Jonathan Dench

Jonathan Dench
University of Ottawa · Department of Biology

B.Sc. Honours; B.Ed.

About

10
Publications
444
Reads
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35
Citations
Citations since 2016
6 Research Items
25 Citations
20162017201820192020202120220123456
20162017201820192020202120220123456
20162017201820192020202120220123456
20162017201820192020202120220123456
Additional affiliations
September 2015 - present
How R, You?
Position
  • Mentor
Description
  • I founded and run the R-coding, statistics and modelling support group "How R, You?".
September 2014 - present
University of Ottawa
Position
  • Ph. D. Candidate
Description
  • My research focuses of computational methods to identify which factors affect evolutionary trajectory. I use microbial systems, presently Pseudomonas aeruginosa, to asses if there is biological evidence to support computational findings.
October 2009 - May 2010
St. Lawrence River Institute of Environmental Sciences
St. Lawrence River Institute of Environmental Sciences
Position
  • Research Assistant
Description
  • This contract was part of a joint collaboration between the SLRIES, CCIW, and SAWater. I joined the research team aimed to develop novel method for detecting geosmin and MIB producing algae.
Education
September 2014 - September 2019
University of Ottawa
Field of study
  • Biology
September 2011 - May 2012
University of Ottawa
Field of study
  • Education
September 2002 - May 2009
University of Ottawa
Field of study
  • Biology

Publications

Publications (10)
Article
Full-text available
Background: A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association...
Article
Full-text available
The ultimate causes of correlated evolution among sites in a genome remain difficult to teaseapart. To address this problem directly, we performed a high‐throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole genome data from clinical strains of Pseudomonas aeruginosa, before va...
Preprint
Full-text available
Background: Machine learning (ML) encompasses a large set of algorithms that aim at discovering complex patterns between elements within large data sets without any prior assumptions or modeling. However, some scientific disciplines still produce small data sets: in particular, empirical studies that try to link complex phenotypes such as virulence...
Preprint
Full-text available
The ultimate causes of correlated evolution among sites in a genome remain difficult to tease apart. To address this problem directly, we performed a high-throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole genome data from clinical strains of Pseudomonas aeruginosa, before v...
Article
Full-text available
In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular ‘disease causing’ mutations do not have the same outcome in all individuals....
Preprint
Full-text available
In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular ‘disease-causing’ mutations do not have the same outcome in all individuals....
Article
Full-text available
Bacteria excrete costly toxins to defend their ecological niche. The evolution of such antagonistic interactions between individuals is expected to depend on both the social environment and the strength of resource competition. Antagonism is expected to be weak among highly similar genotypes because most individuals are immune to antagonistic agent...
Data
Table S1. Inhibition of clinical isolates by toxins in cell free extract collected from laboratory strains PA01 and PA14 as a function of metabolic similarity (correlation coefficient) between toxin producer and clinical isolate based on BIOLOG profiles. A unimodal non-linear relationship peaking at intermediate metabolic similarity give best fit t...
Data
Figure S1. Inhibition of clinical isolates by toxins in cell free extract collected from laboratory strains PA01 and PA14 as a function of metabolic similarity (correlation coefficient) between toxin producer and clinical isolate based on BIOLOG profiles. A unimodal non-linear relationship peaking at intermediate metabolic similarity give best fit...
Article
Full-text available
Background Bacteria excrete costly toxins to defend their ecological niche. The evolution of such antagonistic interactions between individuals is expected to depend on both the social environment and the strength of resource competition. Antagonism is expected to be weak among highly similar genotypes because most individuals are immune to antagon...

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