# Core (Grandi)

> **NIH NIH P01** · STANFORD UNIVERSITY · 2021 · $386,560

## Abstract

CORE B: IN SILICO MODELING Director: Eleonora Grandi Ph.D. (UC Davis)
PROJECT SUMMARY: The computational core will develop and apply multi-scale mechanistic models,
simulation and statistical approaches as predictive tools to inform, interpret, and extend experimental
observations in Projects 1, 2 and 3. Our quantitative models will span the range of physical scales and the
diverse biological functions needed to mechanistically link HF-induced remodeled electrophysiology to patient-
specific clinical phenotype. Namely, we will develop physiologically detailed electrical, chemical, and mechanical
models from single channel to whole-organ scales. Quantitative computational approaches will enhance our
mechanistic understanding of complex and non-linear feedback systems involved in arrhythmia generation and
maintenance. The tools will also be applied for in silico screening and prediction of drug effects on varied genetic
backgrounds to predict patient pharmacological responses. Computational population-based approaches will be
constructed to determine the factors influencing drug efficacy or failure in a specific patient or subgroup of HF
patients and inherited DCM patients. CORE B will also provide translation of findings in human pluripotent stem
cell derived cardiomyocytes (iPSC-CMs) and rabbit ventricular myocytes to adult human ventricular myocytes
via in silico modeling and simulation.

## Key facts

- **NIH application ID:** 10249145
- **Project number:** 5P01HL141084-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Eleonora Grandi
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $386,560
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249145

## Citation

> US National Institutes of Health, RePORTER application 10249145, Core (Grandi) (5P01HL141084-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10249145. Licensed CC0.

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