Christopher N. Topp, PhD

Assistant Member
Donald Danforth Plant Science Center
Adjunct Professor

Plant and Microbial Biosciences Program
Computational and Systems Biology Program
Evolution, Ecology and Population Biology Program
Biomedical Informatics and Data Science Program

  • 314-587-1401


  • imaging; plant; genetics; computation; phenotyping; roots; plant x environment

  • Understanding the genetic basis of root growth and environmental plasticity using phenomics

Research Abstract:

The Topp Lab takes a phenomics approach to study crop root growth dynamics in response to environmental stress such as drought and rhizosphere competition, and as a consequence of artificial selection for agronomically important traits such as Nitrogen uptake. Studying roots requires the development of imaging technologies, computational infrastructure, and statistical methods that can capture and analyze morphologically complex networks over time and at high-throughput. Thus the lab combines expertise in imaging (optical, X-ray CT, PET, etc.), computational analysis, and quantitative genetics with molecular biology to understand root growth and physiology.

Selected Publications:

Jiang, N., Floro, E., Bray, A.L., Laws, B., and Topp, C.N. (2018). High-resolution 4D spatiotemporal analysis reveals the contributions of local growth dynamics to contrasting maize root system architectures. IN REVIEW bioRxiv: 381046. doi:
Chambers E.W., Ju T., Letscher D., Li M., Topp C.N., Yan Y. “Some Heuristics for the Homological Simplification Problem” CCCG, Winnipeg, Canada, August 8-10, 2018. Accepted.
Adam L. Bray, Christopher N. Topp. (2018). The Quantitative Genetic Control of Root Architecture in Maize. Plant and Cell Physiology. DOI:
Mao Li, Margaret H Frank, Viktoriya Coneva, Washington Mio, Daniel H Chitwood*&, Christopher N Topp*& (2018). Enhanced genotype-to-phenotype associations for plant shape using the mathematical framework persistent homology. Plant Physiology. DOI:
Delory B.M., Li M., Topp C.N., Lobet G. (2018). archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems. F1000Research, 7:22. DOI: 10.12688/f1000research.13541.1
A. Tabb, K. E. Duncan and C. N. Topp. "Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets", 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018, pp. 586-595. DOI: 10.1109/WACV.2018.00070
Mao Li, Hong An, Ruthie Angelovici, Clement Bagaza, Albert Batushansky, Lynn Clark, Viktoriya Coneva, Michael Donoghue, Erika Edwards, Diego Fajardo, Hui Fang, Margaret Frank, Timothy Gallaher, Sarah Gebken, Theresa A. Hill, Shelly Jansky, Baljinder Kaur, Philip Klahs, Laura L. Klein, Vasu Kuraparthy, Jason Londo, Zoë Migicovsky, Allison Miller, Rebekah Mohn, Sean Myles, Wagner Otoni, J C. Pires, Edmond Rieffer, Sam Schmerler, Elizabeth Spriggs, Christopher N. Topp, Allen Van Deynze, Kuang Zhang, Linglong Zhu, Braden Zink and Daniel H. Chitwood (2018). Topological data analysis as a morphometric method: using persistent homology to demarcate a leaf morphospace. Frontiers in Plant Science. DOI: 10.3389/fpls.2018.00553
Mao Li; Keith Duncan; Christopher N Topp; Daniel H Chitwood (2017). Persistent homology and the branching topologies of plants. American Journal of Botany. 104 (3), 349-353. DOI: 10.3732/ajb.1700046
Agnew, E.*, Bray, A.*, Floro, E.*, Ellis, N., Gierer, J., Lizárraga, C., O`Brien, D., Wiechert, M., Mockler, T.C., Shakoor, N., and Topp, C.N. 2017. Whole-plant manual and image-based phenotyping in controlled environments. Current Protocols in Plant Biology. 2:1-21. DOI: 10.1002/cppb.20044
Christopher N. Topp; Adam L. Bray; Nathanael A. Ellis; Zhengbin Liu (2016). How can we harness quantitative genetic variation in crop root system architecture for agricultural improvement? Journal of Integrative Plant Biology. DOI: 10.1111/jipb.12470

Last Updated: 11/26/2018 9:19:37 AM

X-ray CT scanned maize root crowns demonstrating distinct architectures
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