# Learning Conditionally Essential Genetic Networks in the Protein Homeostasis System

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2021 · $190,482

## Abstract

Recent technological advances in massively parallel mutagenesis and deep DNA sequencing are enabling
researchers to discover essential genetic networks in complex cellular systems and under what conditions
those genetic networks are essential. But identifying such conditionally essential networks (CENs) has
been challenging for computational and statistical reasons. The goal of this project is to elucidate and
validate CENs in the protein homeostasis system by developing computationally efficient and statistically
accurate methods for analyzing deep DNA sequencing data from massively parallel mutagenesis
experiments. Dysregulation of the protein homeostasis system leads to imbalances in the proteome which
can cause neurodegenerative pathologies such as Alzheimer's, Huntington's, or Parkinson's disease.
Developing a method for learning CENs in the protein homeostasis system will lead to a better
fundamental understanding of this complex system and will inform combination therapeutics for
neurodegenerative diseases. More broadly, a statistically rigorous tool for analyzing massively parallel
mutagenesis experiments would allow researchers to discover CENs in other complex molecular systems.
The team is well-prepared to complete the specific aims of this project because of their preliminary
nonparametric Bayesian model development, their preliminary experimental data from the the protein
homeostasis system, their experience with developing statistical models for learning from genomic data,
their track record of collaborative research together, and the computational and experimental enviromnent
at their institution. To complete the overall objective, the team will accomplish the following specific
aims: (1) develop and validate a nonparametric Bayesian model for identifying CENs from massively
parallel mutagenesis deep sequencing experiments, and (2) identify and validate protein homeostasis
CENs using transposon sequencing experiments. This project will create new statistical methods, models,
and software for analyzing DNA sequencing data from bulk and purified samples from massively parallel
mutagenesis experiments to discover latent conditionally essential networks. The research aims of this
project will advance understanding of nonparametric Bayesian statistical analysis and protein homeostasis
molecular biology, and those research aims connect directly to broader impacts that advance the full
participation of women and minorities in STEM fields and improve well-being of individuals in society.
In partnership with Girls, Inc of Holyoke, MA, a workshop titled "My DNA, My Medicine" will be
developed to encourage participation of middle and high school students in statistics, computer science,
and genetics.

## Key facts

- **NIH application ID:** 10242088
- **Project number:** 5R01GM135931-03
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** PATRICK FLAHERTY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $190,482
- **Award type:** 5
- **Project period:** 2019-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242088

## Citation

> US National Institutes of Health, RePORTER application 10242088, Learning Conditionally Essential Genetic Networks in the Protein Homeostasis System (5R01GM135931-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10242088. Licensed CC0.

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