Ryan N Gutenkunst
Associate Department Head, Molecular and Cellular Biology
Associate Professor, Applied BioSciences - GIDP
Associate Professor, Applied Mathematics - GIDP
Associate Professor, BIO5 Institute
Associate Professor, Cancer Biology -
Associate Professor, Ecology and Evolutionary Biology
Associate Professor, Genetics - GIDP
Associate Professor, Molecular and Cellular Biology
Associate Professor, Public Health
Associate Professor, Statistics-GIDP
Director, Graduate Studies
Member of the Graduate Faculty
Primary Department
(520) 626-0569
Work Summary
We learn history from the genomes of humans, tumors, and other species. Our studies reveal how evolution works at the molecular level, offering fundamental insight into how humans and pathogens adapt to challenges.
Research Interest
The Gutenkunst group studies the function and evolution of the complex molecular networks that comprise life. To do so, they integrate computational population genomics, bioinformatics, and molecular evolution. They focus on developing new computational methods to extract biological insight from genomic data and applying those methods to understand population history and natural selection.


Dornhaus, A. R., Gutenkunst, R. N., Wang, X., & Leighton, G. M. (2017). Behavioral caste is associated with distinct gene expression profiles in workers in Temnothorax rugatulus. BMC Genomics.
BIO5 Collaborators
Anna R Dornhaus, Ryan N Gutenkunst
Qi, X., An, H., Ragsdale, A. P., Hall, T. E., Gutenkunst, R. N., Pires, J. C., & Barker, M. S. (2017). Genome wide analyses of diverse Brassica rapa cultivars reveal significant genetic structure and corroborate historical record of domestication. Molecular Ecology.
BIO5 Collaborators
Michael S Barker, Ryan N Gutenkunst
Altshuler, D. L., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., Clark, A. G., Collins, F. S., M., F., Donnelly, P., Egholm, M., Flicek, P., Gabriel, S. B., Gibbs, R. A., Knoppers, B. M., Lander, E. S., Lehrach, H., Mardis, E. R., McVean, G. A., Nickerson, D. A., , Peltonen, L., et al. (2010). A map of human genome variation from population-scale sequencing. Nature, 467(7319), 1061-1073.

PMID: 20981092;PMCID: PMC3042601;Abstract:

The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10 g-8 per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research. © 2010 Macmillan Publishers Limited. All rights reserved. © 2010 Macmillan Publishers Limited. All rights reserved.

Hermansen, R. A., Mannakee, B. K., Knecht, W., Liberles, D. A., & Gutenkunst, R. N. (2015). Characterizing selective pressures on the pathway for de novo biosynthesis of pyrimidines in yeast. BMC Evolutionary Biology, 15.
Colvin, J., Monine, M. I., Gutenkunst, R. N., Hlavacek, W. S., D., D., & Posner, R. G. (2010). RuleMonkey: Software for stochastic simulation of rule-based models. BMC Bioinformatics, 11.

PMID: 20673321;PMCID: PMC2921409;Abstract:

Background: The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.Results: Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods.Conclusions: RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application http://public.tgen.org/rulemonkey. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models. © 2010 Colvin et al; licensee BioMed Central Ltd.