Synthetic Biology underpins advances in the bioeconomy
Biological systems - including the simplest cells - exhibit a broad range of functions to thrive in their environment. Research in the Imperial College Centre for Synthetic Biology is focused on the possibility of engineering the underlying biochemical processes to solve many of the challenges facing society, from healthcare to sustainable energy. In particular, we model, analyse, design and build biological and biochemical systems in living cells and/or in cell extracts, both exploring and enhancing the engineering potential of biology.
As part of our research we develop novel methods to accelerate the celebrated Design-Build-Test-Learn synthetic biology cycle. As such research in the Centre for Synthetic Biology highly multi- and interdisciplinary covering computational modelling and machine learning approaches; automated platform development and genetic circuit engineering ; multi-cellular and multi-organismal interactions, including gene drive and genome engineering; metabolic engineering; in vitro/cell-free synthetic biology; engineered phages and directed evolution; and biomimetics, biomaterials and biological engineering.
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Journal articleLiberante FG, Ellis T, 2021,
As DNA synthesis has become cheaper, it has made assembly of larger and larger genes possible. To fully realise this opportunity for a new era of synthetic biology in mammals, a number of gaps are beginning to be addressed in the design, synthesis, assembly and delivery of DNA constructs into large genomes. While current DNA design software is still inadequate for complex mammalian genomes, editing large bacterial artificial chromosomes is now easier. Newer viral technologies, such as herpes simplex virus, have been adapted for assembly of mammalian artificial chromosomes. Further advances in genome engineering, such as CRISPR- and Retron-based systems, will simplify targeted insertion of big DNA – all promising an exciting future for this field.
Journal articleCabello-Garcia J, Bae W, Stan G-BV, et al., 2021,
Handhold-mediated strand displacement: a nucleic acid based mechanism for generating far-from-equilibrium assemblies through templated reactions., ACS Nano, Vol: 15, Pages: 3272-3283, ISSN: 1936-0851
The use of templates is a well-established method for the production of sequence-controlled assemblies, particularly long polymers. Templating is canonically envisioned as akin to a self-assembly process, wherein sequence-specific recognition interactions between a template and a pool of monomers favor the assembly of a particular polymer sequence at equilibrium. However, during the biogenesis of sequence-controlled polymers, template recognition interactions are transient; RNA and proteins detach spontaneously from their templates to perform their biological functions and allow template reuse. Breaking template recognition interactions puts the product sequence distribution far from equilibrium, since specific product formation can no longer rely on an equilibrium dominated by selective copy-template bonds. The rewards of engineering artificial polymer systems capable of spontaneously exhibiting nonequilibrium templating are large, but fields like DNA nanotechnology lack the requisite tools; the specificity and drive of conventional DNA reactions rely on product stability at equilibrium, sequestering any recognition interaction in products. The proposed alternative is handhold-mediated strand displacement (HMSD), a DNA-based reaction mechanism suited to producing out-of-equilibrium products. HMSD decouples the drive and specificity of the reaction by introducing a transient recognition interaction, the handhold. We measure the kinetics of 98 different HMSD systems to prove that handholds can accelerate displacement by 4 orders of magnitude without being sequestered in the final product. We then use HMSD to template the selective assembly of any one product DNA duplex from an ensemble of equally stable alternatives, generating a far-from-equilibrium output. HMSD thus brings DNA nanotechnology closer to the complexity of out-of-equilibrium biological systems.
Journal articleKuntz Nussio J, Thomas P, Stan G, et al., 2021,
We study a class of countably-infinite-dimensional linear programs (CILPs)whose feasible sets are bounded subsets of appropriately defined spaces ofmeasures. The optimal value, optimal points, and minimal points of these CILPscan be approximated by solving finite-dimensional linear programs. We show howto construct finite-dimensional programs that lead to approximations witheasy-to-evaluate error bounds, and we prove that the errors converge to zero asthe size of the finite-dimensional programs approaches that of the originalproblem. We discuss the use of our methods in the computation of the stationarydistributions, occupation measures, and exit distributions of Markov~chains.
Journal articleKuntz J, Thomas P, Stan G-B, et al., 2021,
Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations, SIAM Review, ISSN: 0036-1445
Computing the stationary distributions of a continuous-time Markov chaininvolves solving a set of linear equations. In most cases of interest, thenumber of equations is infinite or too large, and cannot be solved analyticallyor numerically. Several approximation schemes overcome this issue by truncatingthe state space to a manageable size. In this review, we first give acomprehensive theoretical account of the stationary distributions and theirrelation to the long-term behaviour of the Markov chain, which is readilyaccessible to non-experts and free of irreducibility assumptions made instandard texts. We then review truncation-based approximation schemes payingparticular attention to their convergence and to the errors they introduce, andwe illustrate their performance with an example of a stochastic reactionnetwork of relevance in biology and chemistry. We conclude by elaborating oncomputational trade-offs associated with error control and some open questions.
Journal articleSelles Vidal L, Ayala R, Stan G-B, et al., 2021,
rfaRm is an R package providing a client-side interface for the Rfam database of non-coding RNA and other structured RNA elements. The package facilitates the search of the Rfam database by keywords or sequences, as well as the retrieval of all available information about specific Rfam families, such as member sequences, multiple sequence alignments, secondary structures and covariance models. By providing such programmatic access to the Rfam database, rfaRm enables genomic workflows to incorporate information about non-coding RNA, whose potential cannot be fully exploited just through interactive access to the database. The features of rfaRm are demonstrated by using it to analyze the SARS-CoV-2 genome as an example case.
Journal articleSarvari P, Ingram D, Stan G-B, 2021,
A modelling framework linking resource-based stochastic translation to the optimal design of synthetic constructs, Biology, Vol: 10, ISSN: 2079-7737
The effect of gene expression burden on engineered cells has motivated the use of “whole-cell models” (WCMs) that use shared cellular resources to predict how unnatural gene expression affects cell growth. A common problem with many WCMs is their inability to capture translation in sufficient detail to consider the impact of ribosomal queue formation on mRNA transcripts. To address this, we have built a “stochastic cell calculator” (StoCellAtor) that combines a modified TASEP with a stochastic implementation of an existing WCM. We show how our framework can be used to link a synthetic construct’s modular design (promoter, ribosome binding site (RBS) and codon composition) to protein yield during continuous culture, with a particular focus on the effects of low-efficiency codons and their impact on ribosomal queues. Through our analysis, we recover design principles previously established in our work on burden-sensing strategies, namely that changing promoter strength is often a more efficient way to increase protein yield than RBS strength. Importantly, however, we show how these design implications can change depending on both the duration of protein expression, and on the presence of ribosomal queues.
Journal articleLossi NS, Manoli E, Foerster A, et al., 2021,
The HsiB1C1 (TssB-TssC) complex of the pseudomonas aeruginosa Type VI secretion system forms a bacteriophage tail sheathlike structure, Journal of Biological Chemistry, Vol: 288, Pages: 7536-7548, ISSN: 0021-9258
Protein secretion systems in Gram-negative bacteria evolved into a variety of molecular nanomachines. They are related to cell envelope complexes, which are involved in assembly of surface appendages or transport of solutes. They are classified as types, the most recent addition being the type VI secretion system (T6SS). The T6SS displays similarities to bacteriophage tail, which drives DNA injection into bacteria. The Hcp protein is related to the T4 bacteriophage tail tube protein gp19, whereas VgrG proteins structurally resemble the gp27/gp5 puncturing device of the phage. The tube and spike of the phage are pushed through the bacterial envelope upon contraction of a tail sheath composed of gp18. In Vibrio cholerae it was proposed that VipA and VipB assemble into a tail sheathlike structure. Here we confirm these previous data by showing that HsiB1 and HsiC1 of the Pseudomonas aeruginosa H1-T6SS assemble into tubules resulting from stacking of cogwheel-like structures showing predominantly 12-fold symmetry. The internal diameter of the cogwheels is ∼100 Å, which is large enough to accommodate an Hcp tube whose external diameter has been reported to be 85 Å. The N-terminal 212 residues of HsiC1 are sufficient to form a stable complex with HsiB1, but the C terminus of HsiC1 is essential for the formation of the tubelike structure. Bioinformatics analysis suggests that HsiC1 displays similarities to gp18-like proteins in its C-terminal region. In conclusion, we provide further structural and mechanistic insights into the T6SS and show that a phage sheathlike structure is likely to be a conserved element across all T6SSs.
Journal articleKeck FD, Polizzi K, 2021,
Advances in synthetic biology have made microbes easier to engineer than ever before. However, synthetic biology in animals and plants has lagged behind. Since it is now known that the phenotype of higher organisms depends largely on their microbiota, we propose that this is an easier route to achieving synthetic biology applications in these organisms.
Journal articleNiu T, Lv X, Liu Y, et al., 2021,
The elucidation of phosphosugar stress response in Bacillus subtilis guides strain engineering for high N-acetylglucosamine production., Biotechnology and Bioengineering, Vol: 118, Pages: 383-396, ISSN: 0006-3592
Bacillus subtilis is a preferred microbial host for the industrial production of nutraceuticals and a promising candidate for the synthesis of functional sugars, such as N-acetylglucosamine (GlcNAc). Previously, a GlcNAc-overproducer Bacillus subtilis SFMI was constructed using glmS ribozyme dual regulatory tool. Herein, we further engineered to enhance carbon flux from glucose towards GlcNAc synthesis. As a result, the increased flux towards GlcNAc synthesis triggered phosphosugar stress response, which caused abnormal cell growth. Unfortunately, the mechanism of phosphosugar stress response had not been elucidated in B. subtilis. In order to reveal stress mechanism and overcome its negative effect in bioproduction, we performed comparative transcriptome analysis. The results indicate that cells slow glucose utilization by repression of glucose import and accelerate catabolic reactions of phosphosugar. To verify these results, we overexpressed the phosphatase YwpJ, which relieved phosphosugar stress and allowed us to identify the enzyme responsible for GlcNAc synthesis from GlcNAc6P. In addition, the deletion of nagBB and murQ, responsible for GlcNAc precursor degradation, further improved GlcNAc synthesis. The best engineered strain, B. subtilis FMIP34, increased GlcNAc titer from 11.5 to 26.1 g/L in shake flasks and produced 87.5 g/L GlcNAc in 30-L fed-batch bioreactor. Our results not only elucidate, for the first time, the phosphosugar stress response mechanism in B. subtilis, but also demonstrate how the combination of rational metabolic engineering with novel insights into physiology and metabolism allows the construction of highly efficient microbial cell factories for the production of high value chemicals. This article is protected by copyright. All rights reserved.
Journal articleMielcarek M, Isalan M, 2021,
Journal articleBerengut J, Kui Wong C, Berengut J, et al., 2020,
Biology demonstrates how a near infinite array of complex systems and structures at many scales can originate from the self-assembly of component parts on the nanoscale. But to fully exploit the benefits of self-assembly for nanotechnology, a crucial challenge remains: How do we rationally encode well-defined global architectures in subunits that are much smaller than their assemblies? Strain accumulation via geometric frustration is one mechanism that has been used to explain the self-assembly of global architectures in diverse and complex systems a posteriori. Here we take the next step and use strain accumulation as a rational design principle to control the length distributions of self-assembling polymers. We use the DNA origami method to design and synthesize a molecular subunit known as the PolyBrick, which perturbs its shape in response to local interactions via flexible allosteric blocking domains. These perturbations accumulate at the ends of polymers during growth, until the deformation becomes incompatible with further extension. We demonstrate that the key thermodynamic factors for controlling length distributions are the intersubunit binding free energy and the fundamental strain free energy, both which can be rationally encoded in a PolyBrick subunit. While passive polymerization yields geometrical distributions, which have the highest statistical length uncertainty for a given mean, the PolyBrick yields polymers that approach Gaussian length distributions whose variance is entirely determined by the strain free energy. We also show how strain accumulation can in principle yield length distributions that become tighter with increasing subunit affinity and approach distributions with uniform polymer lengths. Finally, coarse-grained molecular dynamics and Monte Carlo simulations delineate and quantify the dominant forces influencing strain accumulation in a molecular system. This study constitutes a fundamental investigation of the use of strain accumula
Journal articleMoya-Ramirez I, Bouton C, Kontoravdi C, et al., 2020,
5 Cap structures are ubiquitous on eukaryotic mRNAs, essential for post-transcriptional processing,translation initiation and stability. Here we describea biosensor designed to detect the presence of capstructures on mRNAs that is also sensitive to mRNAdegradation, so uncapped or degraded mRNAs canbe detected in a single step. The biosensor is basedon a chimeric protein that combines the recognitionand transduction roles in a single molecule. The mainfeature of this sensor is its simplicity, enabling semiquantitative analyses of capping levels with minimalinstrumentation. The biosensor was demonstratedto detect the capping level on several in vitro transcribed mRNAs. Its sensitivity and dynamic rangeremained constant with RNAs ranging in size from250 nt to approximately 2700 nt and the biosensorwas able to detect variations in the capping level inincrements of at least 20%, with a limit of detection of2.4 pmol. Remarkably, it also can be applied to morecomplex analytes, such mRNA vaccines and mRNAstranscribed in vivo. This biosensor is an innovativeexample of a technology able to detect analyticallychallenging structures such as mRNA caps. It couldfind application in a variety of scenarios, from qualityanalysis of mRNA-based products such as vaccinesto optimization of in vitro capping reactions.
Journal articleOuldridge T, Stan G-B, Bae W, 2021,
In situ generation of RNA complexes for synthetic molecular strand displacement circuits in autonomous systems, Nano Letters: a journal dedicated to nanoscience and nanotechnology, Vol: 21, Pages: 265-271, ISSN: 1530-6984
Synthetic molecular circuits implementing DNA or RNA strand-displacement reactions can be used to build complex systems such as molecular computers and feedback control systems. Despite recent advances, application of nucleic acid-based circuits in vivo remains challenging due to a lack of efficient methods to produce their essential components, namely, multistranded complexes known as gates, in situ, i.e., in living cells or other autonomous systems. Here, we propose the use of naturally occurring self-cleaving ribozymes to cut a single-stranded RNA transcript into a gate complex of shorter strands, thereby opening new possibilities for the autonomous and continuous production of RNA strands in a stoichiometrically and structurally controlled way.
Journal articleDeshpande A, Ouldridge T, 2020,
Enzymes are central to both metabolism and information processing in cells. In both cases, an enzyme’s ability to accelerate a reaction without being consumed in the reaction is crucial. Nevertheless, enzymes are transiently sequestered when they bind to their substrates; this sequestration limits activity and potentially compromises information processing and signal transduction. In this article, we analyse the mechanism of enzyme–substrate catalysis from the perspective of minimizing the load on the enzymes through sequestration, while maintaining at least a minimum reaction flux. In particular, we ask: which binding free energies of the enzyme–substrate and enzyme–product reaction intermediates minimize the fraction of enzymes sequestered in complexes, while sustaining a certain minimal flux? Under reasonable biophysical assumptions, we find that the optimal design will saturate the bound on the minimal flux and reflects a basic trade-off in catalytic operation. If both binding free energies are too high, there is low sequestration, but the effective progress of the reaction is hampered. If both binding free energies are too low, there is high sequestration, and the reaction flux may also be suppressed in extreme cases. The optimal binding free energies are therefore neither too high nor too low, but in fact moderate. Moreover, the optimal difference in substrate and product binding free energies, which contributes to the thermodynamic driving force of the reaction, is in general strongly constrained by the intrinsic free-energy difference between products and reactants. Both the strategies of using a negative binding free-energy difference to drive the catalyst-bound reaction forward and of using a positive binding free-energy difference to enhance detachment of the product are limited in their efficacy.
Journal articleHe Q, Szczepańska P, Yuzbashev T, et al., 2020,
De novo production of resveratrol from glycerol by engineering different metabolic pathways in Yarrowia lipolytica, Metabolic Engineering Communications, Vol: 11, ISSN: 2214-0301
Resveratrol is a polyphenol with multiple applications in pharma, cosmetics and food. The aim of this study was to construct Yarrowia lipolytica strains able to produce resveratrol. For this purpose, resveratrol-biosynthesis genes from bacteria and plants were expressed in this host. Since resveratrol can be produced either via tyrosine or phenylaniline, both pathways were tested, first with a single copy and then with two copies. The phenylalanine pathway resulted in slightly higher production in glucose media, although when the media was supplemented with amino acids, the best production was found in the strain with two copies of the tyrosine pathway, which reached 0.085 g/L. When glucose was replaced by glycerol, a preferred substrate for bioproduction, the best results, 0.104 g/L, were obtained in a strain combining the expression of the two synthesis pathways. Finally, the best producer strain was tested in bioreactor conditions where a production of 0.43 g/L was reached. This study suggests that Y. lipolytica is a promising host for resveratrol production from glycerol.
Journal articleAw R, Spice AJ, Polizzi K, 2020,
Cell‐free protein synthesis is a powerful tool for engineering biology and has been utilized in many diverse applications, from biosensing and protein prototyping to biomanufacturing and the design of metabolic pathways. By exploiting host cellular machinery decoupled from cellular growth, proteins can be produced in vitro both on demand and rapidly. Eukaryotic cell‐free platforms are often neglected due to perceived complexity and low yields relative to their prokaryotic counterparts, despite providing a number of advantageous properties. The yeast Pichia pastoris (also known as Komagataella phaffii) is a particularly attractive eukaryotic host from which to generate cell‐free extracts, due to its ability to grow to high cell densities with high volumetric productivity, genetic tractability for strain engineering, and ability to perform post‐translational modifications. Here, we describe methods for conducting cell‐free protein synthesis using P. pastoris as the host, from preparing the cell lysates to protocols for both coupled and linked transcription‐translation reactions. By providing these methodologies, we hope to encourage the adoption of the platform by new and experienced users alike.
Journal articleMoore SJ, Lai H-E, Chee S-M, et al., 2020,
<jats:title>Abstract</jats:title><jats:p>Prokaryotic cell-free coupled transcription-translation (TX-TL) systems are emerging as a powerful tool to examine natural product biosynthetic pathways in a test-tube. The key advantages of this approach are the reduced experimental timescales and controlled reaction conditions. In order to realise this potential, specialised cell-free systems in organisms enriched for biosynthetic gene clusters, with strong protein production and well-characterised synthetic biology tools, is essential. The <jats:italic>Streptomyces</jats:italic> genus is a major source of natural products. To study enzymes and pathways from <jats:italic>Streptomyces</jats:italic>, we originally developed a homologous <jats:italic>Streptomyces</jats:italic> cell-free system to provide a native protein folding environment, a high G+C (%) tRNA pool and an active background metabolism. However, our initial yields were low (36 μg/mL) and showed a high level of batch-to-batch variation. Here, we present an updated high-yield and robust <jats:italic>Streptomyces</jats:italic> TX-TL protocol, reaching up to yields of 266 μg/mL of expressed recombinant protein. To complement this, we rapidly characterise a range of DNA parts with different reporters, express high G+C (%) biosynthetic genes and demonstrate an initial proof of concept for combined transcription, translation and biosynthesis of <jats:italic>Streptomyces</jats:italic> metabolic pathways in a single ‘one-pot’ reaction.</jats:p>
Journal articleLiu Y, Su A, Li J, et al., 2020,
Synthetic biology provides powerful tools and novel strategies for designing and modifying microorganisms to function as cell factories for biomanufacturing, which is a promising approach for realizing chemical production in a green and sustainable manner. Recent advances in genetic component design and genome engineering have enabled significant progresses in the field of synthetic biology chassis that have been developed for enzymes or biochemical production based on synthetic biology strategies, with particular reference to model microorganisms, such as Escherichia coli, Bacillus subtilis, Corynebacterium glutamicum, and Saccharomyces cerevisiae. In this review, strategies for engineering four different functional cellular modules which encompass the total process of biomanufacturing are discussed, including expanding the substrate spectrum for substrate uptake modules, refactoring biosynthetic pathways and dynamic regulation for product synthesis modules, balancing energy and redox modules, and cell membrane and cell wall engineering of product storage and secretion modules. Novel strategies of integrating and coordinating different cellular modules aided by synthetic co-culturing of multiple chassis, artificial intelligence–aided data mining for guiding strain development, and the process for designing automatic chassis development via biofoundry are expected to generate next generations of model microorganism chassis for more efficient biomanufacturing.
Journal articleHurault G, Domínguez-Hüttinger E, Langan S, et al., 2020,
Background: A topic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals.Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control.Objective: We aimed to develop a proof-of-principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis.Methods: We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children ove r6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting.Results: Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment.Conclusions: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level,and could inform the design of personalised treatment strategies that can be tested in future studies.Our model-based approach can be applied to other diseases such as asthma with apparent unpredictability and large variation in symptoms and treatment responses.
Journal articleBeal J, Farny NG, Haddock-Angelli T, et al., 2020,
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