Two-component signaling (TCS) is the primary means by which bacteria sense and respond to the environment. TCS involves two partner proteins working in tandem, which interact to perform cellular functions whereas limiting interactions with non-partners (i.e., cross-talk). We construct a Potts model for TCS that can quantitatively predict how mutating amino acid identities affect the interaction between TCS partners and non-partners. The parameters of this model are inferred directly from protein sequence data. This approach drastically reduces the computational complexity of exploring the sequence-space of TCS proteins. As a stringent test, we compare its predictions to a recent comprehensive mutational study, which characterized the functionality of 204 mutational variants of the PhoQ kinase in Escherichia coli. We find that our best predictions accurately reproduce the amino acid combinations found in experiment, which enable functional signaling with its partner PhoP. These predictions demonstrate the evolutionary pressure to preserve the interaction between TCS partners as well as prevent unwanted cross-talk. Further, we calculate the mutational change in the binding affinity between PhoQ and PhoP, providing an estimate to the amount of destabilization needed to disrupt TCS.
WHEN SCIENTISTS FINISHED sequencing the human genome in 2003, many researchers focused their attention on decoding the long strings of As, Ts, Gs and Cs as the way to understanding the mysteries of human genetics. The genome, however, doesn’t appear in nature as a simple long line of letters. Unfurled, the genome stretches for nearly two meters, yet it folds itself in coils and loops to fit inside a nucleus less than 10 microns in diameter.
So, SynBioBeta (San Francisco) happened. SynBioBeta summarized the achievements and announcements from the synbio industry, while Aaron Dy of PLOS SynBio wrote a typically excellent perspective piece of the broader issues and intellectual currents running through the synbio industry’s largest conference. My favorite science writer, Ed Yong, wrote a piece on the freeze-dried sensing and production systems pioneered by Collins lab. And there’s even a quote from new Northwestern professor Danielle Tullman-Ercek in there! The Department of Energy’s Bioenergy Technologies Office (BETO) announces a $35 million advanced biofoundry centered at Lawrence Livermore National Laboratory. If Elon Musk wants to colonize Mars, he’s going to need biotechnology. The Motley Fool’s Maxx Chatsko explains why. Tobi Ogunnaike at SingularityHub looks ahead to the questions and challenges that face the field of DNA data storage. BBC Horizons devotes an entire show to synthetic biology. Craig Venter, Christina Smolke, Amyris and IndieBio all feature.
Jordan Harrison, Sam Davidson and Paul Perkovich from the Northwestern iGEM team present on their project and progress delivering Cas9/sgRNA particles i
While we’ve gotten used to CRISPR-Cas9 blanketing the science news and even inspiring #CRISPRfacts, now we’re getting CRISPR in our mainstream TV shows. The new Marvel Netflix show, Luke Cage, used CRISPR genome editing to explain Carl Lucas’ transformation into superhero Luke Cage.
Artificial intelligence can make numerous contributions to synthetic biology. I would like to suggest three that are related to the past, present and future of artificial intelligence. From the past, works in biology and artificial systems by Turing and von Neumann prove highly interesting to explore within the new framework of synthetic biology, especially with regard to the notions of self-modification and self-replication and their links to emergence and the bottom-up approach. The current epistemological inquiry into emergence and research on swarm intelligence, superorganisms and biologically inspired cognitive architecture may lead to new achievements on the possibilities of synthetic biology in explaining cognitive processes. Finally, the present-day discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of notions such as "life", "cognition", "artificial" and "natural", as well as their interconnections in theoretical synthetic biology.
The idea of storing digital data in DNA seems like science fiction. At first glance, it might not seem obvious that a molecule can store data. The term “data storage” conjures up images of physical artifacts like CDs and ..
Modification of natural product backbones is a proven strategy for the development of clinically useful antibiotics. Such modifications have traditionally been achieved through medicinal chemistry strategies or via in vitro enzymatic activities. In an orthogonal approach, engineering of biosynthetic pathways using synthetic biology techniques can generate chemical diversity. Here we report the use of a minimal teicoplanin class glycopeptide antibiotic (GPA) scaffold expressed in a production-optimized Streptomyces coelicolor strain to expand GPA chemical diversity. Thirteen scaffold-modifying enzymes from 7 GPA biosynthetic gene clusters in different combinations were introduced into S. coelicolor, enabling us to explore the criteria for in-cell GPA modification. These include identifying specific isozymes that tolerate the unnatural GPA scaffold and modifications that prevent or allow further elaboration by other enzymes. Overall, 15 molecules were detected, 9 of which have not been reported previously. Some of these compounds showed activity against GPA-resistant bacteria. This system allows us to observe the complex interplay between substrates and both non-native and native tailoring enzymes in a cell-based system and establishes rules for GPA synthetic biology and subsequent expansion of GPA chemical diversity.
Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework BNICE.ch along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database ( http://lcsb-databases.epfl.ch/atlas/ ) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.
Feedback loops in biological networks, among others, enable differentiation and cell cycle progression, and increase robustness in signal transduction. In natural networks, feedback loops are often complex and intertwined, making it challenging to identify which loops are mainly responsible for an observed behavior. However, minimal synthetic replicas could allow for such identification. Here, we engineered a synthetic permease-inducer-repressor system in Saccharomyces cerevisiae to analyze if a transport-mediated positive feedback loop could be a core mechanism for the switch-like behavior in the regulation of metabolic gene networks such as the S. cerevisiae GAL system or the Escherichia coli lac operon. We characterized the synthetic circuit using deterministic and stochastic mathematical models. Similar to its natural counterparts, our synthetic system shows bistable and hysteretic behavior, and the inducer concentration range for bistability as well as the switching rates between the two stable states depend on the repressor concentration. Our results indicate that a generic permease-inducer-repressor circuit with a single feedback loop is sufficient to explain the experimentally observed bistable behavior of the natural systems. We anticipate that the approach of reimplementing natural systems with orthogonal parts to identify crucial network components is applicable to other natural systems such as signaling pathways.
Protein pairs that control stimulus response in bacteria maintain a sensitive balance between interaction specificity and promiscuity, according to Rice University scientists. A computational model developed at Rice's Center for Theoretical Biological Physics will help biologists take advantage of the homologous nature of bacterial signaling systems to reveal the minimal mutations that allow a signaling protein to be efficiently reprogrammed to prefer a nonpartner signaling protein. Their open-access paper on the topic published online by the Oxford University Press journal Molecular Biology and Evolution will be featured on the cover of the December issue. The research led by Rice biophysicist and protein-folding pioneer José Onuchic, postdoctoral researcher Ryan Cheng and alumnus Faruck Morcos, now of the University of Texas at Dallas, expands upon previous work to model two-component systems that co-evolve amino acids at their binding surfaces to recognize each other. Those systems consist of proteins in bacterial cells that signal each other to sense and respond to stimuli. The new work extends the team's models to cover how mutating a signaling protein affects its interaction with its partner, as well as its interaction with other signaling systems. The extended model connects the fact that partner proteins are able to find each other in a crowd while also preventing unnecessary crosstalk. "We're showing for the first time that the method has enormous power for designing new stimulus-response mechanisms in organisms," said Onuchic, who predicted that the research will bring the disciplines of synthetic biology and systems biology closer together. Synthetic biologists engineer organisms like bacteria to produce chemicals or act as components in biological circuits. The Rice team's goal is to simplify their work by allowing them to model many possibilities before taking a project to the lab. "There are three goals in synthetic biology," Onuchic said. "The first is to force an organism to do something it's not supposed to do, like make insulin. The second is to change a mechanism to respond differently to a particular stimulus. There may be many reasons, and that may affect the phenotype of how an organism behaves. The third is to rewire different parts of an organism to understand what the parts are and what happens if we switch them. That's what our project is about," he said. "We want to understand sequence selection for two-component systems but we also want to demonstrate that our model can predict mutational phenotypes," said Cheng, the paper's lead author. "With this method, we're telling people that if they want 20 functional mutants, they can just pick the top 20 produced by our model. We're simply ranking them. Someone who wants to design novel interactions could use our model, select mutations and generate those mutations." Every cell contains thousands of two-component signaling proteins programmed to find each other and serve as sensors that trigger a cell to act, most often through the activation or repression of genes. "These systems have lots of response-raising kinases (the sensor component) and there's a lot of promiscuity," Onuchic said. "That's important, because a system doesn't have one copy of each thing; it has multiple copies of everything, and they're interacting with each other all the time. "Proteins are so similar, with minor variations between them, because you want them to see everything in their environments," he said. "If they were meant to be completely partner-specific, with no promiscuity, they would be very different from each other. The problem when you make these mutations is that you may offset the balance between interacting with its signaling partner versus interacting with other signaling proteins in a deleterious way." "We've constructed a co-evolutionary landscape that can predict which mutants lead to functional signaling in bacteria," Cheng said. Co-evolutionary landscapes allow for the prediction of how mutations affect signaling in bacteria and, consequently, an organism's fitness. He said the open-access model will make it faster and easier for researchers to plug in mutations and determine whether and how they will function. "Now we can apply these models to predict how mutations affect phenotypes (an organism's characteristics). The direct connection between sequence-level mutations and how they lead to the emergent properties of an organism is a holy grail of molecular biology," he said. Explore further: Researchers tune in to protein pairs: Team quantifies how mutations affect cell signaling in bacteria More information: R. R. Cheng et al. Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes, Molecular Biology and Evolution (2016). DOI: 10.1093/molbev/msw188
Quorum sensing (QS) regulates many natural phenotypes (e.q. virulence, biofilm formation, antibiotic resistance), and its components, when incorporated into synthetic genetic circuits, enable user-directed phenotypes. We created a library of Escherichia coli lsr operon promoters using error-prone PCR (ePCR) and selected for promoters that provided E. coli with higher tetracycline resistance over the native promoter when placed upstream of the tet(C) gene. Among the fourteen clones identified, we found several mutations in the binding sites of QS repressor, LsrR. Using site-directed mutagenesis we restored all p-lsrR-box sites to the native sequence in order to maintain LsrR repression of the promoter, preserving the other mutations for analysis. Two promoter variants, EP01rec and EP14rec, were discovered exhibiting enhanced protein expression. In turn, these variants retained their ability to exhibit the LsrR-mediated QS switching activity. Their sequences suggest regulatory linkage between CytR (CRP repressor) and LsrR. These promoters improve upon the native system and exhibit advantages over synthetic QS promoters previously reported. Incorporation of these promoters will facilitate future applications of QS-regulation in synthetic biology and metabolic engineering.
DNAplotlib (www.dnaplotlib.org) is a computational toolkit for the programmable visualization of highly customizable, standards-compliant genetic designs. Functions are provided to aid with both visualization tasks and to extract and overlay associated experimental data. High-quality output is produced in the form of vector-based PDFs, rasterized images, and animated movies. All aspects of the rendering process can be easily customized or extended by the user to cover new forms of genetic part or regulation. DNAplotlib supports improved communication of genetic design information and offers new avenues for static, interactive and dynamic visualizations that map and explore the links between the structure and function of genetic parts, devices and systems; including metabolic pathways and genetic circuits. DNAplotlib is cross-platform software developed using Python.
The field, or at least the widely used term, of synthetic biology (synbio) started nearly two decades ago. As the field has matured, two PLoS ONE papers have analyzed the publishing data to look at trends, language, and connections among synbio researchers. This data provides snapshots of who’s publishing, what kind of research is being published, when it’s being published, and where it’s being published. The question of why is certainly open for interpretation but the growth dynamics of synthetic biology publishing can give some sense of why a the term has stuck as a useful unifying term.
In 2012, researchers used Thomson Reuters Web of Science publishing data to map where people are publishing synbio research, how those people are connected, and who’s funding it. More recently, three French researchers also used data from the Thomson Reuters Web of Science to assess how synthetic biology the different areas of synthetic biology have grown and interacted.
Both of these papers draw interesting pictures of how new terms and ideas spread within an new umbrella term for a kind of research. Together these two papers paint give us some answers to the ‘Who, What, When, Where, & Why’ of synthetic biology.
Who:
The broad synthetic biology community is growing and thankfully still taking in lots of newcomers from different backgrounds. The figure below shows the large growth in total publications along with the newcomers ratio for synbio authors. While the ratio of newcomers (1st time authors on synbio papers) is decreasing, the total number of new people publishing synthetic biology work is still increasing.
As synthetic biology moves away from trial and error and embraces more formal processes, workflows have emerged that cover the roadmap from conceptualization of a genetic device to its construction and measurement. This latter aspect (i.e., characterization and measurement of synthetic genetic constructs) has received relatively little attention to date, but it is crucial for their outcome. An end-to-end use case for engineering a simple synthetic device is presented, which is supported by information standards and computational methods and focuses on such characterization/measurement. This workflow captures the main stages of genetic device design and description and offers standardized tools for both population-based measurement and single-cell analysis. To this end, three separate aspects are addressed. First, the specific vector features are discussed. Although device/circuit design has been successfully automated, important structural information is usually overlooked, as in the case of plasmid vectors. The use of the Standard European Vector Architecture (SEVA) is advocated for selecting the optimal carrier of a design and its thorough description in order to unequivocally correlate digital definitions and molecular devices. A digital version of this plasmid format was developed with the Synthetic Biology Open Language (SBOL) along with a software tool that allows users to embed genetic parts in vector cargoes. This enables annotation of a mathematical model of the device’s kinetic reactions formatted with the Systems Biology Markup Language (SBML). From that point onward, the experimental results and their in silico counterparts proceed alongside, with constant feedback to preserve consistency between them. A second aspect involves a framework for the calibration of fluorescence-based measurements. One of the most challenging endeavors in standardization, metrology, is tackled by reinterpreting the experimental output in light of simulation results, allowing us to turn arbitrary fluorescence units into relative measurements. Finally, integration of single-cell methods into a framework for multicellular simulation and measurement is addressed, allowing standardized inspection of the interplay between the carrier chassis and the culture conditions.
The aim of this paper is to propose that current robotic technologies cannot have intentional states any more than is feasible within the sensorimotor variant of embodied cognition. It argues that anticipation is an emerging concept that can provide a bridge between both the deepest philosophical theories about the nature of life and cognition and the empirical biological and cognitive sciences steeped in reductionist and Newtonian conceptions of causality. The paper advocates that in order to move forward, cognitive robotics needs to embrace new platforms and a conceptual framework that will enable it to pursue, in a meaningful way, questions about autonomy and purposeful behaviour. We suggest that hybrid systems, part robotic and part cultures of neurones, offer experimental platforms where different dimensions of enactivism (sensorimotor, constitutive foundations of biological autonomy, including anticipation), and their relative contributions to cognition, can be investigated in an integrated way. A careful progression, mindful to the deep philosophical concerns but also respecting empirical evidence, will ultimately lead towards unifying theoretical and empirical biological sciences and may offer advancement where reductionist sciences have been so far faltering.
Our capabilities for systematic design and engineering of biological systems are rapidly increasing. Effectively engineering such systems, however, requires the synthesis of a rapidly expanding and changing complex body of knowledge, protocols, and methodologies. Many of the problems in managing this complexity, however, appear susceptible to being addressed by artificial intelligence (AI) techniques, i.e., methods enabling computers to represent, acquire, and employ knowledge. Such methods can be employed to automate physical and informational "routine" work and thus better allow humans to focus their attention on the deeper scientific and engineering issues. This paper examines the potential impact of AI on the engineering of biological organisms through the lens of a typical organism engineering workflow. We identify a number of key opportunities for significant impact, as well as challenges that must be overcome.
Synthetic biology combines different branches of biology and engineering aimed at designing synthetic biological circuits able to replicate emergent properties useful for the biotechnology industry, human health and environment. The role of negative feedback in noise propagation for a basic enzymatic reaction scheme is investigated. Two feedback control schemes on enzyme expression are considered: one from the final product of the pathway activity, the other from the enzyme accumulation. Both schemes are designed to provide the same steady-state average values of the involved players, in order to evaluate the feedback performances according to the same working mode. Computations are carried out numerically and analytically, the latter allowing to infer information on which model parameter setting leads to a more efficient noise attenuation, according to the chosen scheme. In addition to highlighting the role of the feedback in providing a substantial noise reduction, our investigation concludes that the effect of feedback is enhanced by increasing the promoter sensitivity for both schemes. A further interesting biological insight is that an increase in the promoter sensitivity provides more benefits to the feedback from the product with respect to the feedback from the enzyme, in terms of enlarging the parameter design space.
Cell-free biosynthesis in the form of in vitro multi-enzyme reaction networks or enzyme cascade reactions emerges as a promising tool to carry out complex catalysis in one-step, one-vessel settings. It combines the advantages of well-established in vitro biocatalysis with the power of multi-step in vivo pathways. Such cascades have been successfully applied to the synthesis of fine and bulk chemicals, monomers and complex polymers of chemical importance, and energy molecules from renewable resources as well as electricity. The scale of these initial attempts remains small, suggesting that more robust control of such systems and more efficient optimization are currently major bottlenecks. To this end, the very nature of enzyme cascade reactions as multi-membered systems requires novel approaches for implementation and optimization, some of which can be obtained from in vivo disciplines (such as pathway refactoring and DNA assembly), and some of which can be built on the unique, cell-free properties of cascade reactions (such as easy analytical access to all system intermediates to facilitate modeling).
The design and implementation of regulation motifs ensuring robust perfect adaptation are challenging problems in synthetic biology. Indeed, the design of high-yield robust metabolic pathways producing, for instance, drug precursors and biofuels, could be easily imagined to rely on such a control strategy in order to optimize production levels and reduce production costs, despite the presence of environmental disturbance and model uncertainty. We propose here a motif that ensures tracking and robust perfect adaptation for the controlled reaction network through integral feedback. Its metabolic load on the host is fully tunable and can be made arbitrarily close to the constitutive limit, the universal minimal metabolic load of all possible controllers. A DNA implementation of the controller network is finally provided. Computer simulations using realistic parameters demonstrate the good agreement between the DNA implementation and the ideal controller dynamics.
To get content containing either thought or leadership enter:
To get content containing both thought and leadership enter:
To get content containing the expression thought leadership enter:
You can enter several keywords and you can refine them whenever you want. Our suggestion engine uses more signals but entering a few keywords here will rapidly give you great content to curate.
Your new post is loading...
