will not suffice: AI can discover phenomenological regularities, but for such regularities to clue us into underlying causal forces and guide our interrogation of biological systems, data-intensive approaches need to be integrated with the eco-evolutionary theory of functional biology. These approaches will also inform the development of experimental systems in ecology and evolution that capture the appropriate eco-evolutionary context for organismal function, at least to some phenomenological degree.

Many of the most pressing problems facing humanity - from global change, to infectious disease and zoonosis, to cancer - are all problems of eco-evolutionary function. Confronting these problems will require that we build a sufficient understanding that we can predict, and even control, how these systems function as they evolve. These are challenging and yet crucial problems that will require concerted collaboration across currently disparate fields that extend much beyond the traditional boundaries of Ecology and Evolution, and include all branches of Organismal and Functional Biology, as well as Physics, Engineering, Statistics, and Computer Science.

As we have argued, the enterprise of studying organisms in isolation and as static, already evolved entities, while hugely successful, is also profoundly limited. Our belief, which lies in contrast to Jacques Monod’s famous dictum — ‘Anything found to be true of E. coli must also be true of elephants’ — is that much of what one learns about the functional behavior of E.coli in isolation hardly even extends to E.coli in the gut, let alone to elephants in the savannah. The heart of our proposal, here, is thus the creation of a new field of Function of Evolving Systems that focuses on the function of organisms in the communities in which they reside, over periods of time when interactions evolve. It is both exciting intellectually, and essential practically.

1. 1. Barbaric I
   2. Miller G
   3. Dear TN

   (2007) Appearances can be deceiving: phenotypes of knockout mice

   Briefings in Functional Genomics and Proteomics 6:91–103.

   https://doi.org/10.1093/bfgp/elm008

   • PubMed
   • Google Scholar
2. 1. Barraclough TG

   (2015) How do species interactions affect evolutionary dynamics across whole communities?

   Annual Review of Ecology, Evolution, and Systematics 46:25–48.

   https://doi.org/10.1146/annurev-ecolsys-112414-054030

   • Google Scholar
3. 1. Barrero Farfan ID
   2. Murray SC
   3. Labar S
   4. Pietsch D

   (2013) A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize

   Field Crops Research 149:167–176.

   https://doi.org/10.1016/j.fcr.2013.04.017

   • Google Scholar
4. 1. Barreto HC
   2. Sousa A
   3. Gordo I

   (2020) The landscape of adaptive evolution of a gut commensal Bacteria in aging mice

   Current Biology 30:1102–1109.

   https://doi.org/10.1016/j.cub.2020.01.037

   • PubMed
   • Google Scholar
5. 1. Barrett RD

   (2010) Adaptive evolution of lateral plates in three-spined stickleback Gasterosteus aculeatus: a case study in functional analysis of natural variation

   Journal of Fish Biology 77:311–328.

   https://doi.org/10.1111/j.1095-8649.2010.02640.x

   • PubMed
   • Google Scholar
6. 1. Barrett RDH
   2. Laurent S
   3. Mallarino R
   4. Pfeifer SP
   5. Xu CCY
   6. Foll M
   7. Wakamatsu K
   8. Duke-Cohan JS
   9. Jensen JD
   10. Hoekstra HE

   (2019) Linking a mutation to survival in wild mice

   Science 363:499–504.

   https://doi.org/10.1126/science.aav3824

   • PubMed
   • Google Scholar
7. 1. Basan M
   2. Hui S
   3. Okano H
   4. Zhang Z
   5. Shen Y
   6. Williamson JR
   7. Hwa T

   (2015) Overflow metabolism in Escherichia coli results from efficient proteome allocation

   Nature 528:99–104.

   https://doi.org/10.1038/nature15765

   • PubMed
   • Google Scholar
8. 1. Basan M

   (2018) Resource allocation and metabolism: the search for governing principles

   Current Opinion in Microbiology 45:77–83.

   https://doi.org/10.1016/j.mib.2018.02.008

   • PubMed
   • Google Scholar
9. 1. Bashan A
   2. Gibson TE
   3. Friedman J
   4. Carey VJ
   5. Weiss ST
   6. Hohmann EL
   7. Liu YY

   (2016) Universality of human microbial dynamics

   Nature 534:259–262.

   https://doi.org/10.1038/nature18301

   • PubMed
   • Google Scholar
10. 1. Becks L
    2. Ellner SP
    3. Jones LE
    4. Hairston NG

    (2012) The functional genomics of an eco-evolutionary feedback loop: linking gene expression, trait evolution, and community dynamics

    Ecology Letters 15:492–501.

    https://doi.org/10.1111/j.1461-0248.2012.01763.x

    • PubMed
    • Google Scholar
11. 1. Bergelson J
    2. Purrington CB

    (1996) Surveying patterns in the cost of resistance in plants

    The American Naturalist 148:536–558.

    https://doi.org/10.1086/285938

    • Google Scholar
12. 1. Bergland AO
    2. Behrman EL
    3. O'Brien KR
    4. Schmidt PS
    5. Petrov DA

    (2014) Genomic evidence of rapid and stable adaptive oscillations over seasonal time scales in Drosophila

    PLOS Genetics 10:e1004775.

    https://doi.org/10.1371/journal.pgen.1004775

    • PubMed
    • Google Scholar
13. 1. Brachi B
    2. Faure N
    3. Horton M
    4. Flahauw E
    5. Vazquez A
    6. Nordborg M
    7. Bergelson J
    8. Cuguen J
    9. Roux F

    (2010) Linkage and association mapping of Arabidopsis thaliana flowering time in nature

    PLOS Genetics 6:e1000940.

    https://doi.org/10.1371/journal.pgen.1000940

    • PubMed
    • Google Scholar
14. 1. Brenner K
    2. You L
    3. Arnold FH

    (2009) Response to Goldman and Brown: making sense of microbial consortia using ecology and evolution

    Trends in Biotechnology 27:4.

    https://doi.org/10.1016/j.tibtech.2008.10.004

    • Google Scholar
15. 1. Cardinale S
    2. Arkin AP

    (2012) Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems

    Biotechnology Journal 7:856–866.

    https://doi.org/10.1002/biot.201200085

    • PubMed
    • Google Scholar
16. 1. Chang CY
    2. Vila JCC
    3. Bender M
    4. Li R
    5. Mankowski MC
    6. Bassette M
    7. Borden J
    8. Golfier S
    9. Sanchez PGL
    10. Waymack R
    11. Zhu X
    12. Diaz-Colunga J
    13. Estrela S
    14. Rebolleda-Gomez M
    15. Sanchez A

    (2021) Engineering complex communities by directed evolution

    Nature Ecology & Evolution.

    https://doi.org/10.1038/s41559-021-01457-5

    • PubMed
    • Google Scholar
17. 1. Chanin RB
    2. Nickerson KP
    3. Llanos-Chea A
    4. Sistrunk JR
    5. Rasko DA
    6. Kumar DKV
    7. de la Parra J
    8. Auclair JR
    9. Ding J
    10. Li K
    11. Dogiparthi SK
    12. Kusber BJD
    13. Faherty CS

    (2019) Shigella flexneri adherence factor expression in In Vivo-Like Conditions

    mSphere 4:e00751-19.

    https://doi.org/10.1128/mSphere.00751-19

    • PubMed
    • Google Scholar
18. 1. Chaudhary DK
    2. Khulan A
    3. Kim J

    (2019) Development of a novel cultivation technique for uncultured soil Bacteria

    Scientific Reports 9:6666.

    https://doi.org/10.1038/s41598-019-43182-x

    • PubMed
    • Google Scholar
19. 1. Cruz JA
    2. Savage LJ
    3. Zegarac R
    4. Hall CC
    5. Satoh-Cruz M
    6. Davis GA
    7. Kovac WK
    8. Chen J
    9. Kramer DM

    (2016) Dynamic environmental photosynthetic imaging reveals emergent phenotypes

    Cell Systems 2:365–377.

    https://doi.org/10.1016/j.cels.2016.06.001

    • PubMed
    • Google Scholar
20. 1. Deng J
    2. Wang ES
    3. Jenkins RW
    4. Li S
    5. Dries R
    6. Yates K
    7. Chhabra S
    8. Huang W
    9. Liu H
    10. Aref AR
    11. Ivanova E
    12. Paweletz CP
    13. Bowden M
    14. Zhou CW
    15. Herter-Sprie GS
    16. Sorrentino JA
    17. Bisi JE
    18. Lizotte PH
    19. Merlino AA
    20. Quinn MM
    21. Bufe LE
    22. Yang A
    23. Zhang Y
    24. Zhang H
    25. Gao P
    26. Chen T
    27. Cavanaugh ME
    28. Rode AJ
    29. Haines E
    30. Roberts PJ
    31. Strum JC
    32. Richards WG
    33. Lorch JH
    34. Parangi S
    35. Gunda V
    36. Boland GM
    37. Bueno R
    38. Palakurthi S
    39. Freeman GJ
    40. Ritz J
    41. Haining WN
    42. Sharpless NE
    43. Arthanari H
    44. Shapiro GI
    45. Barbie DA
    46. Gray NS
    47. Wong KK

    (2018) CDK4/6 inhibition augments antitumor immunity by enhancing T-cell activation

    Cancer Discovery 8:216–233.

    https://doi.org/10.1158/2159-8290.CD-17-0915

    • PubMed
    • Google Scholar
21. Preprint

    1. Estrela S
    2. Vila JCC
    3. Lu N
    4. Bajic D
    5. Rebolleda-Gomez M
    6. Chang C-Y
    7. Sanchez A

    (2020) Metabolic rules of microbial community assembly

    bioRxiv.

    https://doi.org/10.1101/2020.03.09.984278

    • Google Scholar
22. 1. Evans R
    2. Beckerman AP
    3. Wright RCT
    4. McQueen-Mason S
    5. Bruce NC
    6. Brockhurst MA

    (2020) Eco-evolutionary dynamics set the tempo and trajectory of metabolic evolution in multispecies communities

    Current Biology 30:4984–4988.

    https://doi.org/10.1016/j.cub.2020.09.028

    • Google Scholar
23. 1. Exposito-Alonso M
    2. Burbano HA
    3. Bossdorf O
    4. Nielsen R
    5. Weigel D
    6. 500 Genomes Field Experiment Team

    (2019) Natural selection on the Arabidopsis thaliana genome in present and future climates

    Nature 573:126–129.

    https://doi.org/10.1038/s41586-019-1520-9

    • PubMed
    • Google Scholar
24. 1. Fiegna F
    2. Moreno-Letelier A
    3. Bell T
    4. Barraclough TG

    (2015) Evolution of species interactions determines microbial community productivity in new environments

    The ISME Journal 9:1235–1245.

    https://doi.org/10.1038/ismej.2014.215

    • PubMed
    • Google Scholar
25. 1. Frachon L
    2. Libourel C
    3. Villoutreix R
    4. Carrère S
    5. Glorieux C
    6. Huard-Chauveau C
    7. Navascués M
    8. Gay L
    9. Vitalis R
    10. Baron E
    11. Amsellem L
    12. Bouchez O
    13. Vidal M
    14. Le Corre V
    15. Roby D
    16. Bergelson J
    17. Roux F

    (2017) Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time

    Nature Ecology & Evolution 1:1551–1561.

    https://doi.org/10.1038/s41559-017-0297-1

    • PubMed
    • Google Scholar
26. 1. Getz WM
    2. Marshall CR
    3. Carlson CJ
    4. Giuggioli L
    5. Ryan SJ
    6. Romañach SS
    7. Boettiger C
    8. Chamberlain SD
    9. Larsen L
    10. D'Odorico P
    11. O'Sullivan D

    (2018) Making ecological models adequate

    Ecology Letters 21:153–166.

    https://doi.org/10.1111/ele.12893

    • PubMed
    • Google Scholar
27. 1. Ghatak S
    2. King ZA
    3. Sastry A
    4. Palsson BO

    (2019) The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function

    Nucleic Acids Research 47:2446–2454.

    https://doi.org/10.1093/nar/gkz030

    • PubMed
    • Google Scholar
28. 1. Gibb R
    2. Franklinos LHV
    3. Redding DW
    4. Jones KE

    (2020) Ecosystem perspectives are needed to manage zoonotic risks in a changing climate

    BMJ 371:m3389.

    https://doi.org/10.1136/bmj.m3389

    • PubMed
    • Google Scholar
29. 1. Goel S
    2. DeCristo MJ
    3. Watt AC
    4. BrinJones H
    5. Sceneay J
    6. Li BB
    7. Khan N
    8. Ubellacker JM
    9. Xie S
    10. Metzger-Filho O
    11. Hoog J
    12. Ellis MJ
    13. Ma CX
    14. Ramm S
    15. Krop IE
    16. Winer EP
    17. Roberts TM
    18. Kim HJ
    19. McAllister SS
    20. Zhao JJ

    (2017) CDK4/6 inhibition triggers anti-tumour immunity

    Nature 548:471–475.

    https://doi.org/10.1038/nature23465

    • PubMed
    • Google Scholar
30. 1. Goldford JE
    2. Lu N
    3. Bajić D
    4. Estrela S
    5. Tikhonov M
    6. Sanchez-Gorostiaga A
    7. Segrè D
    8. Mehta P
    9. Sanchez A

    (2018) Emergent simplicity in microbial community assembly

    Science 361:469–474.

    https://doi.org/10.1126/science.aat1168

    • PubMed
    • Google Scholar
31. 1. Goldman RP
    2. Brown SP

    (2009) Making sense of microbial consortia using ecology and evolution

    Trends in Biotechnology 27:3–4.

    https://doi.org/10.1016/j.tibtech.2008.10.003

    • PubMed
    • Google Scholar
32. 1. Good BH
    2. McDonald MJ
    3. Barrick JE
    4. Lenski RE
    5. Desai MM

    (2017) The dynamics of molecular evolution over 60,000 generations

    Nature 551:45–50.

    https://doi.org/10.1038/nature24287

    • PubMed
    • Google Scholar
33. 1. Graves JL
    2. Hertweck KL
    3. Phillips MA
    4. Han MV
    5. Cabral LG
    6. Barter TT
    7. Greer LF
    8. Burke MK
    9. Mueller LD
    10. Rose MR

    (2017) Genomics of parallel experimental evolution in Drosophila

    Molecular Biology and Evolution 34:831–842.

    https://doi.org/10.1093/molbev/msw282

    • PubMed
    • Google Scholar
34. 1. Gruber K

    (2017) Predicting zoonoses

    Nature Ecology & Evolution 1:98.

    https://doi.org/10.1038/s41559-017-0098

    • PubMed
    • Google Scholar
35. 1. Hairston NG
    2. Ellner SP
    3. Geber MA
    4. Yoshida T
    5. Fox JA

    (2005) Rapid evolution and the convergence of ecological and evolutionary time

    Ecology Letters 8:1114–1127.

    https://doi.org/10.1111/j.1461-0248.2005.00812.x

    • Google Scholar
36. 1. Hajishengallis G
    2. Lamont RJ

    (2016) Dancing with the stars: how choreographed bacterial interactions dictate nososymbiocity and give rise to keystone pathogens, accessory pathogens, and pathobionts

    Trends in Microbiology 24:477–489.

    https://doi.org/10.1016/j.tim.2016.02.010

    • PubMed
    • Google Scholar
37. 1. Hamid I
    2. Korunes KL
    3. Beleza S
    4. Goldberg A

    (2021) Rapid adaptation to malaria facilitated by admixture in the human population of cabo verde

    eLife 10:e63177.

    https://doi.org/10.7554/eLife.63177

    • PubMed
    • Google Scholar
38. 1. He Q
    2. Pilosof S
    3. Tiedje KE
    4. Day KP
    5. Pascual M

    (2021) Frequency-Dependent competition between strains imparts persistence to perturbations in a model of Plasmodium falciparum malaria transmission

    Frontiers in Ecology and Evolution 9:319.

    https://doi.org/10.3389/fevo.2021.633263

    • Google Scholar
39. 1. Herron MD
    2. Doebeli M

    (2013) Parallel evolutionary dynamics of adaptive diversification in Escherichia coli

    PLOS Biology 11:e1001490.

    https://doi.org/10.1371/journal.pbio.1001490

    • PubMed
    • Google Scholar
40. 1. Hsu SK
    2. Belmouaden C
    3. Nolte V
    4. Schlötterer C

    (2021) Parallel gene expression evolution in natural and laboratory evolved populations

    Molecular Ecology 30:884–894.

    https://doi.org/10.1111/mec.15649

    • PubMed
    • Google Scholar
41. 1. Hutchison CA
    2. Chuang RY
    3. Noskov VN
    4. Assad-Garcia N
    5. Deerinck TJ
    6. Ellisman MH
    7. Gill J
    8. Kannan K
    9. Karas BJ
    10. Ma L
    11. Pelletier JF
    12. Qi ZQ
    13. Richter RA
    14. Strychalski EA
    15. Sun L
    16. Suzuki Y
    17. Tsvetanova B
    18. Wise KS
    19. Smith HO
    20. Glass JI
    21. Merryman C
    22. Gibson DG
    23. Venter JC

    (2016) Design and synthesis of a minimal bacterial genome

    Science 351:aad6253.

    https://doi.org/10.1126/science.aad6253

    • PubMed
    • Google Scholar
42. 1. Juenger T
    2. Bergelson J

    (1998) Pairwise versus diffuse natural selection and the multiple herbivores of scarlet Glia, ipomopsis Aggregata

    Evolution 52:1583–1592.

    https://doi.org/10.1111/j.1558-5646.1998.tb02239.x

    • Google Scholar
43. 1. Kaeberlein T
    2. Lewis K
    3. Epstein SS

    (2002) Isolating "uncultivable" microorganisms in pure culture in a simulated natural environment

    Science 296:1127–1129.

    https://doi.org/10.1126/science.1070633

    • PubMed
    • Google Scholar
44. 1. Karasov TL
    2. Kniskern JM
    3. Gao L
    4. DeYoung BJ
    5. Ding J
    6. Dubiella U
    7. Lastra RO
    8. Nallu S
    9. Roux F
    10. Innes RW
    11. Barrett LG
    12. Hudson RR
    13. Bergelson J

    (2014) The long-term maintenance of a resistance polymorphism through diffuse interactions

    Nature 512:436–440.

    https://doi.org/10.1038/nature13439

    • PubMed
    • Google Scholar
45. 1. Kinnersley MA
    2. Holben WE
    3. Rosenzweig F

    (2009) E unibus plurum: genomic analysis of an experimentally evolved polymorphism in Escherichia coli

    PLOS Genetics 5:e1000713.

    https://doi.org/10.1371/journal.pgen.1000713

    • PubMed
    • Google Scholar
46. 1. Lässig M
    2. Mustonen V
    3. Walczak AM

    (2017) Predicting evolution

    Nature Ecology & Evolution 1:77.

    https://doi.org/10.1038/s41559-017-0077

    • PubMed
    • Google Scholar
47. 1. Lawrence D
    2. Fiegna F
    3. Behrends V
    4. Bundy JG
    5. Phillimore AB
    6. Bell T
    7. Barraclough TG

    (2012) Species interactions alter evolutionary responses to a novel environment

    PLOS Biology 10:e1001330.

    https://doi.org/10.1371/journal.pbio.1001330

    • PubMed
    • Google Scholar
48. 1. Lenski RE

    (2017) Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations

    The ISME Journal 11:2181–2194.

    https://doi.org/10.1038/ismej.2017.69

    • PubMed
    • Google Scholar
49. 1. Lenski RE
    2. Travisano M

    (1994) Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations

    PNAS 91:6808–6814.

    https://doi.org/10.1073/pnas.91.15.6808

    • PubMed
    • Google Scholar
50. 1. Ley RE
    2. Peterson DA
    3. Gordon JI

    (2006) Ecological and evolutionary forces shaping microbial diversity in the human intestine

    Cell 124:837–848.

    https://doi.org/10.1016/j.cell.2006.02.017

    • PubMed
    • Google Scholar
51. 1. Lin J-D
    2. Devlin JC
    3. Yeung F
    4. McCauley C
    5. Leung JM
    6. Chen Y-H
    7. Cronkite A
    8. Hansen C
    9. Drake-Dunn C
    10. Ruggles KV
    11. Cadwell K
    12. Graham AL
    13. Loke P’ng

    (2020) Rewilding Nod2 and Atg16l1 mutant mice uncovers genetic and environmental contributions to microbial responses and immune cell composition

    Cell Host & Microbe 27:830–840.

    https://doi.org/10.1016/j.chom.2020.03.001

    • Google Scholar
52. 1. Litchman E
    2. Klausmeier CA
    3. Schofield OM
    4. Falkowski PG

    (2007) The role of functional traits and trade-offs in structuring phytoplankton communities: scaling from cellular to ecosystem level

    Ecology Letters 10:1170–1181.

    https://doi.org/10.1111/j.1461-0248.2007.01117.x

    • PubMed
    • Google Scholar
53. 1. Litchman E
    2. Edwards KF
    3. Klausmeier CA

    (2015) Microbial resource utilization traits and trade-offs: implications for community structure, functioning, and biogeochemical impacts at present and in the future

    Frontiers in Microbiology 6:254.

    https://doi.org/10.3389/fmicb.2015.00254

    • PubMed
    • Google Scholar
54. 1. Louca S
    2. Jacques SMS
    3. Pires APF
    4. Leal JS
    5. Srivastava DS
    6. Parfrey LW
    7. Farjalla VF
    8. Doebeli M

    (2016) High taxonomic variability despite stable functional structure across microbial communities

    Nature Ecology & Evolution 1:15.

    https://doi.org/10.1038/s41559-016-0015

    • PubMed
    • Google Scholar
55. 1. Louca S
    2. Jacques SMS
    3. Pires APF
    4. Leal JS
    5. González AL
    6. Doebeli M
    7. Farjalla VF

    (2017) Functional structure of the bromeliad tank microbiome is strongly shaped by local geochemical conditions

    Environmental Microbiology 19:3132–3151.

    https://doi.org/10.1111/1462-2920.13788

    • PubMed
    • Google Scholar
56. 1. Mallard F
    2. Le Bourlot V
    3. Le Coeur C
    4. Avnaim M
    5. Péronnet R
    6. Claessen D
    7. Tully T

    (2020) From individuals to populations: how intraspecific competition shapes thermal reaction norms

    Functional Ecology 34:669–683.

    https://doi.org/10.1111/1365-2435.13516

    • Google Scholar
57. 1. Manczinger M
    2. Boross G
    3. Kemény L
    4. Müller V
    5. Lenz TL
    6. Papp B
    7. Pál C

    (2019) Pathogen diversity drives the evolution of generalist MHC-II alleles in human populations

    PLOS Biology 17:e3000131.

    https://doi.org/10.1371/journal.pbio.3000131

    • PubMed
    • Google Scholar
58. 1. Marsland R
    2. Cui W
    3. Goldford J
    4. Sanchez A
    5. Korolev K
    6. Mehta P

    (2019) Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities

    PLOS Computational Biology 15:e1006793.

    https://doi.org/10.1371/journal.pcbi.1006793

    • PubMed
    • Google Scholar
59. 1. Martincorena I

    (2019) Somatic mutation and clonal expansions in human tissues

    Genome Medicine 11:35.

    https://doi.org/10.1186/s13073-019-0648-4

    • PubMed
    • Google Scholar
60. 1. McCarty NS
    2. Ledesma-Amaro R

    (2019) Synthetic biology tools to engineer microbial communities for biotechnology

    Trends in Biotechnology 37:181–197.

    https://doi.org/10.1016/j.tibtech.2018.11.002

    • Google Scholar
61. 1. Minerdi D
    2. Moretti M
    3. Gilardi G
    4. Barberio C
    5. Gullino ML
    6. Garibaldi A

    (2008) Bacterial ectosymbionts and virulence silencing in a Fusarium oxysporum strain

    Environmental Microbiology 10:1725–1741.

    https://doi.org/10.1111/j.1462-2920.2008.01594.x

    • PubMed
    • Google Scholar
62. 1. Nichols D
    2. Cahoon N
    3. Trakhtenberg EM
    4. Pham L
    5. Mehta A
    6. Belanger A
    7. Kanigan T
    8. Lewis K
    9. Epstein SS

    (2010) Use of ichip for high-throughput in situ cultivation of "uncultivable" microbial species

    Applied and Environmental Microbiology 76:2445–2450.

    https://doi.org/10.1128/AEM.01754-09

    • PubMed
    • Google Scholar
63. 1. Nourmohammad A
    2. Otwinowski J
    3. Plotkin JB

    (2016) Host-Pathogen coevolution and the emergence of broadly neutralizing antibodies in chronic infections

    PLOS Genetics 12:e1006171.

    https://doi.org/10.1371/journal.pgen.1006171

    • PubMed
    • Google Scholar
64. 1. Parker IM
    2. Saunders M
    3. Bontrager M
    4. Weitz AP
    5. Hendricks R
    6. Magarey R
    7. Suiter K
    8. Gilbert GS

    (2015) Phylogenetic structure and host abundance drive disease pressure in communities

    Nature 520:542–544.

    https://doi.org/10.1038/nature14372

    • PubMed
    • Google Scholar
65. 1. Pelletier F
    2. Garant D
    3. Hendry AP

    (2009) Eco-evolutionary dynamics

    Philosophical Transactions of the Royal Society B: Biological Sciences 364:1483–1489.

    https://doi.org/10.1098/rstb.2009.0027

    • PubMed
    • Google Scholar
66. 1. Post DM
    2. Palkovacs EP

    (2009) Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play

    Philosophical Transactions of the Royal Society B: Biological Sciences 364:1629–1640.

    https://doi.org/10.1098/rstb.2009.0012

    • PubMed
    • Google Scholar
67. 1. Richter SH
    2. Garner JP
    3. Würbel H

    (2009) Environmental standardization: cure or cause of poor reproducibility in animal experiments?

    Nature Methods 6:257–261.

    https://doi.org/10.1038/nmeth.1312

    • PubMed
    • Google Scholar
68. 1. Rodrigues J
    2. Heinrich MA
    3. Teixeira LM
    4. Prakash J

    (2021) 3d in vitro model (R)evolution: unveiling Tumor-Stroma interactions

    Trends in Cancer 7:249–264.

    https://doi.org/10.1016/j.trecan.2020.10.009

    • PubMed
    • Google Scholar
69. 1. Rosenberg NA
    2. Edge MD
    3. Pritchard JK
    4. Feldman MW

    (2019) Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

    Evolution, Medicine, and Public Health 2019:26–34.

    https://doi.org/10.1093/emph/eoy036

    • PubMed
    • Google Scholar
70. 1. Rosenzweig RF
    2. Sharp RR
    3. Treves DS
    4. Adams J

    (1994) Microbial evolution in a simple unstructured environment: genetic differentiation in Escherichia coli

    Genetics 137:903–917.

    https://doi.org/10.1093/genetics/137.4.903

    • PubMed
    • Google Scholar
71. 1. Rozen DE
    2. Lenski RE

    (2000) Long-Term experimental evolution in Escherichia coli. VIII. Dynamics of a balanced polymorphism

    The American Naturalist 155:24–35.

    https://doi.org/10.1086/303299

    • PubMed
    • Google Scholar
72. 1. Ruff JS
    2. Saffarini RB
    3. Ramoz LL
    4. Morrison LC
    5. Baker S
    6. Laverty SM
    7. Tvrdik P
    8. Potts WK

    (2015) Fitness assays reveal incomplete functional redundancy of the HoxA1 and HoxB1 paralogs of mice

    Genetics 201:727–736.

    https://doi.org/10.1534/genetics.115.178079

    • PubMed
    • Google Scholar
73. 1. Sánchez Á
    2. Vila JCC
    3. Chang CY
    4. Diaz-Colunga J
    5. Estrela S
    6. Rebolleda-Gomez M

    (2021) Directed evolution of microbial communities

    Annual Review of Biophysics 50:323–341.

    https://doi.org/10.1146/annurev-biophys-101220-072829

    • PubMed
    • Google Scholar
74. 1. Sanchez-Gorostiaga A
    2. Bajić D
    3. Osborne ML
    4. Poyatos JF
    5. Sanchez A

    (2019) High-order interactions distort the functional landscape of microbial consortia

    PLOS Biology 17:e3000550.

    https://doi.org/10.1371/journal.pbio.3000550

    • PubMed
    • Google Scholar
75. 1. Schaechter M
    2. Maaløe O
    3. Kjeldgaard NO

    (1958) Dependency on medium and temperature of cell size and chemical composition during balanced growth of Salmonella typhimurium

    Microbiology 19:592–606.

    https://doi.org/10.1099/00221287-19-3-592

    • Google Scholar
76. 1. Scheuerl T
    2. Hopkins M
    3. Nowell RW
    4. Rivett DW
    5. Barraclough TG
    6. Bell T

    (2020) Bacterial adaptation is constrained in complex communities

    Nature Communications 11:754.

    https://doi.org/10.1038/s41467-020-14570-z

    • PubMed
    • Google Scholar
77. 1. Scott M
    2. Gunderson CW
    3. Mateescu EM
    4. Zhang Z
    5. Hwa T

    (2010) Interdependence of cell growth and gene expression: origins and consequences

    Science 330:1099–1102.

    https://doi.org/10.1126/science.1192588

    • PubMed
    • Google Scholar
78. 1. Scott M
    2. Hwa T

    (2011) Bacterial growth laws and their applications

    Current Opinion in Biotechnology 22:559–565.

    https://doi.org/10.1016/j.copbio.2011.04.014

    • PubMed
    • Google Scholar
79. 1. Song P
    2. Yang D
    3. Wang H
    4. Cui X
    5. Si X
    6. Zhang X
    7. Zhang L

    (2020) Relationship between the efficacy of immunotherapy and characteristics of specific tumor mutation genes in non-small cell lung Cancer patients

    Thoracic Cancer 11:1647–1654.

    https://doi.org/10.1111/1759-7714.13447

    • PubMed
    • Google Scholar
80. 1. Stearns SC

    (1989) Trade-Offs in Life-History evolution

    Functional Ecology 3:259–268.

    https://doi.org/10.2307/2389364

    • Google Scholar
81. 1. Stearns SC

    (2000) Life history evolution: successes, limitations, and prospects

    Naturwissenschaften 87:476–486.

    https://doi.org/10.1007/s001140050763

    • PubMed
    • Google Scholar
82. 1. Stern DL
    2. Orgogozo V

    (2008) The loci of evolution: how predictable is genetic evolution?

    Evolution 62:2155–2177.

    https://doi.org/10.1111/j.1558-5646.2008.00450.x

    • Google Scholar
83. 1. Strauss SY
    2. Irwin RE

    (2004) Ecological and evolutionary consequences of multispecies Plant-Animal interactions

    Annual Review of Ecology, Evolution, and Systematics 35:435–466.

    https://doi.org/10.1146/annurev.ecolsys.35.112202.130215

    • Google Scholar
84. 1. Tas H
    2. Grozinger L
    3. Stoof R
    4. de Lorenzo V
    5. Goñi-Moreno Á

    (2021) Contextual dependencies expand the re-usability of genetic inverters

    Nature Communications 12:355.

    https://doi.org/10.1038/s41467-020-20656-5

    • PubMed
    • Google Scholar
85. 1. Treves DS
    2. Manning S
    3. Adams J

    (1998) Repeated evolution of an acetate-crossfeeding polymorphism in long-term populations of Escherichia coli

    Molecular Biology and Evolution 15:789–797.

    https://doi.org/10.1093/oxfordjournals.molbev.a025984

    • PubMed
    • Google Scholar
86. 1. Urban MC
    2. Skelly DK

    (2006) Evolving metacommunities: toward an evolutionary perspective on metacommunities

    Ecology 87:1616–1626.

    https://doi.org/10.1890/0012-9658(2006)87[1616:EMTAEP]2.0.CO;2

    • PubMed
    • Google Scholar
87. 1. Wiles TJ
    2. Guillemin K

    (2019) The other side of the coin: what beneficial microbes can teach us about pathogenic potential

    Journal of Molecular Biology 431:2946–2956.

    https://doi.org/10.1016/j.jmb.2019.05.001

    • PubMed
    • Google Scholar
88. 1. Williams K-A
    2. Pennings P

    (2020) Drug resistance evolution in HIV in the late 1990s: hard sweeps, soft sweeps, clonal interference and the accumulation of drug resistance mutations

    G3: Genes, Genomes, Genetics 10:1213–1223.

    https://doi.org/10.1534/g3.119.400772

    • Google Scholar
89. 1. Winogradsky S

    (1935) The method in soil microbiology as illustrated by studies on Azotobacter and the nitrifying organisms

    Soil Science 40:59–76.

    https://doi.org/10.1097/00010694-193507000-00009

    • Google Scholar

Author details

1. Joy Bergelson

   Department of Ecology & Evolution, University of Chicago, Chicago, United States

   Present address

   Department of Biology, NYU, NY, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Contributed equally with

   Martin Kreitman, Dmitri A Petrov, Alvaro Sanchez and Mikhail Tikhonov

   For correspondence

   jbergels@uchicago.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7893-7387

2. Martin Kreitman

   Department of Ecology & Evolution, University of Chicago, Chicago, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Contributed equally with

   Joy Bergelson, Dmitri A Petrov, Alvaro Sanchez and Mikhail Tikhonov

   Competing interests

   No competing interests declared

3. Dmitri A Petrov

   Department of Biology, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Contributed equally with

   Joy Bergelson, Martin Kreitman, Alvaro Sanchez and Mikhail Tikhonov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3664-9130

4. Alvaro Sanchez

   Department of Ecology & Evolutionary Biology, Yale University, New Haven, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Contributed equally with

   Joy Bergelson, Martin Kreitman, Dmitri A Petrov and Mikhail Tikhonov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2292-5608

5. Mikhail Tikhonov

   Department of Physics, Washington University in St Louis, St. Louis, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Contributed equally with

   Joy Bergelson, Martin Kreitman, Dmitri A Petrov and Alvaro Sanchez

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9558-1121

• 11,822

  views

• 1,119

  downloads

• 36

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.