Data-Driven Observability Decomposition with Koopman Operators for Optimization of Output Functions of Nonlinear Systems

TL;DR

This paper introduces a data-driven method using Koopman operators to decompose nonlinear systems into observable components, enabling targeted optimization of output functions, with applications demonstrated in biological gene networks.

Contribution

The paper extends nonlinear observable decomposition to data-informed systems via Koopman operators, proposing a new algorithm for system identification and output-focused observability analysis.

Findings

Successfully identifies key genes driving phenotypes in biological networks.

Demonstrates the effectiveness of Koopman-based observability in complex biological systems.

Provides a new tool for reduced gene network discovery in systems biology.

Abstract

When complex systems with nonlinear dynamics achieve an output performance objective, only a fraction of the state dynamics significantly impacts that output. Those minimal state dynamics can be identified using the differential geometric approach to the observability of nonlinear systems, but the theory is limited to only analytical systems. In this paper, we extend the notion of nonlinear observable decomposition to the more general class of data-informed systems. We employ Koopman operator theory, which encapsulates nonlinear dynamics in linear models, allowing us to bridge the gap between linear and nonlinear observability notions. We propose a new algorithm to learn Koopman operator representations that capture the system dynamics while ensuring that the output performance measure is in the span of its observables. We show that a transformation of this linear, output-inclusive Koopman model renders a new minimum Koopman representation. This representation embodies only the observable portion of the nonlinear observable decomposition of the original system. A prime application of this theory is to identify genes in biological systems that correspond to specific phenotypes, the performance measure. We simulate two biological gene networks and demonstrate that the observability of Koopman operators can successfully identify genes that drive each phenotype. We anticipate our novel system identification tool will effectively discover reduced gene networks that drive complex behaviors in biological systems.