Arlin Stoltzfus

Research Biologist

Stoltzfus Group

Contact

Email: arlin@umd.edu

Call: (240) 314-6208

Education

  • Postdoctoral Fellow, Dalhousie University, 1999
  • Ph.D. Biology, University of Iowa, 1991      
  • B.A., English, Grinnell College, 1985

Profile

Dr. Stoltzfus's research addresses issues in molecular evolution, bioinformatics, and evolutionary theory that are amenable to computer-based approaches. The group explores models and tests hypotheses, develops software, and participates in community efforts to improve interoperability. A major ongoing interest is understanding the role of mutation in evolution, an area where the group has developed novel theory and presented novel results on the foundational issue of how biases in mutation influence the course of evolution. Other topics of interest include the evolution of introns, Bayesian methods for fossil calibration of phylogenies, models of constructive neutral evolution, and the history and philosophy of evolutionary biology.

CURRENT RESEARCH

Mutation-biased evolution

Drs. Yampolsky and Stoltzfus (2001) showed that mutational and developmental biases in the introduction of variation can influence evolution, contrary to classical arguments that selection controls the course of evolution because mutation "pressure" is weak. Recently, a wealth of information on cases of parallel molecular adaptation has made it possible to validate the distinctive prediction that mutation biases can be effectual even during adaptive evolution (Stoltzfus and McCandlish, 2017; Storz, et al., in press).

Meta-analysis of high-throughput fitness data

Drs. Yampolsky and Stoltzfus (2005) were the first to use experimental data to develop an empirical model of amino acid exchangeability. Drs. Stoltzfus and Norris (2015) used fitness data on over 1,000 protein mutants to show that, contrary to conventional wisdom in molecular evolution, transition mutations that change amino acids in proteins are not meaningfully more conservative than transversions. Ongoing work on amino acid exchangeability involves dozens of high-throughput, "deep mutational scanning" studies covering over 105 mutants (McCandlish and Stoltzfus, in preparation).

Disseminating the Tree of Life

With collaborators at the University of Tennessee, Knoxville, New Mexico State University, and the University of Illinois Urbana-Champaign, the Stoltzfus group is building a "Phylotastic" system to extract species trees from existing resources on the fly. The Phylotastic web portal (http://portal.phylotastic.org) provides several user-friendly workflows for obtaining species trees.

Fitness quantile distribution for 56641 amino acid mutations, grouped by source (row) and destination (column), with fitted maximum entropy distribution. Shaded histograms are not substantially or not significantly different from the background (flat) distribution. McCandlish and Stoltzfus (unpublished). 
Publications
2024
A fitness distribution law for amino-acid replacements.
Agent-based modeling of the COVID-19 pandemic in Florida.
Mining proteomes for zinc finger persulfidation.
Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.
Chemoselective Proteomics, Zinc Fingers, and a Zinc(II) Model for H2S Mediated Persulfidation.
2023
Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates.
Screening in serum-derived medium reveals differential response to compounds targeting metabolism.
Multiple models for outbreak decision support in the face of uncertainty.
Mutation bias and the predictability of evolution.
Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.
2022
Mutation bias shapes the spectrum of adaptive substitutions.
2020
COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.
Avoidance of Self during CRISPR Immunization.
Phylotastic: Improving Access to Tree-of-Life Knowledge With Flexible, on-the-Fly Delivery of Trees.
2019
The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function.
2017
Why we don't want another "Synthesis".
Mutational Biases Influence Parallel Adaptation.
2015
On the Causes of Evolutionary Transition:Transversion Bias.
Mutation-biased adaptation in Andean house wrens.
2014
Modeling evolution using the probability of fixation: history and implications.