Synthetic biology (Oxford, England)最新文献

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability and reductions in technical risk and cost.

How the ribonucleic acid (RNA) world transited to the deoxyribonucleic acid (DNA) world has remained controversial in evolutionary biology. At a certain time point in the transition from the RNA world to the DNA world, 'RNA replicons', in which RNAs produce proteins to replicate their coding RNA, and 'DNA replicons', in which DNAs produce RNA to synthesize proteins that replicate their coding DNA, can be assumed to coexist. The coexistent state of RNA replicons and DNA replicons is desired for experimental approaches to determine how the DNA world overtook the RNA world. We constructed a mini-RNA replicon in Escherichia coli. This mini-RNA replicon encoded the β subunit, one of the subunits of the Qβ replicase derived from the positive-sense single-stranded Qβ RNA phage and is replicated by the replicase in E. coli. To maintain the mini-RNA replicon persistently in E. coli cells, we employed a system of α complementation of LacZ that was dependent on the Qβ replicase, allowing the cells carrying the RNA replicon to grow in the lactose minimal medium selectively. The coexistent state of the mini-RNA replicon and DNA replicon (E. coli genome) was successively synthesized. The coexistent state can be used as a starting system to experimentally demonstrate the transition from the RNA-protein world to the DNA world, which will contribute to progress in the research field of the origin of life.

While synthetic biology is hoped to hold promise and potential to address pressing global challenges, the issue of regulation is an under-appreciated challenge. Particularly in Europe, the regulatory frameworks involved are rooted in historical concepts based on containment and release. Through a series of case studies including a field-use biosensor intended to detect arsenic in well water in Nepal and Bangladesh, and insects engineered for sterility, we explore the implications that this regulatory and conceptual divide has had on the deployment of synthetic biology projects in different national contexts. We then consider some of the broader impacts that regulation can have on the development of synthetic biology as a field, not only in Europe but also globally, with a particular emphasis on low- and middle-income countries. We propose that future regulatory adaptability would be increased by moving away from a containment and release dichotomy and toward a more comprehensive assessment that accounts for the possibility of varying degrees of 'contained release'. Graphical Abstract.

As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn (DBTL) approach to simultaneously learn how a function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize and algorithmically perform each step of the DBTL cycle. However, there are many fewer options for automating the build step. Build typically involves deoxyribonucleic acid (DNA) assembly, which remains manual, low throughput and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron, an open-source Python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies, beginning from fragment amplification. We show that AssemblyTron can perform polymerase chain reactions across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies (IVAs) with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent IVA also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs and wastes associated with synthetic biology, which, along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.

Live-cell imaging is extremely common in synthetic biology research, but its ability to be applied reproducibly across laboratories can be hindered by a lack of standardized image analysis. Here, we introduce a novel cell segmentation method developed as part of a broader Independent Verification & Validation (IV&V) program aimed at characterizing engineered Dictyostelium cells. Standardizing image analysis was found to be highly challenging: the amount of human judgment required for parameter optimization, algorithm tweaking, training and data pre-processing steps forms serious challenges for reproducibility. To bring automation and help remove bias from live-cell image analysis, we developed a self-supervised learning (SSL) method that recursively trains itself directly from motion in live-cell microscopy images without any end-user input, thus providing objective cell segmentation. Here, we highlight this SSL method applied to characterizing the engineered Dictyostelium cells of the original IV&V program. This approach is highly generalizable, accepting images from any cell type or optical modality without the need for manual training or parameter optimization. This method represents an important step toward automated bioimage analysis software and reflects broader efforts to design accessible measurement technologies to enhance reproducibility in synthetic biology research.

Maximizing protein secretion is an important target in the design of engineered living systems. In this paper, we characterize a trade-off between cell growth and per-cell protein secretion in the curli biofilm secretion system of Escherichia coli Nissle 1917. Initial characterization using 24-h continuous growth and protein production monitoring confirms decreased growth rates at high induction, leading to a local maximum in total protein production at intermediate induction. Propidium iodide (PI) staining at the endpoint indicates that cellular death is a dominant cause of growth reduction. Assaying variants with combinatorial constructs of inner and outer membrane secretion tags, we find that diminished growth at high production is specific to secretory variants associated with periplasmic stress mediated by outer membrane secretion and periplasmic accumulation of protein containing the outer membrane transport tag. RNA sequencing experiments indicate upregulation of known periplasmic stress response genes in the highly secreting variant, further implicating periplasmic stress in the growth-secretion trade-off. Overall, these results motivate additional strategies for optimizing total protein production and longevity of secretory engineered living systems Graphical Abstract.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.

Reusability has been a key issue since the origins of the parts-based approach to synthetic biology. Starting with the BioBrick™ standard part, multiple efforts have aimed to make biology more exchangeable. The reusability of parts and other deoxyribonucleic acid-based data has proven over time to be challenging, however. Drawing on a series of qualitative interviews and an international workshop, this article explores the challenges of reusability in real laboratory practice. It shows particular ways that standards are experienced as presenting shortcomings for capturing the kinds of contextual information crucial for scientists to be able to reuse biological parts and data. I argue that researchers in specific laboratories develop a sense of how much circumstantial detail they need to share for others to be able to make sense of their data and possibly reuse it. When choosing particular reporting formats, recharacterizing data to gain closer knowledge or requesting additional information, researchers enact an 'economy of details'. The farther apart two laboratories are in disciplinary, epistemological, technical and geographical terms, the more detailed information needs to be captured for data to be reusable across contexts. In synthetic biology, disciplinary distance between computing science and engineering researchers and experimentalist biologists is reflected in diverging views on standards: what kind of information should be included to enable reusability, what kind of information can be captured by standards at all and how they may serve to produce and circulate knowledge. I argue that such interdisciplinary tensions lie at the core of difficulties in setting standards in synthetic biology.