# Gene Regulatory Networks for Development

> **NIH NIH R25** · MARINE BIOLOGICAL LABORATORY · 2021 · $110,519

## Abstract

Project Summary
Gene regulatory network (GRN) science combines experimental approaches at any scale, from single gene to
genome wide, together with computational modeling approaches and incorporates the insights gained from
these analyses into a framework used to explain the causality of developmental processes and animal
evolution. Expanding our understanding of GRNs has been identified as one of the research priorities of the
Developmental Biology branch of the National Institute of Child Health and Human Development. The key
objective of this short course on GRNs is to help students develop a conceptual understanding of
developmental control mechanisms that serves as a basis to formulate research questions and hypotheses,
and to learn how to apply diverse experimental and computational approaches to solve them. Students will
take away from this course a sense for how systems level explanations can be obtained for developmental
processes in any biological context using a variety of experimental and computational techniques. This will be
accomplished through an intensive series of lectures, discussions, and hands-on workshops using
computational methods to analyze big data from a large number of studies in order to construct
comprehensible models for gene networks that guide development.

## Key facts

- **NIH application ID:** 10150475
- **Project number:** 5R25HD094665-04
- **Recipient organization:** MARINE BIOLOGICAL LABORATORY
- **Principal Investigator:** Linda E Hyman
- **Activity code:** R25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $110,519
- **Award type:** 5
- **Project period:** 2018-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10150475

## Citation

> US National Institutes of Health, RePORTER application 10150475, Gene Regulatory Networks for Development (5R25HD094665-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10150475. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*