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.


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
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
Wall, J. D., Lachance, J., Tishkoff, S. A., Hammer, M. F., Hsieh, P., & Gutenkunst, R. N. (2016). Model-based analyses of whole genome data reveal a complex evolutionary history involving archaic introgression in Central African Pygmies. Genome Research.
Myers, C. R., Gutenkunst, R. N., & Sethna, J. P. (2007). Python unleashed on systems biology. Computing in Science and Engineering, 9(3), 34-37.


Cornell University has developed an open source software system called SloppyCell, written in Python, to model biomolecular reaction networks. SloppyCell improves standard dynamical modeling by focusing on inference of model parameters from data and quantification of the uncertainties of model prediction. An important role in the software is to combine together many diverse modules that provide specific functionality. NumPy and SciPy were used for numeric, particularly for integrating differential equations, optimizing parameters by least squares fits to data, and analyzing the Hessian matrix about a best-fit set of parameters. Models are read and written in a standardized XML-based file format and the Systems Biology Markup Language (SBML) with assistance from a Python interface to the libSBML library.

Ragsdale, A. P., & Gutenkunst, R. N. (2017). Inferring demographic history using two-locus statistics. Genetics, 206, 1037.