A data-driven method for quantifying the impact of a genetic circuit on its host

TL;DR

This paper introduces a data-driven approach using Koopman operator theory to quantify how genetic circuits affect host cell dynamics, demonstrated through RNAseq data from E. coli with various genetic modifications.

Contribution

The work presents a novel Koopman-based modeling method to measure the impact of synthetic circuits on host transcriptomic dynamics from sparse RNAseq data.

Findings

Koopman models effectively encode circuit interference on host dynamics.

The method quantifies impact levels of different genetic constructs.

Demonstrated on E. coli with various genetic modifications.

Abstract

Genetic circuits are designed to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-throughput RNAseq measurements collected from wild-type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics.