# LEAPS-MPS: Exploring Properties of Linear Compartmental Models

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · California State University-Stanislaus (CA) · $249,996

## Abstract

This project explores how mathematical diagrams called linear compartmental models (LCMs) can help researchers understand the structure and behavior of systems involving movement or flow, including how diseases spread, how nutrients cycle through ecosystems, or how medications are processed in the body. These models represent systems as interconnected compartments, with arrows indicating how quantities move between them. The investigator aims to identify when meaningful information about these systems, such as flow rates, can be reliably extracted from data, and when different models might appear identical in practice. The project’s goal is to create simple, visual tests for analyzing these characteristics of LCMs, making the process more accessible and less computationally intensive. This project provides undergraduates with opportunities to participate in cutting-edge mathematical research and equips them with background needed to pursue further education. A new colloquium series will further expand students’ exposure to graduate-level research and support their preparation for advanced study.

This project addresses fundamental questions of identifiability and indistinguishability in linear compartmental models, which are widely used to represent dynamical systems with flow. Identifiability, the ability to recover model parameters from input-output data, has been well-studied for single-input, single-output, strongly connected LCMs, and this project extends the scope of 

## Key facts

- **NSF award ID:** 2532620
- **Awardee organization:** California State University-Stanislaus (CA)
- **SAM.gov UEI:** SRT1YX7KJQL4
- **PI:** Cashous W Bortner
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** —
- **Estimated total:** $249,996
- **Funds obligated:** $249,996
- **Transaction type:** Standard Grant
- **Period:** 09/15/2025 → 08/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2532620

## Citation

> US National Science Foundation, Award 2532620, LEAPS-MPS: Exploring Properties of Linear Compartmental Models. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2532620. Licensed CC0.

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