# Optimal reporter placement in sparsely measured genetic networks using the Koopman operator

## TL;DR

This paper introduces a method using the Koopman operator to optimally place sensors in nonlinear biological systems with sparse data, improving the design of genetic reporters.

## Contribution

It develops an algorithm leveraging the Koopman observability gramian for sensor placement in nonlinear systems with sparse temporal data, specifically applied to biological networks.

## Key findings

- Successfully applied to a circadian oscillator simulation.
- Enables optimal sensor placement with sparse biological data.
- Provides a linear framework for nonlinear system observability.

## Abstract

Optimal sensor placement is an important yet unsolved problem in control theory. In biological organisms, genetic activity is often highly nonlinear, making it difficult to design libraries of promoters to act as reporters of the cell state. We make use of the Koopman observability gramian to develop an algorithm for optimal sensor (or reporter) placement for discrete time nonlinear dynamical systems to ease the difficulty of design of the promoter library. This ease is enabled due to the fact that the Koopman operator represents the evolution of a nonlinear system linearly by lifting the states to an infinite-dimensional space of observables. The Koopman framework ideally demands high temporal resolution, but data in biology are often sampled sparsely in time. Therefore we compute what we call the temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator, the latter of which is identified from the sparse data. The optimal placement of sensors then corresponds to maximizing the observability of the fine-grained system. We demonstrate the algorithm on a simulation example of a circadian oscillator.