A data-driven method for quantifying the impact of a genetic circuit on its host
Author(s)Dorfan, Yuval; Espah Borujeni, Amin; Park, YongJin; Saxena, Uma; Gondon, Ben; Voigt, Christopher A.; Yeung, Enoch; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Department of Biological Engineering
BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
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)