# Mechanistic modeling to link scRNAseq data to physiological predictions

> **NIH NIH R21** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $252,854

## Abstract

PROJECT SUMMARY
The overall goal of this R21 application is to develop methods to predict how the presence of multiple subtypes
of ventricular cardiomyocytes influences cardiac function. Recent studies using single-cell RNA sequencing
(scRNAseq) have documented the presence of multiple cardiomyocyte subtypes, but these are described only
in terms of gene expression patterns, and their functional consequences remain unclear. We will use a
combination of approaches in this exploratory project to advance the linkage of gene expression to
physiological function. We will first perform scRNAseq on purified cultures of CMs derived from induced
pluripotent stem cells (iPSCs) to generate primary data about CM subtypes. Simulations with mechanistic
mathematical models will then serve as a computational bridge linking gene expression and function, and
physiological experiments to measure action potentials and intracellular [Ca2+] waveforms will test the
predictions generated by the simulations.
This R21 grant proposal is submitted in response to Notice of Special Interest NOT-HL-21-024, “Bold New
Bioengineering Research for Heart, Lung, Blood and Sleep Disorders and Diseases.” The major advance of
the planned research will be the development of computational tools, based on mechanistic models, that can
link scRNAseq data to experimentally-testable predictions. The work will therefore not only provide new insight
into cardiomyocyte subtypes, but will also deliver strategies and tools that can be applied in other contexts.
This proposal combines the expertise of PI Eric Sobie, in cardiac physiology and mathematical modeling, with
co-Is Ravi Iyengar, in systems biology and omics experiments, and Jens Hansen in computational analysis of
omics data. The combined efforts of the investigators will generate new quantitative data and will yield new
computational methods that can be applied broadly to understand cell subtypes in different contexts.
To achieve the overall project goals, we will combine three complementary approaches, scRNAseq,
mechanistic mathematical modeling, and physiological assays to test modeling predictions, as summarized in
the following general goals:
 1. Use scRNAseq to determine the characteristics and abundances of different subtypes in purified
 cultures of myocytes derived from induced pluripotent stem cells.
 2. Perform simulations with mechanistic mathematical models to predict functional consequences of the
 presence of multiple cardiomyocyte subtypes.
 3. Test predictions of the simulations in experiments measuring action potentials and intracellular calcium
 in purified cultures of cardiomyocytes.

## Key facts

- **NIH application ID:** 10905742
- **Project number:** 1R21HL174051-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** ERIC A SOBIE
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $252,854
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10905742

## Citation

> US National Institutes of Health, RePORTER application 10905742, Mechanistic modeling to link scRNAseq data to physiological predictions (1R21HL174051-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10905742. Licensed CC0.

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