Event Title

Using Metabolic Network Models to Quantify Selective Pressures on Genes in Brassica rapa

Presenter Information

Jacob Rosenthal, Oberlin College

Location

Science Center A254

Start Date

10-28-2016 2:00 PM

End Date

10-28-2016 3:20 PM

Research Program

Research Experience for Undergraduates (REU) program in Bioinformatics and High-Performance Computing, University of Missouri Informatics Institute

Abstract

Randomly occurring mutations are the driving force of evolution. A single-nucleotide polymorphism (SNP) is a single base position in the genome of a species that is variable between members of that species. Because synonymous SNPs do not change the protein’s amino acid sequence, they are unaffected by selective pressures and the rate at which they accumulate over time tracks the baseline mutation rate. Non-synonymous SNPs, however, do change the structure of the encoded proteins and hence are subject to selection. The ratio of non-synonymous and synonymous SNPs (pN/pS) can therefore be used to gauge the selective pressures acting on a gene. We analyzed transcriptome data from 141 accessions of Brassica rapa and calculated pN/pS for each gene, relative to a reference genome. Next, we integrated these data with a metabolic network model of the plant and looked for correlations between network statistics and pN/pS. Our results show that pN/pS tends to be slightly higher both for more highly-interconnected genes in the network and for cases where multiple genes are involved in catalyzing the same metabolic reaction. Finally, we suggest improvements to our methodology that may lead to more significant results in future studies. The ability to construct accurate models is critical for in silico studies of biological systems, and could facilitate novel paths of inquiry in biology.

Notes

Session I, Panel 3 - Networks & Models

Major

Biology

Project Mentor(s)

J. Chris Pires, Biological Sciences, University of Missouri

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Oct 28th, 2:00 PM Oct 28th, 3:20 PM

Using Metabolic Network Models to Quantify Selective Pressures on Genes in Brassica rapa

Science Center A254

Randomly occurring mutations are the driving force of evolution. A single-nucleotide polymorphism (SNP) is a single base position in the genome of a species that is variable between members of that species. Because synonymous SNPs do not change the protein’s amino acid sequence, they are unaffected by selective pressures and the rate at which they accumulate over time tracks the baseline mutation rate. Non-synonymous SNPs, however, do change the structure of the encoded proteins and hence are subject to selection. The ratio of non-synonymous and synonymous SNPs (pN/pS) can therefore be used to gauge the selective pressures acting on a gene. We analyzed transcriptome data from 141 accessions of Brassica rapa and calculated pN/pS for each gene, relative to a reference genome. Next, we integrated these data with a metabolic network model of the plant and looked for correlations between network statistics and pN/pS. Our results show that pN/pS tends to be slightly higher both for more highly-interconnected genes in the network and for cases where multiple genes are involved in catalyzing the same metabolic reaction. Finally, we suggest improvements to our methodology that may lead to more significant results in future studies. The ability to construct accurate models is critical for in silico studies of biological systems, and could facilitate novel paths of inquiry in biology.