Engineering and control of biological systems: A new way to tackle complex diseases

The ongoing merge between engineering and biology has contributed to the emerging field of synthetic biology. The defining features of this new discipline are abstraction and standardisation of biological parts, decoupling between parts to prevent undesired cross-talking, and the application of quantitative modelling of synthetic genetic circuits in order to guide their design. Most of the efforts in the field of synthetic biology in the last decade have been devoted to the design and development of functional gene circuits in prokaryotes and unicellular eukaryotes. Researchers have used synthetic biology not only to engineer new functions in the cell, but also to build simpler models of endogenous gene regulatory networks to gain knowledge of the "rules" governing their wiring diagram. However, the need for innovative approaches to study and modify complex signalling and regulatory networks in mammalian cells and multicellular organisms has prompted advances of synthetic biology also in these species, thus contributing to develop innovative ways to tackle human diseases. In this work, we will review the latest progress in synthetic biology and the most significant developments achieved so far, both in unicellular and multicellular organisms, with emphasis on human health.     

Synthetic biology for plant genetic engineering and molecular farming

Many efforts have been put into engineering plants to improve crop yields and stress tolerance and boost the bioproduction of valuable molecules. Yet, our capabilities are still limited due to the lack of well-characterized genetic building blocks and resources for precise manipulation and given the inherently challenging properties of plant tissues. Advancements in plant synthetic biology can overcome these bottlenecks and release the full potential of engineered plants. In this review, we first discuss the recently developed plant synthetic elements from single parts to advanced circuits, software, and hardware tools expediting the engineering cycle. Next, we survey the advancements in plant biotechnology enabled by these recent resources. We conclude the review with outstanding challenges and future directions of plant synthetic biology.     

A guide to web tools to prioritize candidate genes

Finding the most promising genes among large lists of candidate genes has been defined as the gene prioritization problem. It is a recurrent problem in genetics in which genetic conditions are reported to be associated with chromosomal regions. In the last decade, several different computational approaches have been developed to tackle this challenging task. In this study, we review 19 computational solutions for human gene prioritization that are freely accessible as web tools and illustrate their differences. We summarize the various biological problems to which they have been successfully applied. Ultimately, we describe several research directions that could increase the quality and applicability of the tools. In addition we developed a website (http://www.esat.kuleuven.be/gpp) containing detailed information about these and other tools, which is regularly updated. This review and the associated website constitute together a guide to help users select a gene prioritization strategy that suits best their needs.     

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。     

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。     

Understanding the brain by controlling neural activity

Causal methods to interrogate brain function have been employed since the advent of modern neuroscience in the nineteenth century. Initially, randomly placed electrodes and stimulation of parts of the living brain were used to localize specific functions to these areas. Recent technical developments have rejuvenated this approach by providing more precise tools to dissect the neural circuits underlying behaviour, perception and cognition. Carefully controlled behavioural experiments have been combined with electrical devices, targeted genetically encoded tools and neurochemical approaches to manipulate information processing in the brain. The ability to control brain activity in these ways not only deepens our understanding of brain function but also provides new avenues for clinical intervention, particularly in conditions where brain processing has gone awry.     

Bimodal and hysteretic expression in mammalian cells from a synthetic gene circuit

In order to establish cells and organisms with predictable properties, synthetic biology makes use of controllable, synthetic genetic devices. These devices are used to replace or to interfere with natural pathways. Alternatively, they may be interlinked with endogenous pathways to create artificial networks of higher complexity. While these approaches have been already successful in prokaryotes and lower eukaryotes, the implementation of such synthetic cassettes in mammalian systems and even animals is still a major obstacle. This is mainly due to the lack of methods that reliably and efficiently transduce synthetic modules without compromising their regulation properties. To pave the way for implementation of synthetic regulation modules in mammalian systems we utilized lentiviral transduction of synthetic modules. A synthetic positive feedback loop, based on the Tetracycline regulation system was implemented in a lentiviral vector system and stably integrated in mammalian cells. This gene regulation circuit yields a bimodal expression response. Based on experimental data a mathematical model based on stochasticity was developed which matched and described the experimental findings. Modelling predicted a hysteretic expression response which was verified experimentally. Thereby supporting the idea that the system is driven by stochasticity. The results presented here highlight that the combination of three independent tools/methodologies facilitate the reliable installation of synthetic gene circuits with predictable expression characteristics in mammalian cells and organisms.     

Automatic design of digital synthetic gene circuits

De novo computational design of synthetic gene circuits that achieve well-defined target functions is a hard task. Existing, brute-force approaches run optimization algorithms on the structure and on the kinetic parameter values of the network. However, more direct rational methods for automatic circuit design are lacking. Focusing on digital synthetic gene circuits, we developed a methodology and a corresponding tool for in silico automatic design. For a given truth table that specifies a circuit's input-output relations, our algorithm generates and ranks several possible circuit schemes without the need for any optimization. Logic behavior is reproduced by the action of regulatory factors and chemicals on the promoters and on the ribosome binding sites of biological Boolean gates. Simulations of circuits with up to four inputs show a faithful and unequivocal truth table representation, even under parametric perturbations and stochastic noise. A comparison with already implemented circuits, in addition, reveals the potential for simpler designs with the same function. Therefore, we expect the method to help both in devising new circuits and in simplifying existing solutions.     

Towards synthetic microbial consortia for bioprocessing

The use of microbial consortia for bioprocessing has been limited by our ability to reliably control community composition and function simultaneously. Recent advances in synthetic biology have enabled population-level coordination and control of ecosystem stability and dynamics. Further, new experimental and computational tools for screening and predicting community behavior have also been developed. The integration of synthetic biology with metabolic engineering at the community level is vital to our ability to apply system-level approaches to building and optimizing synthetic consortia for bioprocessing applications. This review details new methods, tools and opportunities that together have the potential to enable a new paradigm of bioprocessing using synthetic microbial consortia.     

A revolutionary tool: CRISPR technology plays an important role in construction of intelligentized gene circuits

With the development of synthetic biology, synthetic gene circuits have shown great applied potential in medicine, biology, and as commodity chemicals. An ultimate challenge in the construction of gene circuits is the lack of effective, programmable, secure and sequence‐specific gene editing tools. The clustered regularly interspaced short palindromic repeat (CRISPR) system, a CRISPR‐associated RNA‐guided endonuclease Cas9 (CRISPR‐associated protein 9)‐targeted genome editing tool, has recently been applied in engineering gene circuits for its unique properties‐operability, high efficiency and programmability. The traditional single‐targeted therapy cannot effectively distinguish tumour cells from normal cells, and gene therapy for single targets has poor anti‐tumour effects, which severely limits the application of gene therapy. Currently, the design of gene circuits using tumour‐specific targets based on CRISPR/Cas systems provides a new way for precision cancer therapy. Hence, the application of intelligentized gene circuits based on CRISPR technology effectively guarantees the safety, efficiency and specificity of cancer therapy. Here, we assessed the use of synthetic gene circuits and if the CRISPR system could be used, especially artificial switch‐inducible Cas9, to more effectively target and treat tumour cells. Moreover, we also discussed recent advances, prospectives and underlying challenges in CRISPR‐based gene circuit development.     

The computational challenges of applying comparative-based computational methods to whole genomes

The explosion in genomic sequence available in public databases has resulted in an unprecedented opportunity for computational whole genome analyses. A number of promising comparative-based approaches have been developed for gene finding, regulatory element discovery and other purposes, and it is clear that these tools will play a fundamental role in analysing the enormous amount of new data that is currently being generated. The synthesis of computationally intensive comparative computational approaches with the requirement for whole genome analysis represents both an unprecedented challenge and opportunity for computational scientists. We focus on a few of these challenges, using by way of example the problems of alignment, gene finding and regulatory element discovery, and discuss the issues that have arisen in attempts to solve these problems in the context of whole genome analysis pipelines.     

Engineering gene overlaps to sustain genetic constructs in vivo

Evolution is often an obstacle to the engineering of stable biological systems due to the selection of mutations inactivating costly gene circuits. Gene overlaps induce important constraints on sequences and their evolution. We show that these constraints can be harnessed to increase the stability of costly genes by purging loss-of-function mutations. We combine computational and synthetic biology approaches to rationally design an overlapping reading frame expressing an essential gene within an existing gene to protect. Our algorithm succeeded in creating overlapping reading frames in 80% of E. coli genes. Experimentally, scoring mutations in both genes of such overlapping construct, we found that a significant fraction of mutations impacting the gene to protect have a deleterious effect on the essential gene. Such an overlap thus protects a costly gene from removal by natural selection by associating the benefit of this removal with a larger or even lethal cost. In our synthetic constructs, the overlap converts many of the possible mutants into evolutionary dead-ends, reducing the evolutionary potential of the system and thus increasing its stability over time.     

Light-controlled synthetic gene circuits

Highly complex synthetic gene circuits have been engineered in living organisms to develop systems with new biological properties. A precise trigger to activate or deactivate these complex systems is desired in order to tightly control different parts of a synthetic or natural network. Light represents an excellent tool to achieve this goal as it can be regulated in timing, location, intensity, and wavelength, which allows for precise spatiotemporal control over genetic circuits. Recently, light has been used as a trigger to control the biological function of small molecules, oligonucleotides, and proteins involved as parts in gene circuits. Light activation has enabled the construction of unique systems in living organisms such as band-pass filters and edge-detectors in bacterial cells. Additionally, light also allows for the regulation of intermediate steps of complex dynamic pathways in mammalian cells such as those involved in kinase networks. Herein we describe recent advancements in the area of light-controlled synthetic networks.     

合成基因线路规模化设计面临的挑战

随着合成基因线路规模的增加,传统的合成基因线路设计思路的瓶颈逐渐凸显,许多之前被忽略的因素对大规模基因线路的性能可能造成显著影响,这对合成基因线路的设计带来了新的挑战。本文重点梳理了基因表达噪声和竞争效应两方面对基因线路性能的影响,阐释了二者间的紧密联系,并基于理性设计的思路,从模拟-数字运算设计、网络拓扑设计、基因线路中的信息传递理论和动态信号等方面,归纳总结了解决这些问题的潜在方案,并展望了规模化合成基因线路理性设计的未来发展方向。     

Rapid construction of insulated genetic circuits via synthetic sequence-guided isothermal assembly

In vitro recombination methods have enabled one-step construction of large DNA sequences from multiple parts. Although synthetic biological circuits can in principle be assembled in the same fashion, they typically contain repeated sequence elements such as standard promoters and terminators that interfere with homologous recombination. Here we use a computational approach to design synthetic, biologically inactive unique nucleotide sequences (UNSes) that facilitate accurate ordered assembly. Importantly, our designed UNSes make it possible to assemble parts with repeated terminator and insulator sequences, and thereby create insulated functional genetic circuits in bacteria and mammalian cells. Using UNS-guided assembly to construct repeating promoter-gene-terminator parts, we systematically varied gene expression to optimize production of a deoxychromoviridans biosynthetic pathway in Escherichia coli. We then used this system to construct complex eukaryotic AND-logic gates for genomic integration into embryonic stem cells. Construction was performed by using a standardized series of UNS-bearing BioBrick-compatible vectors, which enable modular assembly and facilitate reuse of individual parts. UNS-guided isothermal assembly is broadly applicable to the construction and optimization of genetic circuits and particularly those requiring tight insulation, such as complex biosynthetic pathways, sensors, counters and logic gates.     

A split intein T7 RNA polymerase for transcriptional AND-logic

Synthetic biology has developed numerous parts for building synthetic gene circuits. However, few parts have been described for prokaryotes to integrate two signals at a promoter in an AND fashion, i.e. the promoter is only activated in the presence of both signals. Here we present a new part for this function: a split intein T7 RNA polymerase. We divide T7 RNA polymerase into two expression domains and fuse each to a split intein. Only when both domains are expressed does the split intein mediate protein trans-splicing, yielding a full-length T7 RNA polymerase that can transcribe genes via a T7 promoter. We demonstrate an AND gate with the new part: the signal-to-background ratio is very high, resulting in an almost digital signal. This has utility for more complex circuits and so we construct a band-pass filter in Escherichia coli. The split intein approach should be widely applicable for engineering artificial gene circuit parts.     

Computation of steady-state probability distributions in stochastic models of cellular networks

Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.     

It's the machine that matters: Predicting gene function and phenotype from protein networks

Increasing knowledge about the organization of proteins into complexes, systems, and pathways has led to a flowering of theoretical approaches for exploiting this knowledge in order to better learn the functions of proteins and their roles underlying phenotypic traits and diseases. Much of this body of theory has been developed and tested in model organisms, relying on their relative simplicity and genetic and biochemical tractability to accelerate the research. In this review, we discuss several of the major approaches for computationally integrating proteomics and genomics observations into integrated protein networks, then applying guilt-by-association in these networks in order to identify genes underlying traits. Recent trends in this field include a rising appreciation of the modular network organization of proteins underlying traits or mutational phenotypes, and how to exploit such protein modularity using computational approaches related to the internet search algorithm PageRank. Many protein network-based predictions have recently been experimentally confirmed in yeast, worms, plants, and mice, and several successful approaches in model organisms have been directly translated to analyze human disease, with notable recent applications to glioma and breast cancer prognosis.