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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
    Blount BA, Driessen MRM, Ellis T, 2016,

    GC Preps: Fast and Easy Extraction of Stable Yeast Genomic DNA

    , Scientific Reports, Vol: 6, ISSN: 2045-2322

    Existing yeast genomic DNA extraction methods are not ideally suited to extensive screening of colonies by PCR, due to being too lengthy, too laborious or yielding poor quality DNA and inconsistent results. We developed the GC prep method as a solution to this problem. Yeast cells from colonies or liquid cultures are lysed by vortex mixing with glass beads and then boiled in the presence of a metal chelating resin. In around 12 minutes, multiple samples can be processed to extract high yields of genomic DNA. These preparations perform as effectively in PCR screening as DNA purified by organic solvent methods, are stable for up to 1 year at room temperature and can be used as the template for PCR amplification of fragments of at least 8 kb.

  • Journal article
    Webb AJ, Kelwick R, Doenhoff MJ, Kylilis N, MacDonald J, Wen KY, Mckeown C, Baldwin G, Ellis T, Jensen K, Freemont PSet al., 2016,

    A protease-based biosensor for the detection of schistosome cercariae

    , Scientific Reports, Vol: 6, ISSN: 2045-2322

    Parasitic diseases affect millions of people worldwide, causing debilitating illnesses anddeath. Rapid and cost-effective approaches to detect parasites are needed, especially inresource-limited settings. A common signature of parasitic diseases is the release of specificproteases by the parasites at multiple stages during their life cycles. To this end, weengineered several modular Escherichia coli and Bacillus subtilis whole-cell-basedbiosensors which incorporate an interchangeable protease recognition motif into theirdesigns. Herein, we describe how several of our engineered biosensors have been applied todetect the presence and activity of elastase, an enzyme released by the cercarial larvae stageof Schistosoma mansoni. Collectively, S. mansoni and several other schistosomes areresponsible for the infection of an estimated 200 million people worldwide. Since ourbiosensors are maintained in lyophilised cells, they could be applied for the detection of S.mansoni and other parasites in settings without reliable cold chain access.

  • Journal article
    Awan AR, Shaw WM, Ellis T, 2016,

    Biosynthesis of therapeutic natural products using synthetic biology

    , Advanced Drug Delivery Reviews, Vol: 105, Pages: 96-106, ISSN: 1872-8294

    Natural products are a group of bioactive structurally diverse chemicals produced by microorganisms and plants. These molecules and their derivatives have contributed to over a third of the therapeutic drugs produced in the last century. However, over the last few decades traditional drug discovery pipelines from natural products have become far less productive and far more expensive. One recent development with promise to combat this trend is the application of synthetic biology to therapeutic natural product biosynthesis. Synthetic biology is a young discipline with roots in systems biology, genetic engineering, and metabolic engineering. In this review, we discuss the use of synthetic biology to engineer improved yields of existing therapeutic natural products. We further describe the use of synthetic biology to combine and express natural product biosynthetic genes in unprecedented ways, and how this holds promise for opening up completely new avenues for drug discovery and production.

  • Journal article
    Florea M, Reeve B, Abbott J, Freemont PS, Ellis Tet al., 2016,

    Genome sequence and plasmid transformation of the model high-yield bacterial cellulose producer Gluconacetobacter hansenii ATCC 53582.

    , Scientific Reports, Vol: 6, ISSN: 2045-2322

    Bacterial cellulose is a strong, highly pure form of cellulose that is used in a range of applications in industry, consumer goods and medicine. Gluconacetobacter hansenii ATCC 53582 is one of the highest reported bacterial cellulose producing strains and has been used as a model organism in numerous studies of bacterial cellulose production and studies aiming to increased cellulose productivity. Here we present a high-quality draft genome sequence for G. hansenii ATCC 53582 and find that in addition to the previously described cellulose synthase operon, ATCC 53582 contains two additional cellulose synthase operons and several previously undescribed genes associated with cellulose production. In parallel, we also develop optimized protocols and identify plasmid backbones suitable for transformation of ATCC 53582, albeit with low efficiencies. Together, these results provide important information for further studies into cellulose synthesis and for future studies aiming to genetically engineer G. hansenii ATCC 53582 for increased cellulose productivity.

  • Journal article
    Yu N, Nützmann HW, MacDonald JT, Moore B, Field B, Berriri S, Trick M, Rosser SJ, Kumar SV, Freemont PS, Osbourn Aet al., 2016,

    Delineation of metabolic gene clusters in plant genomes by chromatin signatures.

    , Nucleic Acids Research, Vol: 44, Pages: 2255-2265, ISSN: 1362-4962

    Plants are a tremendous source of diverse chemicals, including many natural product-derived drugs. It has recently become apparent that the genes for the biosynthesis of numerous different types of plant natural products are organized as metabolic gene clusters, thereby unveiling a highly unusual form of plant genome architecture and offering novel avenues for discovery and exploitation of plant specialized metabolism. Here we show that these clustered pathways are characterized by distinct chromatin signatures of histone 3 lysine trimethylation (H3K27me3) and histone 2 variant H2A.Z, associated with cluster repression and activation, respectively, and represent discrete windows of co-regulation in the genome. We further demonstrate that knowledge of these chromatin signatures along with chromatin mutants can be used to mine genomes for cluster discovery. The roles of H3K27me3 and H2A.Z in repression and activation of single genes in plants are well known. However, our discovery of highly localized operon-like co-regulated regions of chromatin modification is unprecedented in plants. Our findings raise intriguing parallels with groups of physically linked multi-gene complexes in animals and with clustered pathways for specialized metabolism in filamentous fungi.

  • Conference paper
    Pan W, Yuan Y, Ljung L, Gonçalves JM, Stan G-Bet al., 2016,

    Identifying biochemical reaction networks from heterogeneous datasets

    , 2015 IEEE 54th Annual Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 2525-2530

    In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.

  • Journal article
    Aw R, Polizzi KM, 2016,

    Liquid PTVA: A faster and cheaper alternative for generating multi-copy clones in Pichia pastoris

    , Microbial Cell Factories, Vol: 15, ISSN: 1475-2859

    BACKGROUND:Multiple cognate gene copy clones have often been used in order to increase the yield of recombinant protein expression in the yeast Pichia pastoris. The method of posttransformational vector amplification (PTVA) has allowed for the efficient generation of multi-copy clones in P. pastoris. However, despite its relative ease and success, this process can be expensive and time consuming.RESULTS:We have developed a modified version of PTVA, called Liquid PTVA, which allows for faster and cheaper selection of multi-copy clones. Cultures are grown in liquid medium with only a final selection carried out on agar plates, reducing overall antibiotic usage and increasing the speed of clone amplification. In addition, it was established that starting PTVA with a single copy clone resulted in higher copy number strains for both traditional plate PTVA and liquid PTVA. Furthermore, using the Zeocin selection marker in liquid PTVA results in strains with higher growth rates, which could be beneficial for recombinant protein production processes.CONCLUSIONS:We present a methodology for creating multi-copy clones that can be achieved over 12 days instead of the traditional 45 and at approximately half the cost.

  • Journal article
    Ciechonska M, Grob A, Isalan M, 2016,

    From noise to synthetic nucleoli: can synthetic biology achieve new insights?

    , Integrative Biology, Vol: 8, Pages: 383-393, ISSN: 1757-9708

    Synthetic biology aims to re-organise and control biological components to make functional devices. Along the way, the iterative process of designing and testing gene circuits has the potential to yield many insights into the functioning of the underlying chassis of cells. Thus, synthetic biology is converging with disciplines such as systems biology and even classical cell biology, to give a new level of predictability to gene expression, cell metabolism and cellular signalling networks. This review gives an overview of the contributions that synthetic biology has made in understanding gene expression, in terms of cell heterogeneity (noise), the coupling of growth and energy usage to expression, and spatiotemporal considerations. We mainly compare progress in bacterial and mammalian systems, which have some of the most-developed engineering frameworks. Overall, one view of synthetic biology can be neatly summarised as “creating in order to understand.”

  • Conference paper
    Kopniczky M, Jensen K, Freemont P, 2016,

    Introducing the human cell-free TX-TL system as a new prototyping platform for mammalian synthetic biology

  • Conference paper
    Kelwick R, Webb AJ, Macdonald JT, Freemont PSet al., 2016,

    Development of a bacillus subtilis cell-free transcriptiontranslation system

  • Conference paper
    Polizzi KM, Freemont PS, 2016,

    Synthetic biology biosensors for healthcare and industrial biotechnology applications

  • Journal article
    Sootla A, Oyarzun DA, Angeli D, Stan GBet al., 2016,

    Shaping Pulses to Control Bistable Systems: Analysis, Computation and Counterexamples

    , Automatica, Vol: 63, Pages: 254-264, ISSN: 1873-2836

    In this paper we study how to shape temporal pulses to switch a bistable system between its stable steady states. Our motivation forpulse-based control comes from applications in synthetic biology, where it is generally difficult to implement real-time feedback controlsystems due to technical limitations in sensors and actuators. We show that for monotone bistable systems, the estimation of the set ofall pulses that switch the system reduces to the computation of one non-increasing curve. We provide an efficient algorithm to computethis curve and illustrate the results with a genetic bistable system commonly used in synthetic biology. We also extend these results tomodels with parametric uncertainty and provide a number of examples and counterexamples that demonstrate the power and limitationsof the current theory. In order to show the full potential of the framework, we consider the problem of inducing oscillations in a monotonebiochemical system using a combination of temporal pulses and event-based control. Our results provide an insight into the dynamics ofbistable systems under external inputs and open up numerous directions for future investigation.

  • Conference paper
    Reeve AB, Petkiewicz S, Hagemann H, Santosa G, Florea M, Ellis Tet al., 2016,

    Modified bacterial nanocellulose as a bioadsorbent material

  • Journal article
    Pan W, Yuan Y, Goncalves J, Stan G-Bet al., 2015,

    A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems

    , IEEE Transactions on Automatic Control, Vol: 61, Pages: 182-187, ISSN: 1558-2523

    This technical note considers the identification ofnonlinear discrete-time systems with additive process noise butwithout measurement noise. In particular, we propose a methodand its associated algorithm to identify the system nonlinear functionalforms and their associated parameters from a limited numberof time-series data points. For this, we cast this identificationproblem as a sparse linear regression problem and take a Bayesianviewpoint to solve it. As such, this approach typically leads tononconvex optimizations. We propose a convexification procedurerelying on an efficient iterative re-weighted 1-minimization algorithmthat uses general sparsity inducing priors on the parametersof the system and marginal likelihood maximisation. Using thisapproach, we also show how convex constraints on the parameterscan be easily added to the proposed iterative re-weighted1-minimization algorithm. In the supplementary material availableonline (arXiv:1408.3549), we illustrate the effectiveness of theproposed identification method on two classical systems in biologyand physics, namely, a genetic repressilator network and a largescale network of interconnected Kuramoto oscillators.

  • Journal article
    Oyarzun DA, Chaves M, 2015,

    Design of a bistable switch to control cellular uptake

    , Journal of the Royal Society Interface, Vol: 20150618, ISSN: 1742-5689
  • Journal article
    Hammond A, Galizi R, Kyrou K, Simoni A, Siniscalchi C, Katsanos D, Gribble M, Baker D, Marois E, Russell S, Burt A, Windbichler N, Crisanti A, Nolan Tet al., 2015,

    A CRISPR-Cas9 gene drive system-targeting female reproduction in the malaria mosquito vector Anopheles gambiae

    , Nature Biotechnology, Vol: 34, Pages: 78-83, ISSN: 1087-0156

    Gene drive systems that enable super-Mendelian inheritance of a transgene have the potential to modify insect populations over a timeframe of a few years. We describe CRISPR-Cas9 endonuclease constructs that function as gene drive systems in Anopheles gambiae, the main vector for malaria. We identified three genes (AGAP005958, AGAP011377 and AGAP007280) that confer a recessive female-sterility phenotype upon disruption, and inserted into each locus CRISPR-Cas9 gene drive constructs designed to target and edit each gene. For each targeted locus we observed a strong gene drive at the molecular level, with transmission rates to progeny of 91.4 to 99.6%. Population modeling and cage experiments indicate that a CRISPR-Cas9 construct targeting one of these loci, AGAP007280, meets the minimum requirement for a gene drive targeting female reproduction in an insect population. These findings could expedite the development of gene drives to suppress mosquito populations to levels that do not support malaria transmission.

  • Journal article
    Quinn JY, Cox RS, Adler A, Beal J, Bhatia S, Cai Y, Chen J, Clancy K, Galdzicki M, Hillson NJ, Le Novère N, Maheshwari AJ, McLaughlin JA, Myers CJ P U, Pocock M, Rodriguez C, Soldatova L, Stan GB, Swainston N, Wipat A, Sauro HMet al., 2015,

    SBOL Visual: A Graphical Language for Genetic Designs.

    , PLOS Biology, Vol: 13, ISSN: 1545-7885

    Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.

  • Journal article
    Yuan Y, Rai A, Yeung E, Stan G-B, Warnick S, Goncalves Jet al., 2015,

    A Minimal Realization Technique for the Dynamical Structure Function of a Class of LTI Systems

    , IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, Vol: 4, Pages: 301-311, ISSN: 2325-5870

    The dynamical structure function of a linear time invariant (LTI) system reveals causal dependencies among manifest variables without specifying any particular relationships among the unmeasured states of the system. As such, it is a useful representation for complex networks where a coarse description of global system structure is desired without detailing the intricacies of a full state realization. In this paper, we consider the problem of finding a minimal state realization for a given dynamical structure function. Interestingly, some dynamical structure functions require uncontrollable modes in their state realizations to deliver the desired input-output behavior while respecting a specified system structure. As a result, the minimal order necessary to realize a particular dynamical structure function may be greater than that necessary to realize its associated transfer function. Although finding a minimal realization for a given dynamical structure function is difficult in general, we present a straightforward procedure here that works for a simplified class of systems.

  • Journal article
    Ceroni F, Carbonell P, François JM, Haynes KAet al., 2015,

    Editorial - Synthetic Biology: Engineering Complexity and Refactoring Cell Capabilities.

    , Frontiers in Bioengineering and Biotechnology, Vol: 3, ISSN: 2296-4185
  • Conference paper
    Sootla A, Oyarzun DA, Angeli D, Stan GBet al., 2015,

    Shaping Pulses to Control Bi-Stable Biological Systems

    , American Control Conference 2015, Publisher: IEEE, Pages: 3138-3143

    In this paper, we present a framework for shaping pulses to control biological systems, and specifically systems in synthetic biology. By shaping we mean computing the magnitude and the length of a pulse, application of which results in reaching the desired control objective. Hence the control signals have only two parameters, which makes these signals amenable to wetlab implementations. We focus on the problem of switching between steady states in a bistable system. We show how to estimate the set of the switching pulses, if the trajectories of the controlled system can be bounded from above and below by the trajectories of monotone systems. We then generalise this result to systems with parametric uncertainty under some mild assumptions on the set of admissible parameters, thus providing some robustness guarantees. We illustrate the results on some example genetic circuits.

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