NS 4300 - Proteins, Transcripts, and Metabolism: Big Data in Molecular Nutrition

(PBS-HE) (OPHLS-AG)

Spring. 3 credits. Letter grades only.

Prerequisite: one semester introductory biology lecture (BIOMG 1350, BIOG 1440, or equivalent), biochemistry (NS 3200, BIOMG 3300, or equivalent), and introductory statistics (STSCI 2150, PUBPOL 2100, AEM 2100, or equivalent). Enrollment limited to: senior, junior, and graduate students. Sophomores by permission during the add period. Students are required to bring their own laptop to class.

N. Vacanti.

This course will cover fundamental concepts of big data analysis at an introductory level in the context of gene expression at the mRNA and protein levels with a focus on metabolic regulatory networks. Programming in Python and R will be required, but no prior experience is necessary. Programming in this course will focus methods to parse large data sets and perform informatics analyses.

Outcome 1: Students will be able to: (1) run Python and R on their own systems and (2) access and use programs written in these languages.

Outcome 2: Students will be able to constructively critique potential hypotheses or conclusions based on mRNA or protein abundance patterns.

Outcome 3: Students will be able to: (1) find public data sets and (2) determine the numerical format of public data sets.

Outcome 4: Students will be able to hypothesize the impacts on metabolism of up- or down- regulation of proteins and transcripts corresponding to genes of metabolic pathways.

Outcome 5: Students will be able to propose pairs or groups of genes that regulate aspects of metabolism based on their mRNA or protein abundance patterns.