Data-driven network models for genetic circuits from time-series data with incomplete measurements.

Enoch Yeung,Ye Yuan,Jongmin Kim,Jorge Gonçalves,Richard M Murray

doi:10.1098/rsif.2021.0413

Abstract

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.

Highlights

Synthetic gene networks fulfil diverse roles in realizing circuit logic [1] and timing in living organisms [2], ranging from single-input inverters [3,4] to combinatorial input logic gates [5,6], reduction in DNA synthesis and sequencing costs have made it possible to build increasingly complex genetic circuits with tens to hundreds of components
The expansion of CRISPR-based methods for genome editing [11,12] has led to new network control problems in systems biology, e.g. design of minimal genomes [13], reprogramming regulatory networks for phenotype control [14], and fine-tuned optimization of metabolic pathways
We introduce a class of mesoscopic network reconstruction models with adaptable resolution, commensurate with the depth or coverage of the circuit states available from fluorimetric, spectometry-based, or sequencing based measurements

Summary

Introduction

Synthetic gene networks fulfil diverse roles in realizing circuit logic [1] and timing in living organisms [2], ranging from single-input inverters [3,4] to combinatorial input logic gates [5,6], reduction in DNA synthesis and sequencing costs have made it possible to build increasingly complex genetic circuits with tens to hundreds of components. Prohibitive sampling and library preparation costs make obtaining highly time-resolved omics measurements hard. This makes it difficult to infer dynamic network activity at the scale of whole cell models [18] without extensive experimental investment

Objectives

Methods

Conclusion