# COVID and Translational Science supercomputer (CATS)

> **NIH NIH S10** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $2,000,000

## Abstract

Abstract:
To enable new kinds of scientific discovery and translation related to the newly emerged COVID-19
pandemic, we are requesting a new high-performance instrument with large shared memory nodes. The
COVID and Translational Science (CATS) supercomputer, will support over $120 million in 50 research
projects for 46 PIs. Due to the urgent need for improved understanding, diagnosis, treatment and
prevention of COVID-19, related projects are very time-sensitive in the near-term. CATS will also be utilized
for a spectrum of translational science applications from molecular dynamic simulations leading towards
drug discovery and multi-scale analyses incorporating omic, electronic medical record data and images.
Even after the pandemic is better controlled, research will continue for years on the post-COVID-19
syndrome due to its complex invasion on multiple organ systems. Other disease areas needing CATS include
psychiatric disease, asthma, Down syndrome, myocarditis, Dengue fever and brain cancer. In total, CATS
will contain with 2,640 Intel cores, 82 TB of memory and 16 petabytes of storage. The enhanced capabilities
of CATS will shorten the time to solution and enable more complex biomedical analyses.

## Key facts

- **NIH application ID:** 10177277
- **Project number:** 1S10OD030463-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Patricia Kovatch
- **Activity code:** S10 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,000,000
- **Award type:** 1
- **Project period:** 2021-06-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10177277

## Citation

> US National Institutes of Health, RePORTER application 10177277, COVID and Translational Science supercomputer (CATS) (1S10OD030463-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10177277. Licensed CC0.

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