Model-guided combinatorial optimization of complex synthetic gene networks.

Benjamin Haefliger,Joerg Schreiber,Yaakov Benenson,Nicolas Lapique,Meret Arter

doi:10.15252/msb.20167265

Abstract

Constructing gene circuits that satisfy quantitative performance criteria has been a long‐standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three‐gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high‐throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model‐guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.

Highlights

Optimizing quantitative characteristics of complex artificial gene pathways, networks, and circuits has been a long-standing problem in genetic engineering and synthetic biology
We describe a novel strategy for the development and optimization of complex synthetic gene circuits
The traditional approach to optimizing a genetic component would require repeated cycles of mutagenesis and selection, or prior experimental characterization of a large library of components to evenly cover the parameter range

Summary

Introduction

Optimizing quantitative characteristics of complex artificial gene pathways, networks, and circuits has been a long-standing problem in genetic engineering and synthetic biology. Strategies included rational forward design of genetic components as well as component reshuffling followed by screening (Temme et al, 2012; Zhang et al, 2012; Jeschek et al, 2016) In this case, the optimization task is facilitated by the fact that in metabolic pathway optimization, the statement “the more the better” usually applies, achieved by concurrent optimization of pathways yield (ratio of product to substrate), specific productivity (product/cell per unit time) and volumetric productivity (product per unit volume per unit time). The optimization task is facilitated by the fact that in metabolic pathway optimization, the statement “the more the better” usually applies, achieved by concurrent optimization of pathways yield (ratio of product to substrate), specific productivity (product/cell per unit time) and volumetric productivity (product per unit volume per unit time) Sometimes these parameters can be anticorrelated (Villaverde et al, 2016), in which case the yield would typically take preference over volumetric and specific productivity (Sven Panke, personal communication). Directed evolution was used to improve circuit performance (Haseltine & Arnold, 2007; Schaerli & Isalan, 2013; Benes et al, 2015), but so far experimental results are limited to simple systems (Yokobayashi et al, 2002; Ellefson et al, 2014) or subcircuits (Lou et al, 2010)

Methods

Results

Conclusion