# TRD&1: Model Management and Credibility Infrastructure

> **NIH NIH P41** · UNIVERSITY OF WASHINGTON · 2024 · $241,638

## Abstract

TECHNOLOGY RESEARCH & DEVELOPMENT 1: Project Summary
Computational modeling and simulation continue to be popular in biomedical research. However, doubts
remain as to the reliability or trustworthiness of such efforts. We say that a model is credible for an application
if its predictions are trusted and useful. Although what constitutes a credible model depends on its intended
usage, many current models are not credible. For example, it has been shown that a vast number of published
models cannot even be easily reproduced. That is, recreating the results of a published study is either
impossible or very difficult to achieve. If a model cannot be reproduced, then its credibility is immediately
suspect. Moreover, during the COVID pandemic, there has been much discussion of the credibility and utility
of COVID models, with a great deal focused on the way COVID models were built and tested. With more and
more models finding their way into the clinic, being used by policymakers, and in pharmaceutical companies,
the credibility of biomedical models has become a pressing problem. We therefore feel it is very important that
we begin to consider a systematic approach to assessing the credibility of biomedical models that allows non-
experts (or even researchers outside their particular domain) a means to gauge the credibility of a given
biomedical model. Work over the last decade to improve model reproducibility is a natural first step towards
improving model credibility. The Center for Reproducible Biomedical Modeling has advanced the
reproducibility of biomedical models by developing the biosimulation portal, a service that provides access to
reproducible models across a wide range of biological domains, not just systems biology. However, even though
a model may be reproducible, the question still arises as to how credible is the model.
The goals of this project include: (1) Develop a model management system (MMS) which will be a repository or
simulatable and reusable model parts. Add sophisticated query systems and model decomposition capabilities;
(2) Develop a flexible import/export layer that can accommodate many of the ways that modelers build
models; (3) Create a series of credibility tools that can be used to assess various credibility metrics of models
either held in the MMS or provided by users directly. Technology from TR&D 1 will be used by TR&D 3 as well
as our CPs and SPs.

## Key facts

- **NIH application ID:** 10780531
- **Project number:** 2P41EB023912-06
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** HERBERT M. SAURO
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $241,638
- **Award type:** 2
- **Project period:** 2018-06-13 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10780531

## Citation

> US National Institutes of Health, RePORTER application 10780531, TRD&1: Model Management and Credibility Infrastructure (2P41EB023912-06). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10780531. Licensed CC0.

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