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.

Learning perturbation-inducible cell states of novel compounds from observability analysis of transcriptome dynamics

“…Dynamic mode decomposition (DMD) is a time-series dimensionality reduction algorithm that was developed in the fluid dynamics community to extract coherent structures and reconstruct dynamical systems from high-dimensional data [35]. Recently, several works have adapted and applied DMD to biological systems in various contexts [49–53], choosing DMD for its ability to i) reproduce dynamic data over traditionally static methods such as principal component [54] or independent component analysis [55] and ii) represent the dynamics of high-dimensional processes, in our case gene interaction networks, using only a relatively small number of modes.…”

“…several works have adapted and applied DMD to biological sys-207 tems in various contexts [49][50][51][52][53], choosing DMD for its ability ẑt = r i=1…”

Collaborative methods to enhance reproducibility and accelerate discovery

“…Among other advances, these efforts led to faster and more accurate predictions of protein stability, [58][59][60] faster discovery of perovskite crystals, 54,55,61 and more accurate predictions of the impact of synthetic biological circuits on host organisms. 62…”

Investigating and Modeling the Factors that Affect Genetic Circuit Performance

“…Synthetic biology aims to address pressing global challenges including disease diagnosis and treatment [1, 2], bio-fuels production [3, 4, 5], contamination detection [6], bio-manufacturing [7, 8, 9, 4, 10, 11], etc. These are achieved by engineering biological systems with new capabilities, granting cellular control and user-defined performance [12, 4]. The variety of genetic circuit functions include a genetic toggle switch [13], genetic counters [14], low- or high-frequency filters [15, 16], adders [17], sequential asynchronous logic circuits [18], and more.…”