A data-driven method for quantifying the impact of a genetic circuit on its host
Name
1909.06455.pdf
Description
Submitted version
Size
869.06 KB
Format
Adobe PDF
Checksum (MD5)
9e476b83bb62efcb62aa3f8ca70eba43
Author(s) • • • • • •
Dorfan, Yuval
Espah Borujeni, Amin
Park, YongJin
Saxena, Uma
Gondon, Ben
Voigt, Christopher A.
Yeung, Enoch
Date Issued
October 2019
Journal
BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
Publisher
IEEE
Citation
Hasnain, Aqib et al. “A data-driven method for quantifying the impact of a genetic circuit on its host.” Paper presented at the BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings, Nara, Japan, 17-19 October 2019, IEEE © 2019 The Author(s)
Version
Original manuscript
Abstract
Genetic circuits aredesigned 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/biocas.2019.8919140