Host-circuit coupling: toward a new framework for genetic circuit design

CHU Pan; ZHU Jingwen; HUANG Wenqi; LIU Chenli; FU Xiongfei

doi:10.12211/2096-8280.2020-046

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Host-circuit coupling: toward a new framework for genetic circuit design

Invited Review | 更新时间：2025-03-20

- Host-circuit coupling: toward a new framework for genetic circuit design
- Synthetic Biology Journal Vol. 2, Issue 1, Pages: 91-105(2021)
- 作者机构：
  
  1.中国科学院深圳先进技术研究院，深圳合成生物学创新研究院，中国科学院定量工程生物学重点实验室，广东省合成基因组学重点实验室，广东深圳　518055
  2.中国科学院大学，北京 100049
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2020-046
  CLC： Q;67
- Received：12 April 2020，
  
  Revised：2020-11-10，
  
  Published：28 February 2021
- 稿件说明：
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储攀, 朱静雯, 黄文琦, 刘陈立, 傅雄飞. 底盘-回路耦合：合成基因回路设计新挑战[J]. 合成生物学, 2021, 2(1): 91-105

CHU Pan, ZHU Jingwen, HUANG Wenqi, LIU Chenli, FU Xiongfei. Host-circuit coupling: toward a new framework for genetic circuit design[J]. Synthetic Biology Journal, 2021, 2(1): 91-105
储攀, 朱静雯, 黄文琦, 刘陈立, 傅雄飞. 底盘-回路耦合：合成基因回路设计新挑战[J]. 合成生物学, 2021, 2(1): 91-105 DOI： 10.12211/2096-8280.2020-046.

CHU Pan, ZHU Jingwen, HUANG Wenqi, LIU Chenli, FU Xiongfei. Host-circuit coupling: toward a new framework for genetic circuit design[J]. Synthetic Biology Journal, 2021, 2(1): 91-105 DOI： 10.12211/2096-8280.2020-046.

摘要

随着合成生物学研究领域的发展以及对人工生命系统设计复杂程度的需求增加，合成基因回路设计呈现复杂化、规模化的发展趋势，导致合成基因回路行为变得难以预测。传统的基因回路设计框架注重回路内部元件作用关系的刻画和元件自身性能的参数调试，通过大量的试错，使回路功能达到次优化。近年来大量的工作表明，基因回路和底盘细胞存在难以避免的耦合：合成回路的基因表达受底盘细胞的资源调配机制调控，而合成基因回路的表达消耗底盘细胞的资源。这种相互作用往往导致底盘细胞生理状态的改变并影响回路功能。因此，将底盘细胞生理参数纳入到基因回路的设计框架中将有望提高基因回路设计的可预测性，提高理性设计能力。面对底盘-回路耦合带来的设计挑战，近年来，涌现出了大量基因回路正交化、模块化的设计思路，成功减弱或规避耦合效应。本文回顾了近年来微生物细胞生理与基因回路的相互作用机制研究的进展；进一步介绍了两类生物物理模型的建立思路，展现物理模型如何帮助我们理解、预测和评估底盘-回路耦合带来的效应；总结了模块化和正交化的设计范式，展现了它们对解决底盘-回路耦合效应的潜力。随着基因“读-改-写”能力提升，以及自动化实验的大规模应用，未来基因线路的设计应当着重于以下几个方面：①高质量元件挖掘；②高质量定量数据刻画；③多维度组学数据的整合，全面评估底盘细胞-基因线路作用程度；④精准的模型预测框架建立。

Abstract

With the rapid development of synthetic biology and increasing demand for complicated artificial life systems， increased complexity and scale have been observed in the design of synthetic gene circuits， resulting in more unpredictable behaviors. Traditionally， applying engineering principles into genetic circuit design employs mainly the bottom-up strategy from individual parts to their potential assembly into biological parts/circuits/systems. This strategy involves best fitting intrinsic parameters of individual parts through considering interactions among genetic parts， which requires extensive trial-and-error to tweak the circuits’ properties. Recent research reveals coupling between synthetic circuits and host cells， which originates， on the one hand， from the global regulation of host cell physiology on the circuit gene expression， and on the other hand， from the competition for and depleting of the cellular resources including gene expression machinery and metabolic pools. This coupling not only alters the physiology of host cells， but also influences the functionality of the circuits. Therefore， incorporating the physiological states of host cells into the framework of genetic circuits design may improve the predictability of the circuits’ behaviors for rational design. Facing challenges from the host-circuits coupling， several strategies have been proposed， including parts orthogonalization and device modularity， which show their potentials in unraveling the tangle of hosts and circuits. In this review， we comment on mechanism underlying the coupling between prokaryotic host cells and genetic circuits that have been widely reported in recent years. Two categories of biophysical models， coarse-grained and whole-cell models， are presented， which help us to understand， predict and evaluate the effect of the host-circuit coupling and the counter-intuitive phenomena as well. Meanwhile， attempts to reduce the coupling effect by orthogonalization and modular design strategies are summarized. With the development of genome read-editing-writing techniques and deployment of automatic high-throughput screening and analysis， we prospect on the genetic circuits design：（1） Excavating of high-quality genetic parts，（2） Quantitative method for characterizing parts， and （3） Integrating multi-level omics data to mine for hidden regulator networks between circuits and hosts， and （4） Developing accurate and robust predicting framework.

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references

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