Engineering and applications of genetic circuits

In the field of synthetic biology, recent genetic engineering efforts have enabled the construction of novel genetic circuits with diverse functionalities and unique activation mechanisms. Because of these advances, artificial genetic networks are becoming increasingly complex, and are demonstrating more robust behaviors with reduced crosstalk between defined modules. These properties have allowed for the identification of a growing set of design principles that govern genetic networks, and led to an increased number of applications for genetic circuits in the fields of metabolic engineering and biomedical engineering. Such progress indicates that synthetic biology is rapidly evolving into an integrated engineering practice that uses rational and combinatorial design of synthetic gene networks to solve complex problems in biology, medicine, and human health.     

Toward scalable parts families for predictable design of biological circuits

Our current ability to engineer biological circuits is hindered by design cycles that are costly in terms of time and money, with constructs failing to operate as desired, or evolving away from the desired function once deployed. Synthetic biologists seek to understand biological design principles and use them to create technologies that increase the efficiency of the genetic engineering design cycle. Central to the approach is the creation of biological parts--encapsulated functions that can be composited together to create new pathways with predictable behaviors. We define five desirable characteristics of biological parts--independence, reliability, tunability, orthogonality and composability, and review studies of small natural and synthetic biological circuits that provide insights into each of these characteristics. We propose that the creation of appropriate sets of families of parts with these properties is a prerequisite for efficient, predictable engineering of new function in cells and will enable a large increase in the sophistication of genetic engineering applications.     

哺乳动物合成生物学在生物医学领域的研究进展

虽然合成生物学还处于早期研究阶段,但最近十年,该领域取得了非常显著的研究进展。合成生物学是以工程学思想为基础,通过人工设计、改造基因线路,从而赋予细胞或生物体新的功能,现已广泛应用于各个领域。随着人们对基因线路设计的深入研究,使得合成生物学研究走向临床应用成为可能。本文将围绕哺乳动物合成生物学在疾病治疗方面的研究进展,介绍基因线路的设计思路和方法、不同诱导因子调控的开环式基因线路以及用于疾病诊疗的闭环式基因环路在生物医学领域的应用。最后对合成生物学走向临床治疗的应用前景和挑战进行展望。     

Synthetic circuits,devices and modules

The aim of synthetic biology is to design artificial biological systems for novel applications. From an engineering perspective, construction of biological systems of defined functionality in a hierarchical way is fundamental to this emerging field. Here, we highlight some current advances on design of several basic building blocks in synthetic biology including the artificial gene control elements, synthetic circuits and their assemblies into devices and modules. Such engineered basic building blocks largely expand the synthetic toolbox and contribute to our understanding of the underlying design principles of living cells.     

Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors

The engineering of synthetic gene networks has mostly relied on the assembly of few characterized regulatory elements using rational design principles. It is of outmost importance to analyze the scalability and limits of such a design workflow. To analyze the design capabilities of libraries of regulatory elements, we have developed the first automated design approach that combines such elements to search the genotype space associated to a given phenotypic behavior. Herein, we calculated the designability of dynamical functions obtained from circuits assembled with a given genetic library. By designing circuits working as amplitude filters, pulse counters and oscillators, we could infer new mechanisms for such behaviors. We also highlighted the hierarchical design and the optimization of the interface between devices. We dissected the functional diversity of a constrained library and we found that even such libraries can provide a rich variety of behaviors. We also found that intrinsic noise slightly reduces the designability of digital circuits, but it increases the designability of oscillators. Finally, we analyzed the robust design as a strategy to counteract the evolvability and noise in gene expression of the engineered circuits within a cellular background, obtaining mechanisms for robustness through non-linear negative feedback loops.     

Development of biomaterial scaffold for nerve tissue engineering: Biomaterial mediated neural regeneration

Neural tissue repair and regeneration strategies have received a great deal of attention because it directly affects the quality of the patient's life. There are many scientific challenges to regenerate nerve while using conventional autologous nerve grafts and from the newly developed therapeutic strategies for the reconstruction of damaged nerves. Recent advancements in nerve regeneration have involved the application of tissue engineering principles and this has evolved a new perspective to neural therapy. The success of neural tissue engineering is mainly based on the regulation of cell behavior and tissue progression through the development of a synthetic scaffold that is analogous to the natural extracellular matrix and can support three-dimensional cell cultures. As the natural extracellular matrix provides an ideal environment for topographical, electrical and chemical cues to the adhesion and proliferation of neural cells, there exists a need to develop a synthetic scaffold that would be biocompatible, immunologically inert, conducting, biodegradable, and infection-resistant biomaterial to support neurite outgrowth. This review outlines the rationale for effective neural tissue engineering through the use of suitable biomaterials and scaffolding techniques for fabrication of a construct that would allow the neurons to adhere, proliferate and eventually form nerves.     

Rationally engineering natural protein assemblies in nanobiotechnology

Multimeric protein assemblies are essential components in viruses, bacteria, eukaryotic cells, and organisms where they act as cytoskeletal scaffold, storage containers, or for directional transport. The bottom-up structures can be exploited in nanobiotechnology by harnessing their built-in properties and combining them with new functional modules. This review summarizes the design principles of natural protein assemblies, highlights recent progress in their structural elucidation, and shows how rational engineering can create new biomaterials for applications in vaccine development, biocatalysis, materials science, and synthetic biology.     

合成生物学发展现状与前景

合成生物学是综合了科学与工程的一个崭新的生物学研究领域,为生命现象及其运动规律的解析提供了一种采用“白下而上”合成策略的正向工程学的研究思路和方法手段,在经济和社会发展中具有巨大的应用开发潜力。近年来,DNA合成与系统生物学技术的发展使生命系统复杂基因回路的设计、合成与组装逐步成为可能,并应用于生物基化学品、生物燃料、医药中间体、保健产品的生产和环境保护等领域。但是,合成生物学的研究仍然面临科学、技术和伦理的挑战,只有积极地应对这些问题,在加大研究开发支持力度的同时,做好必要的风险监管,才能真正把握合成生物学发展带来的历史机遇。     

Modular design of artificial tissue homeostasis: robust control through synthetic cellular heterogeneity

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.     

Programming microbial population dynamics by engineered cell-cell communication

A major aim of synthetic biology is to program novel cellular behavior using engineered gene circuits. Early endeavors focused on building simple circuits that fulfill simple functions, such as logic gates, bistable toggle switches, and oscillators. These gene circuits have primarily focused on single-cell behaviors since they operate intracellularly. Thus, they are often susceptible to cell-cell variations due to stochastic gene expression. Cell-cell communication offers an efficient strategy to coordinate cellular behavior at the population level. To this end, we review recent advances in engineering cell-cell communication to achieve reliable population dynamics, spanning from communication within single species to multispecies, from one-way sender-receiver communication to two-way communication in synthetic microbial ecosystems. These engineered systems serve as well-defined model systems to better understand design principles of their naturally occurring counterparts and to facilitate novel biotechnology applications.     

Modular engineering of cellular signaling proteins and networks

Living cells respond to their environment using networks of signaling molecules that act as sensors, information processors, and actuators. These signaling systems are highly modular at both the molecular and network scales, and much evidence suggests that evolution has harnessed this modularity to rewire and generate new physiological behaviors. Conversely, we are now finding that, following nature's example, signaling modules can be recombined to form synthetic tools for monitoring, interrogating, and controlling the behavior of cells. Here we highlight recent progress in the modular design of synthetic receptors, optogenetic switches, and phospho-regulated proteins and circuits, and discuss the expanding role of combinatorial design in the engineering of cellular signaling proteins and networks.     

A perspective of synthetic biology: assembling building blocks for novel functions

Synthetic biology is a recently emerging field that applies engineering formalisms to design and construct new biological parts, devices, and systems for novel functions or life forms that do not exist in nature. Synthetic biology relies on and shares tools from genetic engineering, bioengineering, systems biology and many other engineering disciplines. It is also different from these subjects, in both insights and approach. Applications of synthetic biology have great potential for novel contributions to established fields and for offering opportunities to answer fundamentally new biological questions. This article does not aim at a thorough survey of the literature and detailing progress in all different directions. Instead, it is intended to communicate a way of thinking for synthetic biology in which basic functional elements are defined and assembled into living systems or biomaterials with new properties and behaviors. Four major application areas with a common theme are discussed and a procedure (or "protocol") for a standard synthetic biology work is suggested.     

Synthetic genetic circuits for programmable biological functionalities

Living organisms evolve complex genetic networks to interact with the environment. Due to the rapid development of synthetic biology, various modularized genetic parts and units have been identified from these networks. They have been employed to construct synthetic genetic circuits, including toggle switches, oscillators, feedback loops and Boolean logic gates. Building on these circuits, complex genetic machines with capabilities in programmable decision-making could be created. Consequently, these accomplishments have led to novel applications, such as dynamic and autonomous modulation of metabolic networks, directed evolution of biological units, remote and targeted diagnostics and therapies, as well as biological containment methods to prevent release of engineered microorganisms and genetic materials. Herein, we outline the principles in genetic circuit design that have initiated a new chapter in transforming concepts to realistic applications. The features of modularized building blocks and circuit architecture that facilitate realization of circuits for a variety of novel applications are discussed. Furthermore, recent advances and challenges in employing genetic circuits to impart microorganisms with distinct and programmable functionalities are highlighted. We envision that this review gives new insights into the design of synthetic genetic circuits and offers a guideline for the implementation of different circuits in various aspects of biotechnology and bioengineering.     

噬菌体重组酶系统在合成生物学中的应用*

合成生物学技术采用工程化设计理念,对生物体进行有目标的设计、改造乃至重新合成,对重塑非自然功能的“人造生命”具有重要意义。噬菌体重组系统具有高效、精确和广谱适用性等特点,在基因工程、代谢工程以及生物治疗等合成生物学领域得到了广泛的应用。从基因电路、体内遗传改造和体外重组等方面全面阐述了噬菌体重组系统在合成生物学研究的现状及热点,对当前该系统的局限性进行了探讨,并就未来的研究和发展趋势进行了展望。     

合成生物学的医学应用

合成生物学旨在基于工程学原理,通过人工合成生物调控元件、模块和基因调控网络等对细胞进行设计和改造,以实现细胞和生命体的定向演化。在医学研究中,合成生物学主要采用人工设计合成治疗性的基因回路,制备工程化细胞植入体内,纠正机体已发生缺陷的生物调控元件,以达到治疗疾病的目的。本文对合成生物学的兴起、发展及其在医学中的应用和研究进展进行了综述。     

Optimization of industrial microorganisms: recent advances in synthetic dynamic regulators

Production of biochemicals by industrial fermentation using microorganisms requires maintaining cellular production capacity, because maximal productivity is economically important. High-productivity microbial strains can be developed using static engineering, but these may not maintain maximal productivity throughout the culture period as culture conditions and cell states change dynamically. Additionally, economic reasons limit heterologous protein expression using inducible promoters to prevent metabolic burden for commodity chemical and biofuel production. Recently, synthetic and systems biology has been used to design genetic circuits, precisely controlling gene expression or influencing genetic behavior toward a desired phenotype. Development of dynamic regulators can maintain cellular phenotype in a maximum production state in response to factors including cell concentration, oxygen, temperature, pH, and metabolites. Herein, we introduce dynamic regulators of industrial microorganism optimization and discuss metabolic flux fine control by dynamic regulators in response to metabolites or extracellular stimuli, robust production systems, and auto-induction systems using quorum sensing.     

Synthetic robust perfect adaptation achieved by negative feedback coupling with linear weak positive feedback

Unlike their natural counterparts, synthetic genetic circuits are usually fragile in the face of environmental perturbations and genetic mutations. Several theoretical robust genetic circuits have been designed, but their performance under real-world conditions has not yet been carefully evaluated. Here, we designed and synthesized a new robust perfect adaptation circuit composed of two-node negative feedback coupling with linear positive feedback on the buffer node. As a key feature, the linear positive feedback was fine-tuned to evaluate its necessity. We found that the desired function was robustly achieved when genetic parameters were varied by systematically perturbing all interacting parts within the topology, and the necessity of the completeness of the topological structures was evaluated by destroying key circuit features. Furthermore, different environmental perturbances were imposed onto the circuit by changing growth rates, carbon metabolic strategies and even chassis cells, and the designed perfect adaptation function was still achieved under all conditions. The successful design of a robust perfect adaptation circuit indicated that the top-down design strategy is capable of predictably guiding bottom-up engineering for robust genetic circuits. This robust adaptation circuit could be integrated as a motif into more complex circuits to robustly implement more sophisticated and critical biological functions.     

From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.