NS 4300

Proteins, Transcripts, and Metabolism: Big Data in Molecular Nutrition

Course Description

Course information provided by the 2026-2027 Catalog.

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.

Prerequisites BIOMG 1350 or BIOG 1440; NS 3200 or BIOMG 3300; STSCI 2150 or PUBPOL 2100 or AEM 2100; or equivalents.

Enrollment Information Enrollment limited to: seniors, juniors, and graduate students; sophomores by permission of department if space permits.

Distribution Requirements (DLG-AG, OPHLS-AG), (PBS-HE)

Last 4 Terms Offered 2026SP, 2025SP, 2024SP, 2022SP

Learning Outcomes

• Students will be able to: (1) run Python and R on their own systems and (2) access and use programs written in these languages.
• Students will be able to constructively critique potential hypotheses or conclusions based on mRNA or protein abundance patterns.
• Students will be able to: (1) find public data sets and (2) determine the numerical format of public data sets.
• 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.
• Students will be able to propose pairs or groups of genes that regulate aspects of metabolism based on their mRNA or protein abundance patterns.

View Enrollment Information