Scaling up genetic circuit design for cellular computing: advances and prospects

TLDR

Synthetic biology seeks to engineer biological systems for applications such as biomanufacturing, biosensing, and biotherapy, using gene circuits inspired by computer science that implement Boolean logic or graded analog computation and rely on modularity, orthogonality, predictability, and reliability, and early circuits were limited by scarce parts but recent expansion of component libraries has enabled more complex designs. The authors examine challenges in building predictable large‑scale circuits, including modularity, context dependency, and metabolic burden, and review tools and methods to address them. High‑throughput DNA assembly and characterization tools now enable rapid prototyping, systematic in‑situ characterization, and automated design and assembly of circuits, and recent work explores computing paradigms such as circuit memory and distributed computing with cell consortia. These advances accelerate the design of larger gene circuits and deepen our understanding of circuit–host interactions.

Abstract

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.