Ryan N Gutenkunst

Ryan N Gutenkunst

Associate Department Head, Molecular and Cellular Biology
Associate Professor, Applied BioSciences - GIDP
Associate Professor, Applied Mathematics - GIDP
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
Associate Professor, BIO5 Institute
Member of the Graduate Faculty
Director, Graduate Studies
Primary Department
Contact
(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.

Publications

Myers, C. R., Gutenkunst, R. N., & Sethna, J. P. (2007). Python unleashed on systems biology. Computing in Science and Engineering, 9(3), 34-37.

Abstract:

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.
Chylek, L. A., Hu, B., Blinov, M. L., Emonet, T., Faeder, J. R., Goldstein, B., Gutenkunst, R. N., Haugh, J. M., Lipniacki, T., Posner, R. G., Yang, J., & Hlavacek, W. S. (2011). Guidelines for visualizing and annotating rule-based models. Molecular bioSystems, 7(10), 2779-95.

Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.

Black, E. D., & Gutenkunst, R. N. (2003). An introduction to signal extraction in interferometric gravitational wave detectors. American Journal of Physics, 71(4), 365-378.

Abstract:

In the very near future gravitational wave astronomy is expected to become a reality, giving us a completely new tool for exploring the universe around us. We provide an introduction to how interferometric gravitational wave detectors work, suitable for students entering the field and teachers who wish to cover the subject matter in an advanced undergraduate or beginning graduate level course. © 2003 American Association of Physics Teachers.

Xin, M. a., Kelley, J. L., Eilertson, K., Musharoff, S., Degenhardt, J. D., Martins, A. L., Vinar, T., Kosiol, C., Siepel, A., Gutenkunst, R. N., & Bustamante, C. D. (2013). Population Genomic Analysis Reveals a Rich Speciation and Demographic History of Orang-utans (Pongo pygmaeus and Pongo abelii). PLoS ONE, 8(10).

PMID: 24194868;PMCID: PMC3806739;Abstract:

To gain insights into evolutionary forces that have shaped the history of Bornean and Sumatran populations of orang-utans, we compare patterns of variation across more than 11 million single nucleotide polymorphisms found by previous mitochondrial and autosomal genome sequencing of 10 wild-caught orang-utans. Our analysis of the mitochondrial data yields a far more ancient split time between the two populations (∼3.4 million years ago) than estimates based on autosomal data (0.4 million years ago), suggesting a complex speciation process with moderate levels of primarily male migration. We find that the distribution of selection coefficients consistent with the observed frequency spectrum of autosomal non-synonymous polymorphisms in orang-utans is similar to the distribution in humans. Our analysis indicates that 35% of genes have evolved under detectable negative selection. Overall, our findings suggest that purifying natural selection, genetic drift, and a complex demographic history are the dominant drivers of genome evolution for the two orang-utan populations. © 2013 Ma et al.