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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
    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
    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
    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, Stan G-B, Bae W,

    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
    Ouldridge T, Berengut J, Kui Wong C, Berengut J, Doye J, Lee Let al.,

    Self-Limiting Polymerization of DNA Origami Subunits with Strain Accumulation

    , ACS Nano, ISSN: 1936-0851
  • 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
  • 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
    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.

    , Nat Commun, Vol: 11
  • 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
  • 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, ISSN: 2001-0370
  • Journal article
    Wang J, Ledesma-Amaro R, Wei Y, Ji B, Ji X-Jet 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.

  • Journal article
    Keck FD, Polizzi K,

    Microbial interventions as alternatives to eukaryotic engineering

    , Microbial Biotechnology, ISSN: 1751-7907
  • Conference paper
    Pan K, Hurault G, Arulkumaran K, Williams H, Tanaka Ret 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 article
    Price TAR, Windbichler N, Unckless RL, Sutter A, Runge J-N, Ross PA, Pomiankowski A, Nuckolls NL, Montchamp-Moreau C, Mideo N, Martin OY, Manser A, Legros M, Larracuente AM, Holman L, Godwin J, Gemmell N, Courret C, Buchman A, Barrett LG, Lindholm AKet al., 2020,

    Resistance to natural and synthetic gene drive systems

    , JOURNAL OF EVOLUTIONARY BIOLOGY, Vol: 33, Pages: 1345-1360, ISSN: 1010-061X
  • Journal article
    Niu T, Lv X, Liu Y, Li J, Du G, Ledesma-Amaro R, Liu Let al., 2020,

    The elucidation of phosphosugar stress response in Bacillus subtilis guides strain engineering for high N-acetylglucosamine production.

    , Biotechnology and Bioengineering, 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 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,

    Robust estimation of bacterial cell count from optical density.

    , Commun Biol, Vol: 3

    Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals  <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.

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