A data-driven method for quantifying the impact of a genetic circuit on its host
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.
Cite
@article{arxiv.1909.06455,
title = {A data-driven method for quantifying the impact of a genetic circuit on its host},
author = {Aqib Hasnain and Subhrajit Sinha and Yuval Dorfan and Amin Espah Borujeni and Yongjin Park and Paul Maschhoff and Uma Saxena and Joshua Urrutia and Niall Gaffney and Diveena Becker and Atsede Siba and Narendra Maheshri and Ben Gordon and Chris Voigt and Enoch Yeung},
journal= {arXiv preprint arXiv:1909.06455},
year = {2019}
}
Comments
4 pages Accepted in 2019 IEEE Biomedical Circuits and Systems Conference