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 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 articleDeshpande A, Ouldridge T, 2020,
Optimizing enzymatic catalysts for rapid turnover of substrates with low enzyme sequestration, Biological Cybernetics: communication and control in organisms and automata, Vol: 114, Pages: 653-668, ISSN: 0340-1200
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 articleAw R, Spice AJ, Polizzi K, 2020,
Methods for expression of recombinant proteins using a Pichia pastoris cell-free system, Current protocols in protein science, Vol: 102, ISSN: 1934-3655
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,
A <i>Streptomyces venezuelae</i> Cell-Free Toolkit for Synthetic Biology
<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,
Towards next-generation model microorganism chassis for biomanufacturing, Applied Microbiology and Biotechnology, Vol: 104, Pages: 9095-9108, ISSN: 0175-7598
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,
Personalised prediction of daily eczema severity scores using a mechanistic machine learning model, Clinical and Experimental Allergy, Vol: 50, Pages: 1258-1266, ISSN: 0954-7894
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,
Robust estimation of bacterial cell count from optical density (vol 3, 512, 2020), COMMUNICATIONS BIOLOGY, Vol: 3
- Author Web Link
- Citations: 1
Journal articleAntonakoudis A, Barbosa R, Kotidis P, et al., 2020,
The era of big data: Genome-scale modelling meets machine learning, Computational and Structural Biotechnology Journal, Vol: 18, Pages: 3287-3300, ISSN: 2001-0370
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
Journal articleMurray JW, Rutherford AW, Nixon PJ, 2020,
Photosystem II in a state of disassembly, Joule, Vol: 4, Pages: 2082-2084, ISSN: 2542-4351
The light-driven oxidation of water to oxygen characteristic of oxygenic photosynthesis is catalyzed by a redox-active manganese/calcium cluster embedded in the Photosystem II (PSII) complex. How the cluster is assembled during the biogenesis and repair of PSII is unclear. Cryo-electron microscopy data have now provided new insights into the structure of a PSII complex lacking the cluster and have identified features that might be important for delivery and stabilization of Mn during assembly.
Journal articleMeng F, Ellis T, 2020,
The second decade of synthetic biology: 2010-2020., Nature Communications, Vol: 11, Pages: 5174-5174, ISSN: 2041-1723
Synthetic biology is among the most hyped research topics this century, and in2010 it entered its teenage years. But rather than these being a problematictime, we’ve seen synthetic biology blossom and deliver many new technologiesand landmark achievements.
Journal articleWang J, Ledesma-Amaro R, Wei Y, et al., 2020,
Metabolic engineering for increased lipid accumulation in Yarrowia lipolytica -A Review, Bioresource Technology, Vol: 313, Pages: 1-11, ISSN: 0960-8524
Current energy security and climate change policies encourage the development and utilization of bioenergy. Oleaginous yeasts provide a particularly attractive platform for the sustainable production of biofuels and industrial chemicals due to their ability to accumulate high amounts of lipids. In particular, microbial lipids in the form of triacylglycerides (TAGs) produced from renewable feedstocks have attracted considerable attention because they can be directly used in the production of biodiesel and oleochemicals analogous to petrochemicals. As an oleaginous yeast that is generally regarded as safe, Yarrowia lipolytica has been extensively studied, with large amounts of data on its lipid metabolism, genetic tools, and genome sequencing and annotation. In this review, we highlight the newest strategies for increasing lipid accumulation using metabolic engineering and summarize the research advances on the overaccumulation of lipids in Y. lipolytica. Finally, perspectives for future engineering approaches are proposed.
Conference paperPan K, Hurault G, Arulkumaran K, et al., 2020,
EczemaNet: automating detection and severity assessment of atopic dermatitis, International Workshop on Machine Learning in Medical Imaging, Publisher: Springer Verlag, Pages: 220-230, ISSN: 0302-9743
Atopic dermatitis (AD), also known as eczema, is one of themost common chronic skin diseases. AD severity is primarily evaluatedbased on visual inspections by clinicians, but is subjective and has largeinter- and intra-observer variability in many clinical study settings. Toaid the standardisation and automating the evaluation of AD severity,this paper introduces a CNN computer vision pipeline, EczemaNet, thatfirst detects areas of AD from photographs and then makes probabilisticpredictions on the severity of the disease. EczemaNet combines trans-fer and multitask learning, ordinal classification, and ensembling overcrops to make its final predictions. We test EczemaNet using a set of im-ages acquired in a published clinical trial, and demonstrate low RMSEwith well-calibrated prediction intervals. We show the effectiveness of us-ing CNNs for non-neoplastic dermatological diseases with a medium-sizedataset, and their potential for more efficiently and objectively evaluatingAD severity, which has greater clinical relevance than mere classification.
Journal articlePrice TAR, Windbichler N, Unckless RL, et al., 2020,
Resistance to natural and synthetic gene drive systems, JOURNAL OF EVOLUTIONARY BIOLOGY, Vol: 33, Pages: 1345-1360, ISSN: 1010-061X
- Author Web Link
- Citations: 21
Journal articleCrone MA, Priestman M, Ciechonska M, et al., 2020,
A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics (vol 11, 4464, 2020), NATURE COMMUNICATIONS, Vol: 11, ISSN: 2041-1723
- Author Web Link
- Citations: 1
Journal articleBeal J, Farny NG, Haddock-Angelli T, et al., 2020,
Robust estimation of bacterial cell count from optical density, COMMUNICATIONS BIOLOGY, Vol: 3
- Author Web Link
- Citations: 47
Journal articleFrei T, Cella F, Tedeschi F, et al., 2020,
Characterization and mitigation of gene expression burden in mammalian cells, Nature Communications, Vol: 11, ISSN: 2041-1723
Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.
Journal articleCrone M, Priestman M, Ciechonska M, et al., 2020,
A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics, Nature Communications, Vol: 11, Pages: 1-11, ISSN: 2041-1723
The SARS-CoV-2 pandemic has shown how a rapid rise in demand for patient and community sample testing can quickly overwhelm testing capability globally. With most diagnostic infrastructure dependent on specialized instruments, their exclusive reagent supplies quickly become bottlenecks, creating an urgent need for approaches to boost testing capacity. We address this challenge by refocusing the London Biofoundry onto the development of alternative testing pipelines. Here, we present a reagent-agnostic automated SARS-CoV-2 testing platform that can be quickly deployed and scaled. Using an in-house-generated, open-source, MS2-virus-like particle (VLP) SARS-CoV-2 standard, we validate RNA extraction and RT-qPCR workflows as well as two detection assays based on CRISPR-Cas13a and RT-loop-mediated isothermal amplification (RT-LAMP). In collaboration with an NHS diagnostic testing lab, we report the performance of the overall workflow and detection of SARS-CoV-2 in patient samples using RT-qPCR, CRISPR-Cas13a, and RT-LAMP. The validated RNA extraction and RT-qPCR platform has been installed in NHS diagnostic labs, increasing testing capacity by 1000 samples per day.
Journal articleXu X, Li X, Liu Y, et al., 2020,
Pyruvate-responsive genetic circuits for dynamic control of central metabolism, Nature Chemical Biology, Vol: 16, Pages: 1261-1268, ISSN: 1552-4450
Dynamic regulation is a promising strategy for fine-tuning metabolic fluxes in microbial cell factories. However, few of these synthetic regulatory systems have been developed for central carbon metabolites. Here we created a set of programmable and bifunctional pyruvate-responsive genetic circuits for dynamic dual control (activation and inhibition) of central metabolism in Bacillus subtilis. We used these genetic circuits to design a feedback loop control system that relies on the intracellular concentration of pyruvate to fine-tune the target metabolic modules, leading to the glucaric acid titer increasing from 207 to 527 mg l−1. The designed logic gate-based circuits were enabled by the characterization of a new antisense transcription mechanism in B. subtilis. In addition, a further increase to 802 mg l−1 was achieved by blocking the formation of by-products. Here, the constructed pyruvate-responsive genetic circuits are presented as effective tools for the dynamic control of central metabolism of microbial cell factories.
Conference paperLankinen A, Ruiz IM, Ouldridge TE, 2020,
Implementing non-equilibrium networks with active circuits of duplex catalysts, 26th International Conference on DNA Computing and Molecular Programming (DNA 26), Publisher: Schloss Dagstuhl--Leibniz-Zentrum, Pages: 1-25
DNA strand displacement (DSD) reactions have been used to construct chemicalreaction networks in which species act catalytically at the level of theoverall stoichiometry of reactions. These effective catalytic reactions aretypically realised through one or more of the following: many-stranded gatecomplexes to coordinate the catalysis, indirect interaction between thecatalyst and its substrate, and the recovery of a distinct ``catalyst'' strandfrom the one that triggered the reaction. These facts make emulation of theout-of-equilibrium catalytic circuitry of living cells more difficult. Here, wepropose a new framework for constructing catalytic DSD networks: ActiveCircuits of Duplex Catalysts (ACDC). ACDC components are all double-strandedcomplexes, with reactions occurring through 4-way strand exchange. Catalystsdirectly bind to their substrates, and and the ``identity'' strand of thecatalyst recovered at the end of a reaction is the same molecule as the onethat initiated it. We analyse the capability of the framework to implementcatalytic circuits analogous to phosphorylation networks in living cells. Wealso propose two methods of systematically introducing mismatches within DNAstrands to avoid leak reactions and introduce driving through net base pairformation. We then combine these results into a compiler to automate theprocess of designing DNA strands that realise any catalytic network allowed byour framework.
Journal articleLv X, Zhang C, Cui S, et al., 2020,
Assembly of pathway enzymes by engineering functional membrane microdomain components for improved N-acetylglucosamine synthesis in Bacillus subtilis, Metabolic Engineering, Vol: 61, Pages: 96-105, ISSN: 1096-7176
Enzyme clustering can improve catalytic efficiency by facilitating the processing of intermediates. Functional membrane microdomains (FMMs) in bacteria can provide a platform for enzyme clustering. However, the amount of FMMs at the cell basal level is still facing great challenges in multi-enzyme immobilization. Here, using the nutraceutical N-acetylglucosamine (GlcNAc) synthesis in Bacillus subtilis as a model, we engineered FMM components to improve the enzyme assembly in FMMs. First, by overexpression of the SPFH (stomatin-prohibitin-flotillin-HflC/K) domain and YisP protein, an enzyme involved in the synthesis of squalene-derived polyisoprenoid, the membrane order of cells was increased, as verified using di-4-ANEPPDHQ staining. Then, two heterologous enzymes, GlcNAc-6-phosphate N-acetyltransferase (GNA1) and haloacid dehalogenase-like phosphatases (YqaB), required for GlcNAc synthesis were assembled into FMMs, and the GlcNAc titer in flask was increased to 8.30 ± 0.57 g/L, which was almost three times that of the control strains. Notably, FMM component modification can maintain the OD600 in stationary phase and reduce cell lysis in the later stage of fermentation. These results reveal that the improved plasma membrane ordering achieved by the engineering FMM components could not only promote the enzyme assembly into FMMs, but also improve the cell fitness.
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