Forward engineering of synthetic bio-logical AND gates

The field of synthetic biology has produced genetic circuits capable of emulating functional paradigms seen in digital electronic circuits. Examples are bistable switches, oscillators, and logic gates. The present work combines detailed mechanistic-kinetic models and stochastic simulation techniques as well as the techniques of in vivo molecular biology to study the potential of a synthetic, single promoter AND gate. This device is composed of elements of the tet, lac, and λ-phage promoters and is responsive to the commonly used inducers IPTG and aTc, producing GFP as an output signal. The quantitative behavior of the AND gate phenotype is studied both in numero and in vivo as a function of promoter topology. The model is constructed from kinetic data obtained from the literature and yields clearly defined ON/OFF logical behavior at realistic inducer concentrations. These behaviors are matched with observed in vivo data obtained through fluorescence-activated cell sorting. The effect of incomplete repression by weaker LacI repressor is also investigated and quantified. The simulation results, coupled with in vivo data, not only identify important design degrees of freedom, but also provide parameters that can be used to guide future synthetic designs using these common regulatory elements.     

Recent advances and opportunities in synthetic logic gates engineering in living cells

Recently, a number of synthetic biologic gates including AND, OR, NOR, NOT, XOR and NAND have been engineered and characterized in a wide range of hosts. The hope in the emerging synthetic biology community is to construct an inventory of well-characterized parts and install distinct gene and circuit behaviours that are externally controllable. Though the field is still growing and major successes are yet to emerge, the payoffs are predicted to be significant. In this review, we highlight specific examples of logic gates engineering with applications towards fundamental understanding of network complexity and generating a novel socially useful applications.     

The imminent role of protein engineering in synthetic biology

Protein engineering has for decades been a powerful tool in biotechnology for generating vast numbers of useful enzymes for industrial applications. Today, protein engineering has a crucial role in advancing the emerging field of synthetic biology, where metabolic engineering efforts alone are insufficient to maximize the full potential of synthetic biology. This article reviews the advancements in protein engineering techniques for improving biocatalytic properties to optimize engineered pathways in host systems, which are instrumental to achieve high titer production of target molecules. We also discuss the specific means by which protein engineering has improved metabolic engineering efforts and provide our assessment on its potential to continue to advance biology engineering as a whole.     

The emerging age of cell-free synthetic biology

The engineering of and mastery over biological parts has catalyzed the emergence of synthetic biology. This field has grown exponentially in the past decade. As increasingly more applications of synthetic biology are pursued, more challenges are encountered, such as delivering genetic material into cells and optimizing genetic circuits in vivo. An in vitro or cell-free approach to synthetic biology simplifies and avoids many of the pitfalls of in vivo synthetic biology. In this review, we describe some of the innate features that make cell-free systems compelling platforms for synthetic biology and discuss emerging improvements of cell-free technologies. We also select and highlight recent and emerging applications of cell-free synthetic biology.     

A synthetic C2 auxotroph of Pseudomonas putida for evolutionary engineering of alternative sugar catabolic routes

Acetyl-coenzyme A (AcCoA) is a metabolic hub in virtually all living cells, serving as both a key precursor of essential biomass components and a metabolic sink for catabolic pathways for a large variety of substrates. Owing to this dual role, tight growth-production coupling schemes can be implemented around the AcCoA node. Building on this concept, a synthetic C2 auxotrophy was implemented in the platform bacterium Pseudomonas putida through an in silico-informed engineering approach. A growth-coupling strategy, driven by AcCoA demand, allowed for direct selection of an alternative sugar assimilation route—the phosphoketolase (PKT) shunt from bifidobacteria. Adaptive laboratory evolution forced the synthetic P. putida auxotroph to rewire its metabolic network to restore C2 prototrophy via the PKT shunt. Large-scale structural chromosome rearrangements were identified as possible mechanisms for adjusting the network-wide proteome profile, resulting in improved PKT-dependent growth phenotypes. ¹³C-based metabolic flux analysis revealed an even split between the native Entner-Doudoroff pathway and the synthetic PKT bypass for glucose processing, leading to enhanced carbon conservation. These results demonstrate that the P. putida metabolism can be radically rewired to incorporate a synthetic C2 metabolism, creating novel network connectivities and highlighting the importance of unconventional engineering strategies to support efficient microbial production.     

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.     

Integrated rational and evolutionary engineering of genome-reduced Pseudomonas putida strains promotes synthetic formate assimilation

Formate is a promising, water-soluble C1 feedstock for biotechnology that can be efficiently produced from CO₂—but formatotrophy has been engineered in only a few industrially-relevant microbial hosts. We addressed the challenge of expanding the feedstock range of bacterial hosts by adopting Pseudomonas putida as a robust platform for synthetic formate assimilation. Here, the metabolism of a genome-reduced variant of P. putida was radically rewired to establish synthetic auxotrophies that could be functionally complemented by expressing components of the reductive glycine (rGly) pathway. We adopted a modular engineering approach, dividing C1 assimilation in segments composed of both heterologous activities (sourced from Methylobacterium extorquens) and native biochemical reactions. Modular expression of rGly pathway elements enabled growth on formate as carbon source and acetate (predominantly for energy supply), and adaptive laboratory evolution of two lineages of engineered P. putida formatotrophs lead to doubling times of ca. 15 h. We likewise identified emergent metabolic features for assimilation of C1 units in these evolved P. putida populations. Taken together, our results consolidate the landscape of useful microbial platforms that can be implemented for C1-based biotechnological production towards a formate bioeconomy.     

In silico,in vitro,and in vivo machine learning in synthetic biology and metabolic engineering

Among the main learning methods reviewed in this study and used in synthetic biology and metabolic engineering are supervised learning, reinforcement and active learning, and in vitro or in vivo learning.In the context of biosynthesis, supervised machine learning is being exploited to predict biological sequence activities, predict structures and engineer sequences, and optimize culture conditions.Active and reinforcement learning methods use training sets acquired through an iterative process generally involving experimental measurements. They are applied to design, engineer, and optimize metabolic pathways and bioprocesses.The nascent but promising developments with in vitro and in vivo learning comprise molecular circuits performing simple tasks such as pattern recognition and classification.     

Reliability of regulatory networks and its evolution

The problem of reliability of the dynamics in biological regulatory networks is studied in the framework of a generalized Boolean network model with continuous timing and noise. Using well-known artificial genetic networks such as the repressilator, we discuss concepts of reliability of rhythmic attractors. In a simple evolution process we investigate how overall network structure affects the reliability of the dynamics. In the course of the evolution, networks are selected for reliable dynamics. We find that most networks can be easily evolved towards reliable functioning while preserving the original function.     

基于古菌酪氨酰tRNA合成酶非天然氨基酸插入的研究进展

构筑蛋白质的编码信息存在于高度保守的密码子表中,而生物体仅利用20种天然氨基酸,就能排列组合出不同的蛋白质来行使多种生物学功能。通过合成生物学的飞速发展,使得在蛋白质合成中可控地引入非天然氨基酸成为可能。这极大地拓展了蛋白质的结构和功能,并为生物学工具的开发和生物生理过程的研究提供了便利。具有活性基团的非天然氨基酸可以广泛地应用于蛋白质结构研究、蛋白质功能调控以及新型生物材料构建和医药研发等诸多领域。基因密码子拓展技术利用正交翻译系统,通过重新分配密码子改造中心法则,可以在蛋白质的指定位点引入非天然氨基酸。系统地介绍了目前提升密码子拓展技术插入非天然氨基酸效率的方法,包括tRNA以及氨酰tRNA合成酶的各种突变方法和翻译辅助因子的改造。汇总了利用古细菌酪氨酰tRNA合成酶插入的非天然氨基酸和突变位点并总结了密码子拓展技术在生物医药领域的前沿进展。最后讨论了该项技术目前所面临的挑战,如可利用的密码子数量不多、正交翻译系统的种类有限和非天然氨基酸多插效率低下。希望能够帮助研究者建立适合的非天然氨基酸插入方法并推动密码子拓展技术进一步发展。     

Superstability of the yeast cell-cycle dynamics: ensuring causality in the presence of biochemical stochasticity

Gene regulatory dynamics are governed by molecular processes and therefore exhibits an inherent stochasticity. However, for the survival of an organism it is a strict necessity that this intrinsic noise does not prevent robust functioning of the system. It is still an open question how dynamical stability is achieved in biological systems despite the omnipresent fluctuations. In this paper we investigate the cell cycle of the budding yeast Saccharomyces cerevisiae as an example of a well-studied organism. We study a genetic network model of 11 genes that coordinate the cell-cycle dynamics using a modeling framework which generalizes the concept of discrete threshold dynamics. By allowing for fluctuations in the process times, we introduce noise into the model, accounting for the effects of biochemical stochasticity. We study the dynamical attractor of the cell cycle and find a remarkable robustness against fluctuations of this kind. We identify mechanisms that ensure reliability in spite of fluctuations: 'Catcher states' and persistence of activity levels contribute significantly to the stability of the yeast cell cycle despite the inherent stochasticity.     

基因组工程在医学合成生物学中的应用

合成生物学旨在建立一套完整的工程理论和方法,通过设计和组装基本生物学元件,更为有效地实现复杂生物系统的设计,并使其完成可编程的生物学功能。近年来随着可编程基因组元件的出现,特别是CRISPR和CRISPRi技术平台的建立和完善,使得合成生物学进入了一个全新发展的时期。本文重点综述CRISPR等基因组编辑和调控技术,其在构建可编程生物学元件和复杂基因线路的应用以及合成生物学在医学中(称为医学合成生物学)的发展前景。     

Preservation of dynamic properties in qualitative modeling frameworks for gene regulatory networks

Mathematical modeling often helps to provide a systems perspective on gene regulatory networks. In particular, qualitative approaches are useful when detailed kinetic information is lacking. Multiple methods have been developed that implement qualitative information in different ways, e.g., in purely discrete or hybrid discrete/continuous models. In this paper, we compare the discrete asynchronous logical modeling formalism for gene regulatory networks due to R. Thomas with piecewise affine differential equation models. We provide a local characterization of the qualitative dynamics of a piecewise affine differential equation model using the discrete dynamics of a corresponding Thomas model. Based on this result, we investigate the consistency of higher-level dynamical properties such as attractor characteristics and reachability. We show that although the two approaches are based on equivalent information, the resulting qualitative dynamics are different. In particular, the dynamics of the piecewise affine differential equation model is not a simple refinement of the dynamics of the Thomas model