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  • 1. Berdahl, Andrew
    et al.
    Brelsford, Christa
    De Bacco, Caterina
    Dumas, Marion
    Ferdinand, Vanessa
    Grochow, Joshua A.
    Hebert-Dufresne, Laurent
    Kallus, Yoav
    Kempes, Christopher P.
    Kolchinsky, Artemy
    Larremore, Daniel B.
    Libby, Eric
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Power, Eleanor A.
    Stern, Caitlin A.
    Tracey, Brendan D.
    Dynamics of beneficial epidemics2019Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, artikel-id 15093Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Pathogens can spread epidemically through populations. Beneficial contagions, such as viruses that enhance host survival or technological innovations that improve quality of life, also have the potential to spread epidemically. How do the dynamics of beneficial biological and social epidemics differ from those of detrimental epidemics? We investigate this question using a breadth-first modeling approach involving three distinct theoretical models. First, in the context of population genetics, we show that a horizontally-transmissible element that increases fitness, such as viral DNA, spreads superexponentially through a population, more quickly than a beneficial mutation. Second, in the context of behavioral epidemiology, we show that infections that cause increased connectivity lead to superexponential fixation in the population. Third, in the context of dynamic social networks, we find that preferences for increased global infection accelerate spread and produce superexponential fixation, but preferences for local assortativity halt epidemics by disconnecting the infected from the susceptible. We conclude that the dynamics of beneficial biological and social epidemics are characterized by the rapid spread of beneficial elements, which is facilitated in biological systems by horizontal transmission and in social systems by active spreading behavior of infected individuals.

  • 2. Estrela, Sylvie
    et al.
    Libby, Eric
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Santa Fe Institute, Santa Fe, NM, USA.
    Van Cleve, Jeremy
    Debarre, Florence
    Deforet, Maxime
    Harcombe, William R.
    Pena, Jorge
    Brown, Sam P.
    Hochberg, Michael E.
    Environmentally Mediated Social Dilemmas2019Ingår i: Trends in Ecology & Evolution, ISSN 0169-5347, E-ISSN 1872-8383, Vol. 34, nr 1, s. 6-18Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    By consuming and producing environmental resources, organisms inevitably change their habitats. The consequences of such environmental modifications can be detrimental or beneficial not only to the focal organism but also to other organisms sharing the same environment. Social evolution theory has been very influential in studying how social interactions mediated by public 'goods' or 'bads' evolve by emphasizing the role of spatial structure. The environmental dimensions driving these interactions, however, are typically abstracted away. We propose here a new, environment-mediated taxonomy of social behaviors where organisms are categorized by their production or consumption of environmental factors that can help or harm others in the environment. We discuss microbial examples of our classification and highlight the importance of environmental intermediates more generally.

  • 3.
    Libby, Eric
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Modularity of the life cycle2019Ingår i: Nature Ecology & Evolution, E-ISSN 2397-334X, Vol. 3, nr 8, s. 1142-1143Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    Life stages in Bacillus subtilis are controlled by regulatory blocks that can be kept or lost across species in response to selection in different environments.

  • 4.
    Libby, Eric
    et al.
    Santa Fe Institute, Santa Fe, NM, 87501, USA.
    Driscoll, William W.
    Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, 55108, USA.
    Ratcliff, William C.
    School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
    Programmed cell death can increase the efficacy of microbial bet hedging2018Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, nr 1, artikel-id 1120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Programmed cell death (PCD) occurs in both unicellular and multicellular organisms. While PCD plays a key role in the development and maintenance of multicellular organisms, explaining why single-celled organisms would evolve to actively commit suicide has been far more challenging. Here, we explore the potential for PCD to act as an accessory to microbial bet-hedging strategies that utilize stochastic phenotype switching. We consider organisms that face unpredictable and recurring disasters, in which fitness depends on effective phenotypic diversification. We show that when reproductive opportunities are limited by carrying capacity, PCD drives population turnover, providing increased opportunities for phenotypic diversification through stochastic phenotype switching. The main cost of PCD, providing resources for growth to a PCD(−) competitor, is ameliorated by genetic assortment in spatially structured populations. Using agent -based simulations, we explore how basic demographic factors, namely bottlenecks and local dispersal, can generate sufficient spatial structure to favor the evolution of high PCD rates.

  • 5.
    Libby, Eric
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Santa Fe Institute, Santa Fe, New Mexico, United States of America.
    Hebert-Dufresne, Laurent
    Hosseini, Sayed-Rzgar
    Wagner, Andreas
    Syntrophy emerges spontaneously in complex metabolic systems2019Ingår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, nr 7, artikel-id e1007169Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Syntrophy allows a microbial community as a whole to survive in an environment, even though individual microbes cannot. The metabolic interdependence typical of syntrophy is thought to arise from the accumulation of degenerative mutations during the sustained co-evolution of initially self-sufficient organisms. An alternative and underexplored possibility is that syntrophy can emerge spontaneously in communities of organisms that did not co-evolve. Here, we study this de novo origin of syntrophy using experimentally validated computational techniques to predict an organism's viability from its metabolic reactions. We show that pairs of metabolisms that are randomly sampled from a large space of possible metabolism and viable on specific primary carbon sources often become viable on new carbon sources by exchanging metabolites. The same biochemical reactions that are required for viability on primary carbon sources also confer viability on novel carbon sources. Our observations highlight a new and important avenue for the emergence of metabolic adaptations and novel ecological interactions.

  • 6.
    Libby, Eric
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. IceLab.
    Lind, Peter A.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet).
    Probabilistic Models for Predicting Mutational Routes to New Adaptive Phenotypes2019Ingår i: Bio-protocol, ISSN 2331-8325, Vol. 9, nr 20, artikel-id 3407Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Understanding the translation of genetic variation to phenotypic variation is a fundamental problem in genetics and evolutionary biology. The introduction of new genetic variation through mutation can lead to new adaptive phenotypes, but the complexity of the genotype-to-phenotype map makes it challenging to predict the phenotypic effects of mutation. Metabolic models, in conjunction with flux balance analysis, have been used to predict evolutionary optimality. These methods however rely on large scale models of metabolism, describe a limited set of phenotypes, and assume that selection for growth rate is the prime evolutionary driver.

    Here we describe a method for computing the relative likelihood that mutational change will translate into a phenotypic change between two molecular pathways. The interactions of molecular components in the pathways are modeled with ordinary differential equations. Unknown parameters are offset by probability distributions that describe the concentrations of molecular components, the reaction rates for different molecular processes, and the effects of mutations. Finally, the likelihood that mutations in a pathway will yield phenotypic change is estimated with stochastic simulations.

    One advantage of this method is that only basic knowledge of the interaction network underlying a phenotype is required. However, it can also incorporate available information about concentrations and reaction rates as well as mutational biases and mutational robustness of molecular components. The method estimates the relative probabilities that different pathways produce phenotypic change, which can be combined with fitness models to predict evolutionary outcomes.

  • 7.
    Libby, Eric
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Santa Fe Institute, Santa Fe, New Mexico.
    Ratcliff, William C.
    Shortsighted Evolution Constrains the Efficacy of Long-Term Bet Hedging2019Ingår i: American Naturalist, ISSN 0003-0147, E-ISSN 1537-5323, Vol. 193, nr 3, s. 409-423Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To survive unpredictable environmental change, many organisms adopt bet-hedging strategies that are initially costly but provide a long-term fitness benefit. The temporal extent of these deferred fitness benefits determines whether bet-hedging organisms can survive long enough to realize them. In this article, we examine a model of microbial bet hedging in which there are two paths to extinction: unpredictable environmental change and demographic stochasticity. In temporally correlated environments, these drivers of extinction select for different switching strategies. Rapid phenotype switching ensures survival in the face of unpredictable environmental change, while slower-switching organisms become extinct. However, when both switching strategies are present in the same population, then demographic stochasticity-enforced by a limited population size-leads to extinction of the faster-switching organism. As a result, we find a novel form of evolutionary suicide whereby selection in a fluctuating environment can favor bet-hedging strategies that ultimately increase the risk of extinction. Population structures with multiple subpopulations and dispersal can reduce the risk of extinction from unpredictable environmental change and shift the balance so as to facilitate the evolution of slower-switching organisms.

  • 8.
    Lind, Peter A
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet). New Zealand Institute for Advanced Study, Massey University at Albany, Auckland, New Zealand.
    Libby, Eric
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. New Zealand Institute for Advanced Study, Massey University at Albany, Auckland, New Zealand ; 3 Santa Fe Institute, New Mexico, United States.
    Herzog, Jenny
    Rainey, Paul B
    Predicting mutational routes to new adaptive phenotypes2019Ingår i: eLIFE, E-ISSN 2050-084X, Vol. 8, s. 1-31, artikel-id e38822Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Predicting evolutionary change poses numerous challenges. Here we take advantage of the model bacterium Pseudomonas fluorescens in which the genotype-to-phenotype map determining evolution of the adaptive ‘wrinkly spreader’ (WS) type is known. We present mathematical descriptions of three necessary regulatory pathways and use these to predict both the rate at which each mutational route is used and the expected mutational targets. To test predictions, mutation rates and targets were determined for each pathway. Unanticipated mutational hotspots caused experimental observations to depart from predictions but additional data led to refined models. A mismatch was observed between the spectra of WS-causing mutations obtained with and without selection due to low fitness of previously undetected WS-causing mutations. Our findings contribute toward the development of mechanistic models for forecasting evolution, highlight current limitations, and draw attention to challenges in predicting locus-specific mutational biases and fitness effects.

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