# Dendrite structure: Data-Driven Models to Bridge from Molecules to Morphology

> **NIH NIH R01** · YALE UNIVERSITY · 2021 · $418,750

## Abstract

PROJECT SUMMARY
Dendrite structure: Data-Driven Models to Bridge from Molecules to Morphology
The highly branched structures of dendritic arbors enable the extraordinary connectivity and
information-processing power of the nervous system. Altered dendritic morphologies are often
associated with neurological conditions and diseases. While we know many molecular
components underlying dendritic growth and structure through genetic and cell biological studies,
we still do not understand how molecular interactions generate dendritic arbors, which are
thousands to millions of times larger than the constituent molecules.
 The overall goal of this application is to develop data-driven models that predict,
quantitatively, dendritic growth in Drosophila Class IV da neurons. These cells are chosen
because their dendrites can be imaged with outstanding spatial and temporal resolution, and the
genetic tools in flies facilitate molecular manipulations. Our central hypothesis is that the growing
and shrinking tips of dendrites constitute an intermediate level of organization between molecules
and morphology. This allows us to divide the large gap between genotype and phenotype into
two parts: the first is from molecules to dendrite tips, and the second is from dendrite tips to
morphology. The second part will be bridged using models.
 To attain our overall objective, we will pursue the following three specific aims: (i) We will
formulate kinetic rules underlying the dynamics of dendritic tips using high-resolution, live-cell
imaging to measure the birth and death of tips through branching and retraction, and the transition
rates between different velocity states. (ii) We will develop multi-scale mathematical models that
take as input the data such as obtained in Aim 1 and predict morphologies, which will be compared
to real dendritic arbors. (iii) We will genetically perturb cytoskeletal proteins and use the models
to test whether the effects on tip dynamics account for the altered dendrite structures. The
expected outcome is mechanistic understanding of how morphological phenotypes emerge from
molecular processes occurring at the level of dendrite tips. These results will positively impact the
field by bridging genotype to phenotype and by providing insight into the pathophysiology of
genetic disorders that affect neuronal structures.

## Key facts

- **NIH application ID:** 10145937
- **Project number:** 1R01NS118884-01A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Jonathon Howard
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $418,750
- **Award type:** 1
- **Project period:** 2020-12-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145937

## Citation

> US National Institutes of Health, RePORTER application 10145937, Dendrite structure: Data-Driven Models to Bridge from Molecules to Morphology (1R01NS118884-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10145937. Licensed CC0.

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