# Quantitative regulatory genomics: networks, cis-regulatory codes, and phenotypic variation

> **NIH NIH R35** · GEORGIA INSTITUTE OF TECHNOLOGY · 2023 · $356,979

## Abstract

How do changes in DNA sequence impact organismal properties? This is a central question of modern 
biology, and insights into it can help us understand, among other things, why patients respond 
differently to the same treatment, or why some species exhibit behavioral properties not seen in 
other species. A major hurdle in solving this ‘genotype-to-phenotype’ problem is our poor knowledge 
of gene regulatory mechanisms underlying phenotypes and cellular processes, and how those 
mechanisms are encoded in DNA. It also leads to severe difficulties in prioritizing 
phenotype-linked non-coding variants (polymorphisms) for further investigation. Driven by these 
challenges, my lab seeks to develop quantitative frameworks for describing and 
discovering transcriptional regulatory mechanisms. We have made significant progress towards this 
goal in two main directions: (1) We have developed detailed biophysical models of the 
cis-regulatory encoding of gene expression. Using these models we have shown how the regulatory 
function of transcription factor (TF) binding sites depends on their sequence and DNA shape, as 
well as their ‘trans-context’, e.g., cellular concentrations of regulators, and 
‘cis-context’, e.g., proximity to other TF binding sites and chromatin states. (2) We have 
devised statistical models to discover TF-gene interactions from transcriptomic data, as well as 
other types of ‘omics’ data if available. Working closely with biologists, we have applied these 
models to understand phenotypes such as cytotoxic drug response in cell lines, behavioral response 
to social encounters, and embryonic development. Building on the strong foundations of our 
past work, I propose to establish a research program that studies transcriptional regulation 
holistically at the cis- and trans- levels. Our new pursuits will include: (1) use of our 
computational, sequence-level models to describe two data-rich mammalian regulatory programs, an 
experimental collaboration to dissect the cis-regulatory logic of a key inflammation gene using 
massively parallel reporter assays, and major advances in our modeling techniques; (2) new 
machine learning methods for reconstructing networks of TF-gene interactions that explain 
phenotypic differences, integration of cis- and trans-regulatory evidence from multi-omics 
data, and collaborations to apply these methods in cancer pharmacogenomics and behavioral 
neurogenomics; (3) a new probabilistic framework to combine traditional statistical scores of a 
non-coding variant with quantitative predictions of its regulatory impact based on the 
above-mentioned techniques. Explorations of new forms of synergy among these related goals of 
network reconstruction, cis-regulatory sequence modeling and variant interpretation will be woven 
throughout our research program.

## Key facts

- **NIH application ID:** 10690568
- **Project number:** 5R35GM131819-06
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Saurabh Sinha
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $356,979
- **Award type:** 5
- **Project period:** 2019-09-20 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10690568

## Citation

> US National Institutes of Health, RePORTER application 10690568, Quantitative regulatory genomics: networks, cis-regulatory codes, and phenotypic variation (5R35GM131819-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10690568. Licensed CC0.

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