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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 article
    Sarvari P, Ingram D, Stan G-B,

    A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs

    , Biology, ISSN: 2079-7737
  • Journal article
    Vidal LS, Ayala R, Stan G-B, Ledesma Amaro Ret al.,

    rfaRm: an R client-side interface to facilitate the analysis of the Rfam database of RNA families: Automated identification and annotation of non-coding RNA

    , PLoS One, ISSN: 1932-6203
  • Journal article
    Aw R, De Wachter C, Laukens B, De Rycke R, De Bruyne M, Bell D, Callewaert N, Polizzi KMet al., 2020,

    Knockout of RSN1 , TVP18 or CSC1 ‐2 causes perturbation of Golgi cisternae in Pichia pastoris

    , Traffic, ISSN: 1398-9219

    The structural organization of the Golgi stacks in mammalian cells is intrinsically linked to function, including glycosylation, but the role of morphology is less clear in lower eukaryotes. Here we investigated the link between the structural organization of the Golgi and secretory pathway function using Pichia pastoris as a model system. To unstack the Golgi cisternae, we disrupted 18 genes encoding proteins in the secretory pathway without loss of viability. Using biosensors, confocal microscopy and transmission electron microscopy we identified three strains with irreversible perturbations in the stacking of the Golgi cisternae, all of which had disruption in genes that encode proteins with annotated function as or homology to calcium/calcium permeable ion channels. Despite this, no variation in the secretory pathway for ER size, whole cell glycomics or recombinant protein glycans was observed. Our investigations showed the robust nature of the secretory pathway in P. pastoris and suggest that Ca2+ concentration, homeostasis or signalling may play a significant role for Golgi stacking in this organism and should be investigated in other organisms.

  • Journal article
    Aw 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 article
    Deshpande 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 article
    He Q, Szczepańska P, Yuzbashev T, Lazar Z, Ledesma-Amaro Ret 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 article
    Kuntz Nussio J, Thomas P, Stan G, Barahona Met al., 2020,

    Approximations of countably-infinite linear programs over bounded measure spaces

    , SIAM Journal on Optimization, ISSN: 1052-6234
  • Journal article
    Ouldridge T, Berengut J, Kui Wong C, Berengut J, Doye J, Lee Let al., 2020,

    Self-Limiting Polymerization of DNA Origami Subunits with Strain Accumulation

    , ACS Nano, ISSN: 1936-0851
  • Journal article
    Ouldridge T, Stan G-B, Bae W, 2020,

    In situ generation of RNA complexes for synthetic molecular strand displacement circuits in autonomous systems

    , Nano Letters: a journal dedicated to nanoscience and nanotechnology, ISSN: 1530-6984
  • Journal article
    Moore SJ, Lai H-E, Chee S-M, Toh M, Coode S, Capel P, Corre C, de los Santos ELC, Freemont PSet al., 2020,

    A Streptomyces venezuelae 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 article
    Miyano T, Tanaka R, 2020,

    Identification of keratinocyte subpopulations in transcriptome to evaluate drug effects in atopic dermatitis

    , British Journal of Dermatology, ISSN: 0007-0963
  • Journal article
    Moya-Ramirez I, Bouton C, Kontoravdi C, Polizzi Ket al., 2020,

    High resolution biosensor to test the capping level and integrity of mRNAs

    , Nucleic Acids Research, ISSN: 0305-1048

    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 article
    Liu Y, Su A, Li J, Ledesma-Amaro R, Xu P, Du G, Liu Let 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 article
    Hurault G, Domínguez-Hüttinger E, Langan S, Williams H, Tanaka Ret 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 article
    Beal J, Farny NG, Haddock-Angelli T, Selvarajah V, Baldwin GS, Buckley-Taylor R, Gershater M, Kiga D, Marken J, Sanchania V, Sison A, Workman CT, iGEM Interlab Study Contributorset al., 2020,

    Author Correction: Robust estimation of bacterial cell count from optical density.

    , Commun Biol, Vol: 3

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  • Journal article
    Keck FD, Polizzi K, 2020,

    Microbial interventions are an easier alternative to engineer higher organisms

    , Microbial Biotechnology, ISSN: 1751-7907

    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 article
    Antonakoudis A, Barbosa R, Kotidis P, Kontoravdi Ket 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 article
    Gilbert C, Tang T-C, Ott W, Dorr B, Shaw W, Sun G, Lu T, Ellis Tet al.,

    Living materials with programmable functionalities grown from engineered microbial co-cultures

    , Nature Materials, ISSN: 1476-1122

    Biological systems assemble living materials that are autonomously patterned, can self-repair and can sense and respond to their environment. The field of engineered living materials aims to create novel materials with properties similar to those of natural biomaterials using genetically-engineered organisms. Here we describe an approach to fabricate functional bacterial cellulose-based living materials using a stable co-culture of Saccharomyces cerevisiae yeast and bacterial cellulose-producing Komagataeibacter rhaeticus bacteria. Yeast strains can be engineered to secrete enzymes into bacterial cellulose, generating autonomously grown catalytic materials and enabling DNA-encoded modification of bacterial cellulose bulk properties. Alternatively, engineered yeast can be incorporated within the growing cellulose matrix, creating living materials that can sense and respond to chemical and optical stimuli. This symbiotic culture of bacteria and yeast is a flexible platform for the production of bacterial cellulosed-based engineered living materials with potential applications in biosensing and biocatalysis.

  • Journal article
    Murray 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 article
    Meng 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.

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