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

• Main Authors: Dorfan, Yuval (Author), Espah Borujeni, Amin (Author), Park, YongJin (Author), Saxena, Uma (Author), Gondon, Ben (Author), Voigt, Christopher A. (Author), Yeung, Enoch (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor)
• Format: Article
• Language: English
• Published: IEEE, 2020-09-08T17:14:07Z.
• Subjects: Article
• Online Access: Get fulltext

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.
United States. Defense Advanced Research Projects Agency (Grants FA8750-17-C-0229, HR001117C0092, HR001117C0094, DEAC0576RL01830)