Synthetic microbial consortia: from systematic analysis to construction and applications.

Synthetic biology is not a unified field. It involves many disciplines, from molecular genetics to biochemistry, and from engineering to bioinformatics. It includes xenobiological approaches that build and use artificial nucleic acids, metabolic reengineering of existing microbes, maybe using standardized parts and devices, creation of minimal organisms, bio-brick-biochemistry, and attempts to make synthetic organisms from scratch. The contributions to this thematic issue reflect on these scientific activities in synthetic biology and in related fields. They grew out of a workshop on the scientific, philosophical, and social dimensions of synthetic biology, including its research strategies, conceptual apparatus and societal implications. For many, the attraction of synthetic biology is its aim to apply in biology the methods of traditional engineering, especially rational engineering, using standardized components that allow plug-and-play modular design (Canton et al. 2008; Mutalik et al. 2013a, b). Rational engineering contrasts with creating desired systems through trial-and-error experimentation, which includes tried-and-true methods like tinkering and kludging, as well as sophisticated methods like generating and screening massive libraries of high-throughput wet-lab experimental data. Trial-and-error methods in synthetic biology also include familiar and widespread methods like in vitro (or directed) evolution (Yokobayashi et al. 2002), as well as new applications of machine-learning methods to make high-throughput experiments maximally intelligent and efficient (Caschera et al. 2010, 2011). Synthetic biology involves a plurality of experimental research programs. It includes top-down reengineering of existing life forms, for example, to mass-produce products such as pharmaceuticals (Martin et al. 2003) or biofuel (Savage et al. 2008). It also includes bottom-up attempts to create new minimal chemical life or ‘‘protocells’’ using nothing but non-living materials (Rasmussen et al. 2004, 2009a). Both top-down and bottom-up synthetic biology employ the two methods we contrasted above: rational engineering and trial-and-error experimentation. These two distinctions (top-down versus bottom-up, and rational engineering versus trial-and-error experimentation) define four quadrants in Table 1. These quadrants map achievements in synthetic biology and illustrate some of the field’s scientific plurality. Least familiar and least developed is the lower right quadrant of trial-and-error methods for bottomup synthetic biology, though some would argue that this quadrant also shows the most upside potential (e.g., Bedau 2013, this issue).

Synthetic Biology is an interdisciplinary field that utilizes well-established engineering principles, ranging from electrical, control and computer systems, for analyzing the biological systems, such as biological circuits, enzymes, pathways and controllers. Traditionally, these biological systems, i.e., the genetic circuits are analyzed using paper-and-pencil proofs and computer-based simulations techniques. However, these methods cannot provide accurate results due to their inherent limitations such as human error-proneness, round-off errors and the unverified algorithms present in the core of the tools, providing such analyses. In this paper, we propose to use higher-order-logic theorem proving as a complementary technique for analyzing these systems and thus overcome the above-mentioned issues. In particular, we propose a higher-order-logic theorem proving based framework to formally reason about the genetic circuits used in synthetic biology. The main idea is to, first, model the continuous dynamics of the genetic circuits using differential equations. The next step is to obtain the systems' transfer function from their corresponding block diagram representations. Finally, the transfer function based analysis of these differential equation based models is performed using the Laplace transform. To illustrate the practical utilization of our proposed framework, we formally analyze the genetic circuits of activated and repressed expressions of protein.

Shor and Preskill (see Phys. Rev. Lett., vol.85, p.441, 2000) have provided a simple proof of security of the standard quantum key distribution scheme by Bennett and Brassard (1984) by demonstrating a connection between key distribution and entanglement purification protocols (EPPs) with one-way communications. Here, we provide proofs of security of standard quantum key distribution schemes, Bennett and Brassard and the six-state scheme, against the most general attack, by using the techniques of two-way entanglement purification. We demonstrate clearly the advantage of classical post-processing with two-way classical communications over classical post-processing with only one-way classical communications in quantum key distribution (QKD). This is done by the explicit construction of a new protocol for (the error correction/detection and privacy amplification of) Bennett and Brassard that can tolerate a bit error rate of up to 18.9%, which is higher than what any Bennett and Brassard scheme with only one-way classical communications can possibly tolerate. Moreover, we demonstrate the advantage of the six-state scheme over Bennett and Brassard by showing that the six-state scheme can strictly tolerate a higher bit error rate than Bennett and Brassard. In particular, our six-state protocol can tolerate a bit error rate of 26.4%, which is higher than the upper bound of 25% bit error rate for any secure Bennett and Brassard protocol. Consequently, our protocols may allow higher key generation rate and remain secure over longer distances than previous protocols. Our investigation suggests that two-way entanglement purification is a useful tool in the study of advantage distillation, error correction, and privacy amplification protocols.

EDITORIAL article Front. Microbiol., 11 January 2016Sec. Microbial Physiology and Metabolism Volume 6 - 2015 | https://doi.org/10.3389/fmicb.2015.01562

Sense and sensibility: of synthetic biology and the redesign of bioreporter circuits.

The expression of many antigens, stimulatory molecules, or even metabolic pathways in mycobacteria such as Mycobacterium bovis BCG or M. smegmatis was made possible through the development of shuttle vectors, and several recombinant vaccines have been constructed. However, gene expression in any of these systems relied mostly on the selection of natural promoters expected to provide the required level of expression by trial and error. To establish a systematic selection of promoters with a range of strengths, we generated a library of mutagenized promoters through error-prone PCR of the strong PL5 promoter, originally from mycobacteriophage L5. These promoters were cloned upstream of the enhanced green fluorescent protein reporter gene, and recombinant M. smegmatis bacteria exhibiting a wide range of fluorescence levels were identified. A set of promoters was selected and identified as having high (pJK-F8), intermediate (pJK-B7, pJK-E6, pJK-D6), or low (pJK-C1) promoter strengths in both M. smegmatis and M. bovis BCG. The sequencing of the promoter region demonstrated that it was extensively modified (6 to 11%) in all of the plasmids selected. To test the functionality of the system, two different expression vectors were demonstrated to allow corresponding expression levels of the Schistosoma mansoni antigen Sm29 in BCG. The approach used here can be used to adjust expression levels for synthetic and/or systems biology studies or for vaccine development to maximize the immune response.

Synthetic biology has the potential to revolutionize biotechnology, public health, and agriculture. Recent studies have shown the enormous potential of plants as chassis for synthetic biology applications. However, tools to precisely manipulate metabolic pathways for bioproduction in plants are still needed. We used bacterial allosteric transcription factors (aTFs) that control gene expression in a ligand-specific manner and tested their ability to repress semi-synthetic promoters in plants. We also tested the modulation of their repression activity in response to specific plant metabolites, especially phenylpropanoid-related molecules. Using these aTFs, we also designed synthetic genetic circuits capable of computing Boolean logic operations. Three aTFs, CouR, FapR, and TtgR, achieved c. 95% repression of their respective target promoters. For TtgR, a sixfold de-repression could be triggered by inducing its ligand accumulation, showing its use as biosensor. Moreover, we designed synthetic genetic circuits that use AND, NAND, IMPLY, and NIMPLY Boolean logic operations and integrate metabolite levels as input to the circuit. We showed that biosensors can be implemented in plants to detect phenylpropanoid-related metabolites and activate a genetic circuit that follows a predefined logic, demonstrating their potential as tools for exerting control over plant metabolic pathways and facilitating the bioproduction of natural products.

In the past few years,new bacterial-cell-free transcription–translationsystems have emerged as potent and quick platforms for protein productionas well as for prototyping of DNA regulatory elements, genetic circuits,and metabolic pathways. The Gram-positive commensal Cutibacterium acnes is one of the most abundant bacteriapresent in the human skin microbiome. However, it has recently beenreported that some C. acnes phylotypescan be associated with common inflammatory skin conditions, such asacne vulgaris, whereas others seem to play a protective role, actingas possible “skin probiotics”. This fact has made C. acnes become a bacterial model of interest forthe cosmetic industry. In the present study we report for the firsttime the development and optimization of a C. acnes-based cell-free system (CFS) that is able to produce 85 μg/mLfirefly luciferase. We highlight the importance of harvesting thebacterial pellet in mid log phase and maintaining CFS reactions at30 °C and physiological pH to obtain the optimal yield. Additionally,a C. acnes promoter library was engineeredto compare coupled in vitro TX-TL activities, anda temperature biosensor was tested, demonstrating the wide range ofapplications of this toolkit in the synthetic biology field.

In recent years, synthetic biology develops rapidly, the purpose of which is to create beneficial products or organisms with special functions. Genetic circuits are dynamic regulating systems of organisms controlling their own life processes. According to specific rules and grammar, synthetic biologists try to design artificial genetic circuits to achieve their goals. In our research, we designed grammar rules on basis of the context-free grammar, imported a part library which we acquired from IGEM database and then we processed, and constructed a genetic circuit for expressing TetR in GenoCAD, which is a free web-based application for synthetic biology. Finally, we generated a plasmid map on the PlasMapper, and we can see the concrete information of the plasmid. We creatively designed the genetic circuit to express the TetR. The work provides a new dynamic regulating system for synthetic biology. We think the genetic circuit will help us predict the biological regulator process.

Recent developments demonstrate that the combination of microbiology with micro- and nanoelectronics is a successful approach to develop new miniaturized sensing devices and other technologies. In the last decade, there has been a shift from the optimization of the abiotic components, for example, the chip, to the improvement of the processing capabilities of cells through genetic engineering. The synthetic biology approach will not only give rise to systems with new functionalities, but will also improve the robustness and speed of their response towards applied signals. To this end, the development of new genetic circuits has to be guided by computational design methods that enable to tune and optimize the circuit response. As the successful design of genetic circuits is highly dependent on the quality and reliability of its composing elements, intense characterization of standard biological parts will be crucial for an efficient rational design process in the development of new genetic circuits. Microengineered devices can thereby offer a new analytical approach for the study of complex biological parts and systems. By summarizing the recent techniques in creating new synthetic circuits and in integrating biology with microdevices, this review aims at emphasizing the power of combining synthetic biology with microfluidics and microelectronics.

Optimization-Based Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM) Tools: Computational Approach to Synthetic Biology

Rational engineering of biological systems is often complicated by the complex but unwanted interactions between cellular components at multiple levels. Here we address this issue at the level of prokaryotic transcription by insulating minimal promoters and operators to prevent their interaction and enable the biophysical modeling of synthetic transcription without free parameters. This approach allows genetic circuit design with extraordinary precision and diversity, and consequently simplifies the design-build-test-learn cycle of circuit engineering to a mix-and-match workflow. As a demonstration, combinatorial promoters encoding NOT-gate functions were designed from scratch with mean errors of <1.5-fold and a success rate of >96% using our insulated transcription elements. Furthermore, four-node transcriptional networks with incoherent feed-forward loops that execute stripe-forming functions were obtained without any trial-and-error work. This insulation-based engineering strategy improves the resolution of genetic circuit technology and provides a simple approach for designing genetic circuits for systems and synthetic biology.

BackgroundThe use of in silico simulations as a basis for designing artificial biological systems (and experiments to characterize them) is one of the tangible differences between Synthetic Biology and “classical” Genetic Engineering. To this end, synthetic biologists have adopted approaches originating from the traditionally non-biological fields of Nonlinear Dynamics and Systems & Control Theory. However, due to the complex molecular interactions affecting the emergent properties of biological systems, mechanistic descriptions of even the simplest genetic circuits (transcriptional feedback oscillators, bi-stable switches) produced by these methods tend to be either oversimplified, or numerically intractable. More comprehensive and realistic models can be approximated by constructing “toy” genetic circuits that provide the experimenter with some degree of control over the transcriptional dynamics, and allow for experimental set-ups that generate reliable data reflecting the intracellular biochemical state in real time. To this end, we designed two genetic circuits (basic and tunable) capable of exhibiting synchronized oscillatory green fluorescent protein (GFP) expression in small populations of Escherichia coli cells. The functionality of the basic circuit was verified microscopically. High-level visualizations of computational simulations were analyzed to determine whether the reliability and utility of a synchronized transcriptional oscillator could be enhanced by the introduction of chemically inducible repressors.ResultsSynchronized oscillations in GFP expression were repeatedly observed in chemically linked sub-populations of cells. Computational simulations predicted that the introduction of independently inducible repressors substantially broaden the range of conditions under which oscillations could occur, in addition to allowing the frequency of the oscillation to be tuned.ConclusionsThe genetic circuits described here may prove to be valuable research tools for the study of synchronized transcriptional feedback loops under a variety of conditions and experimental set-ups. We further demonstrate the benefit of using abstract visualizations to discover subtle non-linear trends in complex dynamic models with large parameter spaces.

Synthetic biology uses genetically encoded devices and circuits to implement novel complex functions in living cells and organisms. A hallmark of these genetic circuits is the interaction among their individual parts, according to predefined rules, to process cellular information and produce a circuit output or response. As the number of individual components in a genetic circuit increases, so does the number of interactions needed to achieve the correct behavior, and hence, a greater need to fine-tune the levels of expression of each component. Transcriptional promoters play a key regulatory role in genetic circuits, as they influence the levels of RNA and proteins produced. In multicellular organisms, such as plants, they can also determine developmental, spatial, and tissue-specific patterns of gene expression. The 35S promoter from the Cauliflower Mosaic Virus (CaMV 35S) is widely used in plant biotechnology to direct high levels of gene expression in a variety of plant species. We produced a library of 21 variants of the CaMV 35S promoter by introducing all single nucleotide substitutions to the promoter's TATA box sequence. We then characterized the activity of all variants in homozygous transgenic plants and showed that some of these variants have lower activity than the wild type in plants. These promoter variants could be used to fine-tune the behavior of synthetic genetic circuits in plants.

Biological systems are inherently stochastic, a fact which is often ignored when simulating genetic circuits. Synthetic biology aims to design genetic circuits de novo, and cannot therefore afford to ignore the effects of stochastic behavior. Since computational design tools will be essential for large-scale synthetic biology, it is important to develop an understanding of the role of stochasticity in molecular biology, and incorporate this understanding into computational tools for genetic circuit design. We report upon an investigation into the combination of evolutionary algorithms and stochastic simulation for genetic circuit design, to design regulatory systems based on the Bacillus subtilis sin operon.