# Agent-based Models to address the Crisis of Reproducibility and Precision Medicine

> **NIH NIH U01** · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · 2020 · $567,434

## Abstract

Summary
This proposal seeks to address fundamental methodological challenges associated with the development and
use of multi-scale models (MSMs), and by extension, can potentially address a current epistemic crisis
affecting biomedical research as a whole. We propose an approach by which a novel perspective of using
MSMs, and specifically agent-based models (ABMs), provides a means of explaining and eventually
addressing the Crisis of Reproducibility, and, in so doing, providing a tractable path towards “real” Precision
Medicine (i.e. right drug, right patient, right time, and how to design such a strategy). We assert that the Crisis
of Reproducibility arises in great part because of the sparseness of “real world” data relative to the space of all
possible biological/pathological phenotypes (in terms of system state and especially system trajectories); this
leads to a discordance between what can be sampled experimentally and the true richness of biological
heterogeneity. We further propose that addressing this discrepancy can be accomplished by approximating the
behavioral landscape of a system using large-scale parameter/trajectory space exploration of ABMs as proxies
for the real world system. This perspective is novel because it emphasizes the distribution and variability of
multi-dimensional spaces/manifolds generated by many trajectories, as opposed to the individual or highly-
selected subset of trajectories that result from classical parameter fitting/calibration. Thus, the validation target
shifts away from high-fidelity/precision fitting (e.g. fitting mean values of a single dataset), which contributes to
the sparseness problem; instead, validation involves recapitulating the breadth of coverage and distribution of
outcomes across many datasets, which embraces heterogeneity. Given the importance of system dynamics
and the non-uniqueness of trajectories to a particular state, this perspective leads to our assertions that true
Precision Medicine can only be achieved after behavioral manifolds are thoroughly characterized, and that,
without an existing mathematical formalism, establishing the direction for developing control strategies can
best be achieved using evolutionary computing and reinforcement learning on simulation data.

## Key facts

- **NIH application ID:** 9788445
- **Project number:** 5U01EB025825-03
- **Recipient organization:** UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
- **Principal Investigator:** Gary An
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $567,434
- **Award type:** 5
- **Project period:** 2018-09-19 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9788445

## Citation

> US National Institutes of Health, RePORTER application 9788445, Agent-based Models to address the Crisis of Reproducibility and Precision Medicine (5U01EB025825-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9788445. Licensed CC0.

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