David Gresham

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Associate Professor

New York University

United States

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About

David Gresham is an Associate Professor at New York University in the United States. His research focuses on gene expression dynamics, particularly in the context of obesity and influenza vaccination, as well as adaptive gene amplification mechanisms in DNA replication. Additionally, he explores microbial communities and their genomic characteristics in various environments, including agricultural landscapes.

Recent Grants

Grant: Close

Regulation of Quiescence in Eukaryotic Cells

Open Date: 2013-09-01

Close Date: 2018-08-31

Grant: Close

The Functional Basis of Genetic Interactions Underlying Quantitative Trait Variation

Open Date: 2013-03-15

Close Date: 2017-02-28

Articles (16)

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2025

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2020

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2020

Collaborators (4)

Gabriel Perron

Associate Professor of Biology

Bard College

UNITED STATES

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Yoav Ram

ISRAEL

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Grace Avecilla

Lecturer, Doctoral Schedule

City College of the University of New York

UNITED STATES

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Andrew Munkacsi

Victoria University of Wellington

NEW ZEALAND

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David Gresham

Research Interests

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About

Recent Grants

Articles (16)

Obese individuals exhibit distinct temporal gene expression dynamics in response to influenza vaccination

Multilevel Gene Expression Changes in Lineages Containing Adaptive Copy Number Variants

Template switching during DNA replication is a prevalent source of adaptive gene amplification

Template switching during DNA replication is a prevalent source of adaptive gene amplification

Prokaryote communities along a source-to-estuary river continuum in the Brazilian Atlantic Forest

Chemical genomic analysis reveals the interplay between iron chelation, zinc homeostasis, and retromer function in the bioactivity of an ethanol adduct of the feijoa fruit–derived ellagitannin vescalagin

Host obesity impacts genetic variation in influenza A viral populations

Copy number variation alters local and global mutational tolerance

Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution

Metagenomes from Soils along an Agricultural Transect in Ulster County, New York

Complex Genomic Rearrangements following Selection in a Glutamine-Limited Medium over Hundreds of Generations

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

Inverted duplicate DNA sequences increase translocation rates through sequencing nanopores resulting in reduced base calling accuracy

Genetic interaction profiles of regulatory kinases differ between environmental conditions and cellular states

A Bright IDEA

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Collaborators (4)

Gabriel Perron

Yoav Ram

Grace Avecilla

Andrew Munkacsi

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