About

Joanna Masel is a faculty member in the Ecology & Evolutionary Biology department at the University of Arizona. Her research interests include theoretical population genetics, evolution of robustness, and evolvability. She is involved in exploring the mathematical and biological principles underlying genetic variation and evolutionary processes, contributing to the understanding of how organisms adapt and evolve over time.

Research topics

• Biology
• Medicine
• Internal medicine
• Demography
• Virology
• Genetics
• Psychology
• Psychiatry
• Bioinformatics
• Pediatrics
• Evolutionary biology
• Immunology

Selected publications

• What good is modeling? Introducing biology students to theory

  arXiv (Cornell University) · 2026-04-14

  preprintOpen access1st authorCorresponding

  Theory and empirical science should be in constant dialogue, but often find it hard to understand one another. Here we describe a graduate-level university course we developed to improve matters. The course was designed to help empirically-focused biology graduate students read and understand theory papers, despite little prior mathematical training. It uses several evidence-based principles of modern teaching: backwards design, active learning, and just-in-time teaching. We believe that this or similar curricular content, emphasizing the nature of evidence and the role of theory in science, will improve critical thinking and scientific progress.

  PublisherDOI

• What good is modeling? Introducing biology students to theory

  ArXiv.org · 2026-04-14

  articleOpen access1st authorCorresponding

  Theory and empirical science should be in constant dialogue, but often find it hard to understand one another. Here we describe a graduate-level university course we developed to improve matters. The course was designed to help empirically-focused biology graduate students read and understand theory papers, despite little prior mathematical training. It uses several evidence-based principles of modern teaching: backwards design, active learning, and just-in-time teaching. We believe that this or similar curricular content, emphasizing the nature of evidence and the role of theory in science, will improve critical thinking and scientific progress.

  PublisherOA PDF

• How and why does aging occur? Updating evolutionary theory to meet a new era of data

  Evolution Medicine and Public Health · 2025-12-20

  articleOpen access

  Our ability to define the causes of aging could enable targeted interventions to extend healthspan. Classical evolutionary models based on individual age have provided critical insights into empirical trajectories of aging; however, gaps remain. We argue that technological advances in data capture, resolution, and scale present a rich opportunity to shed light on heterogeneity in patterns of aging. Computational and data analysis advances have produced expanded theoretical models that explicitly address details of the underlying biology, introducing variables and dynamics that go beyond 'age' itself. We argue that by incorporating richer biological detail to create more integrative predictive models, we can gain insight into expected future distributions of aging within populations, and better understand the molecular and demographic context in which selection has given rise to variability in aging. We provide an overview of existing models that address heterogeneity, and outline future directions and applications that would advance this key area in aging and biomedical research.

  PublisherOA PDFDOI

• Early incorporation of sulfur-containing amino acids into the genetic code

  2025-01-01

  article
  PublisherDOI

• Enhanced testing can substantially improve defense against several types of respiratory virus pandemic

  Epidemics · 2025-01-13 · 1 citations

  articleOpen accessSenior author

  , time to peak viral load) and on the testing strategy (limit of detection, testing frequency, test turnaround time, adherence). We base time-dependent test sensitivity and time-dependent infectiousness on an underlying viral load trajectory model. We show that given moderately high public adherence, frequent testing can prevent as many transmissions as more costly interventions such as school or business closures. With very high adherence and fast, frequent, and sensitive testing, we show that most respiratory virus pandemics could be controlled with mass testing alone.

  PublisherDOI

• Substitution load revisited: a high proportion of deaths can be selective

  Genetics · 2025-01-25 · 2 citations

  articleOpen accessSenior author

  Haldane's Dilemma refers to the concern that the need for many "selective deaths" to complete a substitution (i.e. selective sweep) creates a speed limit to adaptation. However, discussion of this concern has been marked by confusion, especially with respect to the term "substitution load". Here, we distinguish different historical lines of reasoning, and identify one, focused on finite reproductive excess and the proportion of deaths that are "selective" (i.e. causally contribute to adaptive allele frequency changes), that has not yet been fully addressed. We develop this into a more general theoretical model that can apply to populations with any life history, even those for which a generation or even an individual are not well defined. The actual speed of adaptive evolution is coupled to the proportion of deaths that are selective. The degree to which reproductive excess enables a high proportion of selective deaths depends on the details of when selection takes place relative to density regulation, and there is therefore no general expression for a speed limit. To make these concepts concrete, we estimate both reproductive excess, and the proportion of deaths that are selective, from a dataset measuring survival of 517 different genotypes of Arabidopsis thaliana grown in 8 different environmental conditions. In this dataset, a much higher proportion of deaths contribute to adaptation, in all environmental conditions, than the 10% cap that was anticipated as substantially restricting adaptation during historical discussions of speed limits.

  PublisherOA PDFDOI

• A Mechanistically Integrated Model of Exploitative and Interference Competition over a Single Resource Produces Widespread Coexistence

  The American Naturalist · 2025-07-16 · 1 citations

  articleSenior author

  Many ecological models treat exploitative competition in isolation from interference competition. Corresponding theory centers around the R* rule, according to which consumers that share a single limiting resource cannot coexist. Here we model motile consumers that directly interfere while handling resources, mechanistically capturing both exploitative and interference competition. Our analytical coexistence conditions show that interference competition readily promotes coexistence. In contrast to previous theory, coexistence does not require intraspecific interference propensities to exceed interspecific interference propensities or for interference behaviors to carry a direct (rather than merely an opportunity) cost. The underlying mechanism of coexistence can resemble the hawk-dove game, the dominance-discovery trade-off (akin to the competition-colonization trade-off), or a novel trade-off we call the “dove-discovery trade-off,” depending on parameter values. Competitive exclusion via the R* rule occurs only when differences in exploitative abilities swamp other differences between species, and it occurs more easily when differences in R* reflect different search speeds than when they reflect different handling times. Our model provides a mathematically tractable framework that integrates exploitative and interference competition and synthesizes previous disparate models.

  PublisherDOI

• Improved gene tree inference from removing alignment errors both from focal genes and when training substitution models

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-02 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract Multiple Sequence Alignment (MSA) is a key step in phylogenetic analysis, and is prone to error. Unfortunately, algorithms that remove likely alignment errors from MSAs sometimes also remove informative residues, making phylogenetic tree inference worse. Here we present a novel MSA cleaning algorithm based on consensus between MSAs using a range of Hidden Markov Models and guide trees, named CLOAK ( CL eaning O n the basis of A lignment C( K )onsensus). CLOAK is a gentle filter, with a low false positive rate for removal from MSAs according to the BALiBASE benchmarks, while still removing a significant fraction of likely alignment errors. Gentle vs. stringent MSA filtering methods are appropriate for different tasks. We assess methods based on their ability to bring the gene trees of single copy orthologs closer to the accepted species tree. Amino acid substitution models trained on filtered MSAs improve gene tree inference, with stricter filtering methods providing the biggest model improvements. In contrast, it is gentler filtering of single gene MSAs that provides additional improvements to gene tree inference, with CLOAK performing best.

  PublisherDOI

• Author response: The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder

  2024-09-06

  peer-reviewOpen accessSenior author

  Evolution is the process through which populations change over time, starting with mutations in the genetic sequence of an organism. Many of these mutations harm the survival and reproduction of an organism, but only by a very small amount. Some species, especially those with large populations, can purge these slightly harmful mutations more effectively than other species. This fact has been used by the ‘drift barrier theory’ to explain various profound differences amongst species, including differences in biological complexity. In this theory, the effectiveness of eliminating slightly harmful mutations is specified by an ‘effective' population size, which depends on factors beyond just the number of individuals in the population. Effective population size is normally calculated from the amount of time a ‘neutral’ mutation (one with no effect at all) stays in the population before becoming lost or taking over. Estimating this time requires both representative data for genetic diversity and knowledge of the mutation rate. A major limitation is that these data are unavailable for most species. A second limitation is that a brief, temporary reduction in the number of individuals has an oversized impact on the metric, relative to its impact on the number of slighly harmful mutations accumulated. Weibel, Wheeler et al. developed a new metric to more directly determine how effectively a species purges slightly harmful mutations. Their approach is based on the fact that the genetic code has ‘synonymous’ sequences. These sequences code for the same amino acid building block, with one of these sequences being only slightly preferred over others. The metric by Weibel, Wheeler et al. quantifies the proportion of the genome from which less preferred synonymous sequences have been effectively purged. It judges a population to have a higher effective population size when the usage of synonymous sequences departs further from the usage predicted from mutational processes. The researchers expected that natural selection would favour ‘ordered’ proteins with robust three-dimensional structures, i.e., that species with a higher effective population size would tend to have more ordered versions of a protein. Instead, they found the opposite: species with a higher effective population size tend to have more disordered versions of the same protein. This changes our view of how natural selection acts on proteins. Why species are so different remains a fundamental question in biology. Weibel, Wheeler et al. provide a useful tool for future applications of drift barrier theory to a broad range of ways that species differ.

  PublisherDOI

• Order of amino acid recruitment into the genetic code resolved by last universal common ancestor’s protein domains

  Proceedings of the National Academy of Sciences · 2024-12-12 · 30 citations

  articleOpen accessSenior authorCorresponding

  The current "consensus" order in which amino acids were added to the genetic code is based on potentially biased criteria, such as the absence of sulfur-containing amino acids from the Urey-Miller experiment which lacked sulfur. More broadly, abiotic abundance might not reflect biotic abundance in the organisms in which the genetic code evolved. Here, we instead identify which protein domains date to the last universal common ancestor (LUCA) and then infer the order of recruitment from deviations of their ancestrally reconstructed amino acid frequencies from the still-ancient post-LUCA controls. We find that smaller amino acids were added to the code earlier, with no additional predictive power in the previous consensus order. Metal-binding (cysteine and histidine) and sulfur-containing (cysteine and methionine) amino acids were added to the genetic code much earlier than previously thought. Methionine and histidine were added to the code earlier than expected from their molecular weights and glutamine later. Early methionine availability is compatible with inferred early use of S-adenosylmethionine and early histidine with its purine-like structure and the demand for metal binding. Even more ancient protein sequences-those that had already diversified into multiple distinct copies prior to LUCA-have significantly higher frequencies of aromatic amino acids (tryptophan, tyrosine, phenylalanine, and histidine) and lower frequencies of valine and glutamic acid than single-copy LUCA sequences. If at least some of these sequences predate the current code, then their distinct enrichment patterns provide hints about earlier, alternative genetic codes.

  PublisherDOI

Recent grants

• NIH Grant R01GM076041

  NIH · $565k · 2011

• THE CONVERSION OF NONCODING SEQUENCES INTO PROTEINS

  NIH · $839k · 2013–2019

• Ecological effects of clonal interference in a changing environment

  NSF · $250k · 2014–2018

Frequent coauthors

• Jennifer James

  Science for Life Laboratory

  28 shared

• Catherine Weibel

  University of Arizona

  22 shared

• Katrin Mende

  San Antonio Military Medical Center

  22 shared

• Anuradha Ganesan

  Uniformed Services University of the Health Sciences

  22 shared

• Jason Bertram

  University of Arizona

  21 shared

• Rhonda E Colombo

  Madigan Army Medical Center

  21 shared

• David A Lindholm

  Uniformed Services University of the Health Sciences

  21 shared

• Sara M Willis

  Uppsala University

  19 shared

Education

• D.Phil., Zoology

  University of Oxford

  2020

• B.Sc.(Hons), Genetics

  University of Melbourne

  1995