# TR&D 1 - Generating Differential and Dynamic Networks

> **NIH NIH P41** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $340,217

## Abstract

TR&D 1: GENERATING DIFFERENTIAL AND DYNAMIC NETWORKS – PROJECT SUMMARY
Biological systems are incredibly diverse and dynamic, with hundreds of known cell types and states in a
complex multicellular organism such as human. In contrast, molecular network and pathway maps typically
show a single static view of all interactions for an organism, largely because of cost and technical limitations of
gene and protein interaction mapping technologies. Recently, a range of breakthrough experimental advances
is enabling networks to be mapped at much higher coverage and finer resolution in space and time than
previously possible. New mass spectrometry technology can capture comprehensive changes in protein
expression and phosphorylation at lower cost and higher speed, enabling measurement of differential network
expression information in clinical samples and other contexts. Single-cell genomics, including single-cell
RNA-seq (scRNA-seq), now achieves high resolution measurements of transcriptional state on a per cell basis
over multiple time points. Finally, new high-resolution mass spectrometry workflows enable comprehensive
interactome mapping in a sample at multiple time points and with spatial resolution across a tissue or within
different cellular compartments.
In this TR&D, we develop new computational technologies that take advantage of these qualitatively new data
types to better understand and quantitatively model how networks function in differential biological conditions
and to infer whole-cell dynamic network models. The goals of these technologies are to [​Aim 1] capture the
molecular information flow from targeted perturbations to downstream cellular responses in fully data-driven
predictive dynamic network models; [​Aim 2] functionally characterize mechanisms defining individual cell types
and model the dynamics of developmental lineages; and [​Aim 3] visualize, analyze and predictively model
differential changes in protein interactions across biological contexts, such as disease versus normal. Our
technology research and development aims are motivated by a range of Driving Biomedical Projects (DBPs),
including global mapping of protein interactions (DBPs 1-2,5) and single cell biology focused on understanding
tissue development with engineering applications in regenerative medicine (DBPs 6,7). These aims will be
supported by Technology Partnerships that will help us use gene function information from biological
ontologies and databases (TPs 1,3) and scRNA-seq data portals (TP 5) to characterize differential and
dynamic networks of cells, tissues and disease states.
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## Key facts

- **NIH application ID:** 10145012
- **Project number:** 5P41GM103504-12
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** CHRIS SANDER
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $340,217
- **Award type:** 5
- **Project period:** 2010-09-13 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145012

## Citation

> US National Institutes of Health, RePORTER application 10145012, TR&D 1 - Generating Differential and Dynamic Networks (5P41GM103504-12). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10145012. Licensed CC0.

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