  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.
