About

We use a combination of mathematical analysis, computer simulation, and laboratory experiments with microbes to explore theoretical and empirical angles on topics in ecology, evolutionary biology, and the philosophy of biology.

Research topics

• Computer Science
• Biology
• Genetics
• Artificial Intelligence
• Mathematics
• Microbiology
• Biological system
• Evolutionary biology
• Statistics

Selected publications

• Intracellular interactions shape antiviral resistance outcomes in poliovirus via eco-evolutionary feedback

  Nature Ecology & Evolution · 2025-12-05

  articleCorresponding
  PublisherDOI

• Intracellular interactions shape antiviral resistance outcomes in poliovirus via eco-evolutionary feedback

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-26

  preprintOpen access

  Abstract Antiviral resistance evolution poses a major obstacle for controlling viral infections. A promising strategy is to target shared viral proteins that allow drug susceptible viruses to sensitize resistant ones during cellular coinfection, muting selection for resistance. Pocapavir, a poliovirus capsid inhibitor, employs this sociovirological strategy. While susceptible viruses significantly suppressed resistance in the presence of pocapavir in cell culture, a pocapavir clinical trial observed widespread resistance evolution and limited improvements to clearance times. To reconcile these findings, we present an intra-host eco-evolutionary model of poliovirus in the presence of pocapavir, which reproduces both the potent interference observed in vitro and the resistance emergence seen in patients. In the short term, our model predicts that a high density of susceptible viruses sensitizes resistant ones to pocapavir, mirroring cell culture results. However, over multiple replication cycles, pocapavir’s high potency collapses viral density, which reduces coinfection and allows resistance to evolve as observed in the clinical trial. Since coinfection is essential to suppress resistance, enabling greater survival of susceptible viruses could offer therapeutic advantages. Counterintuitively, we demonstrate that this can be achieved by lessening antiviral potency, which can limit resistance evolution while also maintaining a low viral load. These findings suggest that antivirals that rely on viral intracellular interaction must balance immediate neutralization with the preservation of future coinfection, yielding more sustained inhibition. Explicitly considering the eco-evolutionary feedback encompassing viral density, shared phenotypes and absolute fitness not only provides new insights into designing effective therapies but also illuminates viral evolutionary dynamics more broadly.

  PublisherOA PDFDOI

• Agents of change: a partnership between mobile genetic elements facilitates rapid bacterial adaptation

  Trends in Microbiology · 2024-11-19 · 2 citations

  articleSenior author
  PublisherDOI

• Evolutionary “Crowdsourcing”: Alignment of Fitness Landscapes Allows for Cross-species Adaptation of a Horizontally Transferred Gene

  Molecular Biology and Evolution · 2023-10-31 · 14 citations

  articleOpen accessSenior author

  Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds. Despite the ubiquity of cross-species HGT, the effects of switching hosts on gene evolution remains understudied. Here, we present a framework to examine the evolutionary consequences of host-switching and apply this framework to an antibiotic resistance gene commonly found on conjugative plasmids. Specifically, we determined the adaptive landscape of this gene for a small set of mutationally connected genotypes in 3 enteric species. We uncovered that the landscape topographies were largely aligned with minimal host-dependent mutational effects. By simulating gene evolution over the experimentally gauged landscapes, we found that the adaptive evolution of the mobile gene in one species translated to adaptation in another. By simulating gene evolution over artificial landscapes, we found that sufficient alignment between landscapes ensures such "adaptive equivalency" across species. Thus, given adequate landscape alignment within a bacterial community, vehicles of HGT such as plasmids may enable a distributed form of genetic evolution across community members, where species can "crowdsource" adaptation.

  PublisherDOI

• Division of labor promotes the entrenchment of multicellularity

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-03-16 · 11 citations

  preprintOpen accessSenior authorCorresponding

  Abstract Simple multicellularity evolves readily in diverse unicellular species, but nascent multicellular groups are prone to reversion to unicellularity. Successful transitions to multicellularity therefore require subsequent mutations that promote the entrenchment of the higher-level unit, stabilizing it through time. Here we explore the causes of entrenchment using digital evolution. When faced with a trade-off between cellular metabolic productivity and information fidelity, digital “multicells” often evolve reproductive division of labor. Because digital “unicells” cannot circumvent this trade-off, unicellular revertants tend to exhibit low fitness relative to their differentiated multicellular ancestors. Thus, division of labor can drive the entrenchment of multicellularity. More generally, division of labor may play a crucial role in major transitions, enriching the complexity and functionality of higher-level units while enhancing their evolutionary stability.

  PublisherOA PDFDOI

• Evolutionary crowdsourcing: alignment of fitness landscapes allows cross-species adaptation of a horizontally transferred gene

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-09-15 · 1 citations

  preprintOpen accessSenior author

  Abstract Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds as they move between hosts, in contrast to genes that evolve under strict vertical inheritance. Despite the ubiquity of HGT in microbial communities, the effects of host-switching on gene evolution have been understudied. Here, we present a novel framework to examine the consequences of host-switching on gene evolution by probing the existence and form of host-dependent mutational effects. We started exploring the effects of HGT on gene evolution by focusing on an antibiotic resistance gene (encoding a beta-lactamase) commonly found on conjugative plasmids in Enterobacteriaceae pathogens. By reconstructing the resistance landscape for a small set of mutationally connected alleles in three enteric species ( Escherichia coli, Salmonella enterica , and Klebsiella pneumoniae ), we uncovered that the landscape topographies were largely aligned with very low levels of host-dependent mutational effects. By simulating gene evolution with and without HGT using the species-specific empirical landscapes, we found that evolutionary outcomes were similar despite HGT. These findings suggest that the adaptive evolution of a mobile gene in one species can translate to adaptation in another species. In such a case, vehicles of cross-species HGT such as plasmids enable a distributed form of genetic evolution across a bacterial community, where species can ‘crowdsource’ adaptation from other community members. The role of evolutionary crowdsourcing on the evolution of bacteria merits further investigation.

  PublisherOA PDFDOI

• Author response: Host-parasite coevolution promotes innovation through deformations in fitness landscapes

  2022-05-24

  peer-reviewOpen access

  An interdisciplinary approach combining high throughput genotype-to-phenotype mapping, population genetic simulations, and experimental evolution provides an answer to the question of how populations evolve new functions by providing tests of the role antagonistic coevolution plays in pressuring populations to innovate.

  PublisherDOI

• Host-parasite coevolution promotes innovation through deformations in fitness landscapes

  eLife · 2022-06-21 · 33 citations

  articleOpen access

  During the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high-throughput gene editing-phenotyping technology (MAGE-Seq) to measure the fitness landscape of a virus, bacteriophage λ, as it coevolved with its host, the bacterium Escherichia coli . An analysis of the empirical fitness landscape revealed mutation-by-mutation-by-host-genotype interactions that demonstrate coevolution modified the contours of λ’s landscape. Computer simulations of λ’s evolution on a static versus shifting fitness landscape showed that the changes in contours increased λ’s chances of evolving the ability to use a new host receptor. By coupling sequencing and pairwise competition experiments, we demonstrated that the first mutation λ evolved en route to the innovation would only evolve in the presence of the ancestral host, whereas later steps in λ’s evolution required the shift to a resistant host. When time-shift replays of the coevolution experiment were run where host evolution was artificially accelerated, λ did not innovate to use the new receptor. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities.

  PublisherDOI

• Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis

  PLoS Biology · 2022 · 30 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Biology

  To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria-Delbrück method ("LDM"), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude.

  PublisherDOI

• The cost of information acquisition by natural selection

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-07-03 · 11 citations

  preprintOpen access

  Natural selection enriches genotypes that are well-adapted to their environment. Over successive generations, these changes to the frequencies of types accumulate information about the selective conditions. Thus, we can think of selection as an algorithm by which populations acquire information about their environment. Kimura (1961) pointed out that every bit of information that the population gains this way comes with a minimum cost in terms of unrealized fitness (substitution load). Due to the gradual nature of selection and ongoing mismatch of types with the environment, a population that is still gaining information about the environment has lower mean fitness than a counter-factual population that already has this information. This has been an influential insight, but here we find that experimental evolution of Escherichia coli with mutations in a RNA polymerase gene ( rpoB ) violates Kimura’s basic theory. To overcome the restrictive assumptions of Kimura’s substitution load and develop a more robust measure for the cost of selection, we turn to ideas from computational learning theory. We reframe the ‘learning problem’ faced by an evolving population as a population versus environment (PvE) game, which can be applied to settings beyond Kimura’s theory – such as stochastic environments, frequency-dependent selection, and arbitrary environmental change. We show that the learning theoretic concept of ‘regret’ measures relative lineage fitness and rigorously captures the efficiency of selection as a learning process. This lets us establish general bounds on the cost of information acquisition by natural selection. We empirically validate these bounds in our experimental system, showing that computational learning theory can account for the observations that violate Kimura’s theory. Finally, we note that natural selection is a highly effective learning process in that selection is an asymptotically optimal algorithm for the problem faced by evolving populations, and no other algorithm can consistently outperform selection in general. Our results highlight the centrality of information to natural selection and the value of computational learning theory as a perspective on evolutionary biology.

  PublisherOA PDFDOI

Recent grants

• CAREER: Real-time evolution in host-pathogen networks

  NSF · $827k · 2010–2017

• DISSERTATION RESEARCH: Adaptation to variable ultraviolet radiation threats in alpine Daphnia populations.

  NSF · $12k · 2008–2010

• Collaborative Research: Adaptive Bridge or Barrier? The Impact of Horizontal Transfer on Genetic Evolution

  NSF · $799k · 2022–2027

• Collaborative Research: The Evolution of Pathogen Virulence in Experimental Metapopulations

  NSF · $428k · 2007–2011

• Virulence Tradeoffs in a Vertebrate Virus

  NSF · $986k · 2008–2012

Frequent coauthors

• Heather J. Goldsby

  H-Net: Humanities & Social Sciences Online

  31 shared

• Peter Godfrey‐Smith

  University of Sydney

  25 shared

• Charles Ofria

  Michigan State University

  24 shared

• Brian D. Connelly

  13 shared

• Sarah P. Hammarlund

  University of Minnesota System

  12 shared

• David B. Knoester

  12 shared

• Olivia Kosterlitz

  University of Washington

  10 shared

• Katherine J. Dickinson

  University of Washington

  10 shared

Labs

• Kerr LabPI

  We use a combination of mathematical analysis, computer simulation, and laboratory experiments with microbes to explore theoretical and empirical angles on topics in ecology, evolutionary biology, and the philosophy of biology.