Profiles: Victor Hanson-Smith

Victor Hanson-Smith portraitDissertation Title

Error and Uncertainty in Computational Phylogenetics

Dissertation Abstract

The evolutionary history of protein families can be difficult to study because necessary ancestral molecules are often unavailable for direct observation. As an alternative, the field of computational phylogenetics has developed statistical methods to infer the evolutionary relationships among extant molecular sequences and their ancestral sequences. Typically, the methods of computational phylogenetic inference and ancestral sequence reconstruction are combined with other non-computational techniques in a larger analysis pipeline to study the inferred forms and functions of ancient molecules. Two big problems surrounding this analysis pipeline are computational error and statistical uncertainty. In this dissertation, I use simulations and analysis of empirical systems to show that phylogenetic error can be reduced by using an alternative search heuristic. I then use similar methods to reveal the relationship between phylogenetic uncertainty and the accuracy of ancestral sequence reconstruction. Finally, I provide a case-study of a molecular machine in yeast, to demonstrate all stages of the analysis pipeline. This dissertation includes previously published co-authored material.


Joe Thornton (biology) and John Conery (CIS)

Where do you work now?

University of California, San Francisco

I am a post-doctoral fellow in Alexander Johnson's lab at UCSF. I investigate how genomes evolve, using a computational approach in collaboration with several molecular biology labs. I develop algorithms, models, and simulations to study how evolution sculpts populations of genomes in order to produce novel and diverse biological forms. There are two specific projects that I am especially excited about. First, I'm developing a new algorithm to detect significant shifts in protein sequence entropy in order to make strong predictions about historic evolutionary mutations that changed protein functions. Second, I'm designing a synthetic populations of genomes that we can evolve under simulated conditions in order to learn how protein-protein networks are rewired over evolutionary timescales.

What were your best experiences while a graduate student at UO?

My best experience was mentoring small groups of students on bioinformatic research projects. It was exciting to see how much students could accomplish in only ten weeks. Mentoring student groups convinced me that academia is the right place for me.


  • Cleaning the Bunn coffee maker in the colloquium room with Kevin Huck, and then brewing lots of Cafe Mam coffee so we could stay awake and finish our projects.
  • The CIS department ski trips in 2009. The weather was horrible, so we stayed in the lodge and drank Deschutes beer.
  • The ACM programming contest.
  • Andrzej's algorithms course.

Although I love living in California, I miss Eugene -- especially the bike culture and all the organic produce.