guy poncing

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.


Search or filter publications

Filter by type:

Filter by publication type

Filter by year:



  • Showing results for:
  • Reset all filters

Search results

  • Journal article
    Mannan AA, Liu D, Zhang F, Oyarzun DAet al., 2017,

    Fundamental design principles for transcription-factor-based metabolite biosensors

    , ACS Synthetic Biology, Vol: 6, Pages: 1851-1859, ISSN: 2161-5063

    Metabolite biosensors are central to current efforts toward precision engineering of metabolism. Although most research has focused on building new biosensors, their tunability remains poorly understood and is fundamental for their broad applicability. Here we asked how genetic modifications shape the dose–response curve of biosensors based on metabolite-responsive transcription factors. Using the lac system in Escherichia coli as a model system, we built promoter libraries with variable operator sites that reveal interdependencies between biosensor dynamic range and response threshold. We developed a phenomenological theory to quantify such design constraints in biosensors with various architectures and tunable parameters. Our theory reveals a maximal achievable dynamic range and exposes tunable parameters for orthogonal control of dynamic range and response threshold. Our work sheds light on fundamental limits of synthetic biology designs and provides quantitative guidelines for biosensor design in applications such as dynamic pathway control, strain optimization, and real-time monitoring of metabolism.

  • Journal article
    Misirli G, Madsen C, Sainz de Murieta I, Bultelle M, Flanagan K, Pocock M, Halllinan J, McLaughlin J, Clark-Casey J, Lyne M, Micklem G, Stan G, Kitney R, Wipat Aet al., 2017,

    Constructing synthetic biology workflows in the cloud

    , Engineering Biology, Vol: 1, Pages: 61-65, ISSN: 2398-6182

    The synthetic biology design process has traditionally been heavily dependent upon manual searching, acquisition and integration of existing biological data. A large amount of such data is already available from Internet-based resources, but data exchange between these resources is often undertaken manually. Automating the communication between different resources can be done by the generation of computational workflows to achieve complex tasks that cannot be carried out easily or efficiently by a single resource. Computational workflows involve the passage of data from one resource, or process, to another in a distributed computing environment. In a typical bioinformatics workflow, the predefined order in which processes are invoked in a synchronous fashion and are described in a workflow definition document. However, in synthetic biology the diversity of resources and manufacturing tasks required favour a more flexible model for process execution. Here, the authors present the Protocol for Linking External Nodes (POLEN), a Cloud-based system that facilitates synthetic biology design workflows that operate asynchronously. Messages are used to notify POLEN resources of events in real time, and to log historical events such as the availability of new data, enabling networks of cooperation. POLEN can be used to coordinate the integration of different synthetic biology resources, to ensure consistency of information across distributed repositories through added support for data standards, and ultimately to facilitate the synthetic biology life cycle for designing and implementing biological systems.

  • Journal article
    Piotrowska I, Isalan M, Mielcarek ML, 2017,

    Early transcriptional alteration of histone deacetylases in a murine model of doxorubicin-induced cardiomyopathy

    , PLOS One, Vol: 12, ISSN: 1932-6203

    Doxorubicin is a potent chemotherapeutic agent that is widely-used to treat a variety of cancers but causes acute and chronic cardiac injury, severely limiting its use. Clinically, the acute side effects of doxorubicin are mostly manageable, whereas the delayed consequences can lead to life-threatening heart failure, even decades after cancer treatment. The cardiotoxicity of doxorubicin is subject to a critical cumulative dose and so dosage limitation is considered to be the best way to reduce these effects. Hence, a number of studies have defined a “safe dose” of the drug, both in animal models and clinical settings, with the aim of avoiding long-term cardiac effects. Here we show that a dose generally considered as safe in a mouse model can induce harmful changes in the myocardium, as early as 2 weeks after infusion. The adverse changes include the development of fibrotic lesions, disarray of cardiomyocytes and a major transcription dysregulation. Importantly, low-dose doxorubicin caused specific changes in the transcriptional profile of several histone deacetylases (HDACs) which are epigenetic regulators of cardiac remodelling. This suggests that cardioprotective therapies, aimed at modulating HDACs during doxorubicin treatment, deserve further exploration.

  • Journal article
    Smith WD, Bardin E, Cameron L, Edmondson CL, Farrant KV, Martin I, Murphy RA, Soren O, Turnbull AR, Wierre-Gore N, Alton EW, Bundy JG, Bush A, Connett GJ, Faust SN, Filloux A, Freemont PS, Jones AL, Takats Z, Webb JS, Williams HD, Davies JCet al., 2017,

    Current and future therapies for Pseudomonas aeruginosa infection in patients with cystic fibrosis

    , FEMS Microbiology Letters, Vol: 364, ISSN: 0378-1097

    Pseudomonas aeruginosa opportunistically infects the airways of patients with cystic fibrosis and causes significant morbidity and mortality. Initial infection can often be eradicated though requires prompt detection and adequate treatment. Intermittent and then chronic infection occurs in the majority of patients. Better detection of P. aeruginosa infection using biomarkers may enable more successful eradication before chronic infection is established. In chronic infection P. aeruginosa adapts to avoid immune clearance and resist antibiotics via efflux pumps, β-lactamase expression, reduced porins and switching to a biofilm lifestyle. The optimal treatment strategies for P. aeruginosa infection are still being established, and new antibiotic formulations such as liposomal amikacin, fosfomycin in combination with tobramycin and inhaled levofloxacin are being explored. Novel agents such as the alginate oligosaccharide OligoG, cysteamine, bacteriophage, nitric oxide, garlic oil and gallium may be useful as anti-pseudomonal strategies, and immunotherapy to prevent infection may have a role in the future. New treatments that target the primary defect in cystic fibrosis, recently licensed for use, have been associated with a fall in P. aeruginosa infection prevalence. Understanding the mechanisms for this could add further strategies for treating P. aeruginosa in future.

  • Journal article
    Moore SJ, macdonald JT, freemont PS, 2017,

    Cell-free synthetic biology for in vitro prototype engineering

    , Biochemical Society Transactions, Vol: 45, Pages: 785-791, ISSN: 1470-8752

    Cell-free transcription–translation is an expandingfield in synthetic biology as a rapidprototyping platform for blueprinting the design of synthetic biological devices. Exemplarefforts include translation of prototype designs into medical test kits for on-site identifica-tion of viruses (Zika and Ebola), while gene circuit cascades can be tested, debuggedand re-designed within rapid turnover times. Coupled with mathematical modelling, thisdiscipline lends itself towards the precision engineering of new synthetic life. The nextstages of cell-free look set to unlock new microbial hosts that remain slow to engineerand unsuited to rapid iterative design cycles. It is hoped that the development of suchsystems will provide new tools to aid the transition from cell-free prototype designs tofunctioning synthetic genetic circuits and engineered natural product pathways in livingcells.

  • Journal article
    Dekker L, Polizzi KM, 2017,

    Sense and sensitivity in bioprocessing-detecting cellular metabolites with biosensors.

    , Current Opinion in Chemical Biology, Vol: 40, Pages: 31-36, ISSN: 1367-5931

    Biosensors use biological elements to detect or quantify an analyte of interest. In bioprocessing, biosensors are employed to monitor key metabolites. There are two main types: fully biological systems or biological recognition coupled with physical/chemical detection. New developments in chemical biosensors include multiplexed detection using microfluidics. Synthetic biology can be used to engineer new biological biosensors with improved characteristics. Although there have been few biosensors developed for bioprocessing thus far, emerging trends can be applied in the future. A range of new platform technologies will enable rapid engineering of new biosensors based on transcriptional activation, riboswitches, and Förster Resonance Energy Transfer. However, translation to industry remains a challenge and more research into the robustness biosensors at scale is needed.

  • Journal article
    Awan AR, Blount BA, Bell DJ, Shaw WM, Ho JCH, McKiernan RM, Ellis Tet al., 2017,

    Biosynthesis of the antibiotic nonribosomal peptide penicillin in baker's yeast

    , Nature Communications, Vol: 8, ISSN: 2041-1723

    Fungi are a valuable source of enzymatic diversity and therapeutic natural products includingantibiotics. Here we engineer the baker’s yeastSaccharomyces cerevisiaeto produce andsecrete the antibiotic penicillin, a beta-lactam nonribosomal peptide, by taking genes from afilamentous fungus and directing their efficient expression and subcellular localization. Usingsynthetic biology tools combined with long-read DNA sequencing, we optimize productivityby 50-fold to produce bioactive yields that allow spentS. cerevisiaegrowth media to haveantibacterial action againstStreptococcusbacteria. This work demonstrates thatS. cerevisiaecan be engineered to perform the complex biosynthesis of multicellular fungi, opening up thepossibility of using yeast to accelerate rational engineering of nonribosomal peptideantibiotics.

  • Journal article
    Scholes NS, Isalan M, 2017,

    A three-step framework for programming pattern formation

    , Current Opinion in Chemical Biology, Vol: 40, Pages: 1-7, ISSN: 1879-0402

    The spatial organisation of gene expression is essential to create structure and function in multicellular organisms during developmental processes. Such organisation occurs by the execution of algorithmic functions, leading to patterns within a given domain, such as a tissue. Engineering these processes has become increasingly important because bioengineers are seeking to develop tissues ex vivo. Moreover, although there are several theories on how pattern formation can occur in vivo, the biological relevance and biotechnological potential of each of these remains unclear. In this review, we will briefly explain four of the major theories of pattern formation in the light of recent work. We will explore why programming of such patterns is necessary, while discussing a three-step framework for artificial engineering approaches.

  • Journal article
    McClymont DW, Freemont PS, 2017,

    With all due respect to Maholo, lab automation isn't anthropomorphic

    , NATURE BIOTECHNOLOGY, Vol: 35, Pages: 312-314, ISSN: 1087-0156
  • Journal article
    Walker BJ, stan GB, Polizzi KM, 2017,

    Intracellular delivery of biologic therapeutics by bacterial secretion systems

    , Expert Reviews in Molecular Medicine, Vol: 19, ISSN: 1462-3994

    Biologics are a promising new class of drugs based on complex macromolecules such as proteins and nucleic acids. However, delivery of these macromolecules into the cytoplasm of target cells remains a significant challenge. Here we present one potential solution: bacterial nanomachines that have evolved over millions of years to efficiently deliver proteins and nucleic acids across cell membranes and between cells. In this review, we provide a brief overview of the different bacterial systems capable of direct delivery into the eukaryotic cytoplasm and the medical applications for which they are being investigated, along with a perspective on the future directions of this exciting field.

  • Journal article
    Aw, McKay, Shattock, Polizzi KMet al., 2017,

    Expressing anti-HIV VRC01 antibody using the murine IgG1 secretion signal in Pichia pastoris

    , AMB Express, Vol: 7, ISSN: 2191-0855

    The use of the recombinant expression platform Pichia pastoris to produce pharmaceutically important proteins has been investigated over the past 30 years. Compared to mammalian cultures, expression in P. pastoris is cheaper and faster, potentially leading to decreased costs and process development times. Product yields depend on a number of factors including the secretion signal chosen for expression, which can influence the host cell response to recombinant protein production. VRC01, a broadly neutralising anti-HIV antibody, was expressed in P. pastoris, using the methanol inducible AOX1 promoter for both the heavy and light chains. Titre reached up to 3.05 μg mL-1 in small scale expression. VRC01 was expressed using both the α-mating factor signal peptide from Saccharomyces cerevisiae and the murine IgG1 signal peptide. Surprisingly using the murine IgG1 signal peptide resulted in higher yield of antibody capable of binding gp140 antigen. Furthermore, we evaluated levels of secretory stress compared to the untransformed wild-type strain and show a reduced level of secretory stress in the murine IgG1 signal peptide strains versus those containing the α-MF signal peptide. As bottlenecks in the secretory pathway are often the limiting factor in protein secretion, reduced levels of secretory stress and the higher yield of functional antibody suggest the murine IgG1 signal peptide may lead to better protein folding and secretion. This work indicates the possibilities for utilising the murine IgG1 signal peptide for a range of antibodies, resulting in high yields and reduced cellular stress.

  • Journal article
    Sou SN, Jedrzejewski PM, Lee K, Sellick C, Polizzi KM, Kontoravdi Cet al., 2017,

    Model-based investigation of intracellular processes determining antibody Fc-glycosylation under mild hypothermia

    , Biotechnology and Bioengineering, Vol: 114, Pages: 1570-1582, ISSN: 1097-0290

    Despite the positive effects of mild hypothermic conditions on monoclonal antibody (mAb) productivity (qmAb) during mammalian cell culture, the impact of reduced culture temperature on mAb Fc-glycosylation and the mechanism behind changes in the glycan composition is not fully established. The lack of knowledge about the regulation of dynamic intracellular processes under mild hypothermia restricts bioprocess optimisation. To address this issue, a mathematical model that quantitatively describes CHO cell behaviour and metabolism, mAb synthesis and its N-linked glycosylation profiles before and after the induction of mild hypothermia is constructed using two sets of parameters. Results from this study show that the model is capable of representing experimental results well in all of the aspects mentioned above, including the N-linked glycosylation profile of mAb produced under mild hypothermia. Most importantly, comparison between model simulation results for different culture temperatures suggest the reduced rates of nucleotide sugar donor production and galactosyltransferase (GalT) expression to be critical contributing factors that determine the variation in Fc-glycan profiles between physiological and mild hypothermic conditions in stable CHO transfectants. This is then confirmed using experimental measurements of GalT expression levels, thereby closing the loop between the experimental and the computational system. The identification of bottlenecks within CHO cell metabolism under mild hypothermic conditions will aid bioprocess optimisation, e.g., by tailoring feeding stradegies to improve NSD production, or manipulating the expression of specific glycosyltransferases through cell line engineering.

  • Journal article
    Mielcarek M, Smolenski RT, Isalan M, 2017,

    Transcriptional signature of an altered purine metabolism in the skeletal muscle of a Huntington’s disease mouse model

    , Frontiers in Physiology, Vol: 8, ISSN: 1664-042X

    Huntington’s disease (HD) is a fatal neurodegenerative disorder,caused by a polyglutamine expansion in the huntingtin protein (HTT).HD has a peripheral component to its pathology: skeletal musclesare severely affected, leading to atrophy and malfunction in both pre-clinical and clinical settings. We previously used two symptomatic HD mouse models to demonstrate the impairment of the contractile characteristics of the hind limb muscles, which was accompanied by a significant loss of function of motor units. The mice displayed a significant reduction in muscle force, likely because of deteriorationsin energy metabolism, decreased oxidation and altered purine metabolism. There is growing evidence suggesting that HD-related skeletal muscle malfunction might be partially or completely independent of CNS degeneration. The pathology might arise from mutant HTT within muscle (loss or gain of function). Hence, it is vital to identify novel peripheral biomarkers that will reflect HD skeletal muscle atrophy. These will be important for upcoming clinical trials that may target HD peripherally. In order to identify potential biomarkers that might reflect muscle metabolic changes, we used qPCR to validate key gene transcripts in different skeletal muscle types. Consequently, we report a number of transcript alterations that are linked to HD muscle pathology.

  • Journal article
    Moore SJ, Lai HE, Needham H, Polizzi KM, Freemont PSet al., 2017,

    Streptomyces venezuelae TX-TL - a next generation cell-free synthetic biology tool

    , Biotechnology Journal, Vol: 12, ISSN: 1860-7314

    Streptomyces venezuelae is a promising chassis in synthetic biology for fine chemical and secondary metabolite pathway engineering. The potential of S. venezuelae could be further realized by expanding its capability with the introduction of its own in vitro transcription-translation (TX-TL) system. TX-TL is a fast and expanding technology for bottom-up design of complex gene expression tools, biosensors and protein manufacturing. Herein, we introduce a S. venezuelae TX-TL platform by reporting a streamlined protocol for cell-extract preparation, demonstrating high-yield synthesis of a codon-optimized sfGFP reporter and the prototyping of a synthetic tetracycline-inducible promoter in S. venezuelae TX-TL based on the tetO-TetR repressor system. The aim of this system is to provide a host for the homologous production of exotic enzymes from Actinobacteria secondary metabolism in vitro. As an example, the authors demonstrate the soluble synthesis of a selection of enzymes (12-70 kDa) from the Streptomyces rimosus oxytetracycline pathway.

  • Journal article
    Gilbert C, Howarth M, Harwood CR, Ellis Tet al., 2017,

    Extracellular self-assembly of functional and tunable protein conjugates from Bacillus subtilis

    , ACS Synthetic Biology, Vol: 6, Pages: 957-967, ISSN: 2161-5063

    The ability to stably and specifically conjugate recombinant proteins to one another is a powerful approach for engineering multifunctional enzymes, protein therapeutics, and novel biological materials. While many of these applications have been illustrated through in vitro and in vivo intracellular protein conjugation methods, extracellular self-assembly of protein conjugates offers unique advantages: simplifying purification, reducing toxicity and burden, and enabling tunability. Exploiting the recently described SpyTag-SpyCatcher system, we describe here how enzymes and structural proteins can be genetically encoded to covalently conjugate in culture media following programmable secretion from Bacillus subtilis. Using this approach, we demonstrate how self-conjugation of a secreted industrial enzyme, XynA, dramatically increases its resilience to boiling, and we show that cellular consortia can be engineered to self-assemble functional protein–protein conjugates with tunable composition. This novel genetically encoded modular system provides a flexible strategy for protein conjugation harnessing the substantial advantages of extracellular self-assembly.

  • Journal article
    Goers L, Ainsworth C, Goey CH, Kontoravdi, Freemont PS, Polizzi KMet al., 2017,

    Whole-cell Escherichia coli lactate biosensor for monitoring mammalian cell cultures during biopharmaceutical production

    , Biotechnology and Bioengineering, Vol: 114, Pages: 1290-1300, ISSN: 1097-0290

    Many high-value added recombinant proteins, such as therapeutic glycoproteins, are produced using mammalian cell cultures. In order to optimise the productivity of these cultures it is important to monitor cellular metabolism, for example the utilisation of nutrients and the accumulation of metabolic waste products. One metabolic waste product of interest is lactic acid (lactate), overaccumulation of which can decrease cellular growth and protein production. Current methods for the detection of lactate are limited in terms of cost, sensitivity, and robustness. Therefore, we developed a whole-cell Escherichia coli lactate biosensor based on the lldPRD operon and successfully used it to monitor lactate concentration in mammalian cell cultures. Using real samples and analytical validation we demonstrate that our biosensor can be used for absolute quantification of metabolites in complex samples with high accuracy, sensitivity and robustness. Importantly, our whole-cell biosensor was able to detect lactate at concentrations more than two orders of magnitude lower than the industry standard method, making it useful for monitoring lactate concentrations in early phase culture. Given the importance of lactate in a variety of both industrial and clinical contexts we anticipate that our whole-cell biosensor can be used to address a range of interesting biological questions. It also serves as a blueprint for how to capitalise on the wealth of genetic operons for metabolite sensing available in Nature for the development of other whole-cell biosensors.

  • Journal article
    Webb AJ, Kelwick R, Freemont PS, 2017,

    Opportunities for applying whole-cell bioreporters towards parasite detection

    , Microbial Biotechnology, Vol: 10, Pages: 244-249, ISSN: 1751-7915
  • Conference paper
    Foo M, Sawlekar R, Kim J, Bates DG, Stan G-B, Kulkarni Vet al., 2017,

    Biomolecular implementation of nonlinear system theoretic operators

    , European Control Conference (ECC), Publisher: IEEE, Pages: 1824-1831

    Synthesis of biomolecular circuits for controlling molecular-scale processes is an important goal of synthetic biology with a wide range of in vitro and in vivo applications, including biomass maximization, nanoscale drug delivery, and many others. In this paper, we present new results on how abstract chemical reactions can be used to implement commonly used system theoretic operators such as the polynomial functions, rational functions and Hill-type nonlinearity. We first describe how idealised versions of multi-molecular reactions, catalysis, annihilation, and degradation can be combined to implement these operators. We then show how such chemical reactions can be implemented using enzyme-free, entropy-driven DNA reactions. Our results are illustrated through three applications: (1) implementation of a Stan-Sepulchre oscillator, (2) the computation of the ratio of two signals, and (3) a PI+antiwindup controller for regulating the output of a static nonlinear plant.

  • Conference paper
    Pan W, Menolascina F, Stan G, 2016,

    Online Model Selection for Synthetic Gene Networks

    , IEEE Conference on Decision and Control, Publisher: IEEE

    Control algorithms combined with microfluidicdevices and microscopy have enabled in vivo real-time controlof protein expression in synthetic gene networks. Most controlalgorithms rely on the a priori availability of mathematicalmodels of the gene networks to be controlled. These modelsare typically black/grey box models, which can be obtainedthrough the use of data-driven techniques developed in thecontext of systems identification. Data-driven inference of bothmodel structure and parameters is the main focus of thispaper. There are two main challenges associated with theinference of dynamical models for real-time control of generegulatory networks in living cells. Since biological systemsare typically evolving over time, the first challenge stemsfrom the fact that model selection needs to be done online,which prevents the application of computationally expensiveidentification algorithms iterating through large amounts ofstreaming data. The second challenge consists in performingnonlinear model selection, which is typically too burdensomefor Kalman filtering related techniques due the heterogeneityand nonlinearity of the candidate models. In this paper,we combine sparse Bayesian techniques with classic Kalmanfiltering techniques to tackle these challenges

  • Journal article
    Kuntz J, Ottobre M, Stan G-B, Barahona Met al., 2016,

    Bounding stationary averages of polynomial diffusions via semidefinite programming

    , SIAM Journal on Scientific Computing, Vol: 38, Pages: A3891-A3920, ISSN: 1095-7197

    We introduce an algorithm based on semidefinite programming that yields increasing (resp.decreasing) sequences of lower (resp. upper) bounds on polynomial stationary averages of diffusionswith polynomial drift vector and diffusion coefficients. The bounds are obtained byoptimising an objective, determined by the stationary average of interest, over the set of realvectors defined by certain linear equalities and semidefinite inequalities which are satisfied bythe moments of any stationary measure of the diffusion. We exemplify the use of the approachthrough several applications: a Bayesian inference problem; the computation of Lyapunov exponentsof linear ordinary differential equations perturbed by multiplicative white noise; and areliability problem from structural mechanics. Additionally, we prove that the bounds convergeto the infimum and supremum of the set of stationary averages for certain SDEs associated withthe computation of the Lyapunov exponents, and we provide numerical evidence of convergencein more general settings.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=991&limit=20&page=6&respub-action=search.html Current Millis: 1601205127736 Current Time: Sun Sep 27 12:12:07 BST 2020