# Mathematical modeling of selective vulnerability of genes, cells and network in mouse tauopathy

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $2,138,027

## Abstract

Project Summary
Alzheimer's disease (AD) is a heterogeneous, multifactorial disease that selectively affects certain regions of the
brain, e.g. locus coeruleus, entorhinal and hippocampus. Factors underlying this selective vulnerability (SV)
remain unclear: Why is progression so stereotyped? Why is pathology seen in specific structures at early stages?
What about certain cells makes them susceptible to AD? Current hypotheses have focused on specific features,
e.g. cytoarchitecture, cell morphology, neurotransmitter system or molecular composition. The concept of cellular
vulnerability (“SV-C”) has gained currency due to advances in single cell sequencing and spatial transcriptomics.
Another vulnerability relates to network-based trans-neuronal “prion-like” transmission of pathology, due to
which certain circuits, fiber pathways and regions (network hubs) may become selectively vulnerable (“SV-N”).
This proposal will quantitatively test and validate hypotheses regarding SV-C and SV-N: 1) Protein aggregation,
clearance and transmission on the network underly the spatiotemporal progression of pathology; hence SV of
certain regions (e.g. EC and Hipp) may simply be a result of their location within the network topology.
Alternatively, 2) SV is dictated by distribution and composition of certain neural cell types (e.g. large pyramidal
neurons) that are selectively targeted by AD pathology. Beyond these are competing hypotheses is the possibility
that both factors combine: 3) Pathology is governed by network transmission, but whose local and spread
parameters are mediated by certain cell types (e.g. microglia). Unfortunately, AD research has so far been unable
to fully test between these hypotheses or to identify which of these vulnerabilities are germane. Much of available
bench, animal or human data are descriptive and do not accommodate quantitative models.
We propose to develop network models for SV-C and SV-N and formal statistical models to test them. We
capitalize and build on two enabling technologies that have recently come out of our laboratory: Matrix Inversion
and Subset Selection (MISS) algorithm for creating whole-brain cell type maps; and Network Diffusion Model
(NDM) which mathematically recapitulates transmission of tau along fiber projections. With further development
of these enabling technologies, we will explore SV-C and SV-N in mouse tauopathy data. We will also develop
and test models where cells or genes mediate network vulnerability indirectly. If successful this project could
lead to conclusive evidence for or against each of the identified SV hypotheses. We will explore in future work
the morphological, molecular or electrophysiological properties of short-listed cells, genes, neural pathways and
network epicenters. Concurrently and independently, we will extend the approach to humans, by developing
novel MISS algorithm suitable for human gene expression, and analogously develop mathematical models of
human connectome-driven t...

## Key facts

- **NIH application ID:** 10884976
- **Project number:** 1RF1AG087302-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Ashish Raj
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,138,027
- **Award type:** 1
- **Project period:** 2024-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10884976

## Citation

> US National Institutes of Health, RePORTER application 10884976, Mathematical modeling of selective vulnerability of genes, cells and network in mouse tauopathy (1RF1AG087302-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10884976. Licensed CC0.

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