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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
    Kelwick RJR, Ricci L, Chee SM, Bell D, Webb A, Freemont Pet al., 2019,

    Cell-free prototyping strategies for enhancing the sustainable production of polyhydroxyalkanoates bioplastics

    , Synthetic Biology, Vol: 3, ISSN: 2397-7000

    The polyhydroxyalkanoates (PHAs) are microbially-produced biopolymers that could potentially be used as sustainable alternatives to oil-derived plastics. However, PHAs are currently more expensive to produce than oil-derived plastics. Therefore, more efficient production processes would be desirable. Cell-free metabolic engineering strategies have already been used to optimise several biosynthetic pathways and we envisioned that cell-free strategies could be used for optimising PHAs biosynthetic pathways. To this end, we developed several Escherichia coli cell-free systems for in vitro prototyping PHAs biosynthetic operons, and also for screening relevant metabolite recycling enzymes. Furthermore, we customised our cell-free reactions through the addition of whey permeate, an industrial waste that has been previously used to optimise in vivo PHAs production. We found that the inclusion of an optimal concentration of whey permeate enhanced relative cell-free GFPmut3b production by ∼50%. In cell-free transcription-translation prototyping reactions, GC-MS quantification of cell-free 3-hydroxybutyrate (3HB) production revealed differences between the activities of the Native ΔPhaC_C319A (1.18 ±0.39 µM), C104 ΔPhaC_C319A (4.62 ±1.31 µM) and C101 ΔPhaC_C319A (2.65 ±1.27 µM) phaCAB operons that were tested. Interestingly, the most active operon, C104 produced higher levels of PHAs (or PHAs monomers) than the Native phaCAB operon in both in vitro and in vivo assays. Coupled cell-free biotransformation/transcription-translation reactions produced greater yields of 3HB (32.87 ±6.58 µM) and these reactions were also used to characterise a Clostridium propionicum Acetyl-CoA recycling enzyme. Together, these data demonstrate that cell-free approaches complement in vivo workflows for identifying additional strategies for optimising PHAs production.

  • Journal article
    Moya-Ramirez I, Kontoravdi K, Polizzi K, 2019,

    Low-cost and user-friendly biosensor to test the integrity of mRNA molecules suitable for field applications

    , Biosensors and Bioelectronics, Vol: 137, Pages: 199-206, ISSN: 0956-5663

    The use of mRNA in biotechnology has expanded with novel applications such as vaccines and therapeutic mRNA delivery recently demonstrated. For mRNA to be used in patients, quality control assays will need to be routinely established. Currently, there is a gap between the highly sophisticated RNA integrity tests available and broader application of mRNA-based products by non-specialist users, e.g. in mass vaccination campaigns. Therefore, the aim of this work was to develop a low-cost biosensor able to test the integrity of a mRNA molecule with low technological requirements and easy end-user application. The biosensor is based on a bi-functional fusion protein, composed by the λN peptide that recognizes its cognate aptamer encoded on the 5’ end of the RNA under study and β-lactamase, which is able to produce a colorimetric response through a simple test. We propose two different mechanisms for signal processing adapted to two levels of technological sophistication, one based on spectrophotometric measurements and other on visual inspection. We show that the proposed λN-βLac chimeric protein specifically targets its cognate RNA aptamer, boxB, using both gel shift and biolayer interferometry assays. More importantly, the results presented confirm the biosensor performs reliably, with a wide dynamic range and a proportional response at different percentages of full-length RNA, even when gene-sized mRNAs were used. Thus, the features of the proposed biosensor would allow to end-users of products such as mRNA vaccines to test the integrity of the product before its application in a low-cost fashion, enabling a more reliable application of these products.

  • Journal article
    Kuntz Nussio J, Thomas P, Stan GB, Barahona Met al., 2019,

    Rigorous bounds on the stationary distributions of the chemical master equation via mathematical programming

    , Journal of Chemical Physics, ISSN: 0021-9606

    The stochastic dynamics of biochemical networks are usually modelled with the chemical master equation (CME). The stationary distributions of CMEs are seldom solvable analytically, and numerical methods typically produce estimates with uncontrolled errors. Here, we introduce mathematical programming approaches that yield approximations of these distributions with computable error bounds which enable the verification of their accuracy. First, we use semidefinite programming to compute increasingly tighter upper and lower bounds on the moments of the stationary distributions for networks with rational propensities. Second, we use these moment bounds to formulate linear programs that yield convergent upper and lower bounds on the stationary distributions themselves, their marginals and stationary averages. The bounds obtained also provide a computational test for the uniqueness of the distribution. In the unique case, the bounds form an approximation of the stationary distribution with a computable bound on its error. In the non unique case, our approach yields converging approximations of the ergodic distributions. We illustrate our methodology through several biochemical examples taken from the literature: Schl¨ogl’s model for a chemical bifurcation, a two-dimensional toggle switch, a model for bursty gene expression, and a dimerisation model with multiple stationary distributions.

  • Journal article
    Brittain R, Jones N, Ouldridge T,

    Biochemical Szilard engines for memory-limited inference

    , New Journal of Physics, ISSN: 1367-2630

    By designing and leveraging an explicit molecular realisation of a measurement-and-feedback-powered Szilard engine, we investigate the extraction of work from complex environments by minimalmachines with finite capacity for memory and decision-making. Living systems perform inferenceto exploit complex structure, or correlations, in their environment, but the physical limits andunderlying cost/benefit trade-offs involved in doing so remain unclear. To probe these questions,we consider a minimal model for a structured environment—a correlated sequence of molecules—and explore mechanisms based on extended Szilard engines for extracting the work stored in thesenon-equilibrium correlations. We consider systems limited to a single bit of memory making binary‘choices’ at each step. We demonstrate that increasingly complex environments allow increasinglysophisticated inference strategies to extract more free energy than simpler alternatives, and arguethat optimal design of such machines should also consider the free energy reserves required to ensurerobustness against fluctuations due to mistakes.

  • Journal article
    Scholes N, Schnoerr D, Isalan M, Stumpf Met al.,

    A comprehensive network atlas reveals that Turing patterns are common but not robust

    , Cell Systems, ISSN: 2405-4712

    Turing patterns (TPs) underlie many fundamental de-velopmental processes, but they operate over narrowparameter ranges, raising the conundrum of how evo-lution can ever discover them. Here we explore TPdesign space to address this question and to distill de-sign rules. We exhaustively analyze 2- and 3-node bio-logical candidate Turing systems, amounting to 7,625networks and more than3×1011analysed scenar-ios. We find that network structure alone neither im-plies nor guarantees emergent TPs. A large fraction(>61%) of network design space can produce TPs,but these are sensitive to even subtle changes in pa-rameters, network structure and regulatory mecha-nisms. This implies that TP networks are more com-mon than previously thought, and evolution mightregularly encounter prototypic solutions. We deducecompositional rules for TP systems that are almostnecessary and sufficient (96%of TP networks containthem, and92%of networks implementing them pro-duce TPs). This comprehensive network atlas providesthe blueprints for identifying natural TPs, and for en-gineering synthetic systems.

  • Journal article
    McCarty NS, Shaw WM, Ellis T, Ledesma-Amaro Ret al., 2019,

    Rapid assembly of gRNA arrays via modular cloning in yeast

    , ACS Synthetic Biology, Vol: 8, Pages: 906-910, ISSN: 2161-5063

    CRISPR is a versatile technology for genomic editing and regulation, but the expression of multiple gRNAs in S. cerevisiae has thus far been limited. We present here a simple extension to the Yeast MoClo Toolkit, which enables the rapid assembly of gRNA arrays using a minimal set of parts. Using a dual-PCR, Type IIs restriction enzyme Golden Gate assembly approach, at least 12 gRNAs can be assembled and expressed from a single transcriptional unit. We demonstrate that these gRNA arrays can stably regulate gene expression in a synergistic manner via dCas9-mediated repression. This approach expands the number of gRNAs that can be expressed in this model organism and may enable the versatile editing or transcriptional regulation of a greater number of genes in vivo.

  • Journal article
    Boo A, Ellis T, Stan G, 2019,

    Host-aware synthetic biology

    , Current Opinion in Systems Biology, Vol: 14, Pages: 66-72, ISSN: 2452-3100

    Unnatural gene expression imposes a load on engineered microorganisms thatdecreases their growth and subsequent production yields, a phenomenon knownasburden. In the last decade, the field of synthetic biology has made progress onthe development of biomolecular feedback control systems and other approachesthat can improve the growth of engineered cells, as well as the genetic stability,portability and robust performance of cell-hosted synthetic constructs. In thisreview, we highlight recent work focused on the development of host-aware syn-thetic biology.

  • Journal article
    Kuntz J, Thomas P, Stan G-B, Barahona Met al., 2019,

    The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains

    , SIAM Journal on Scientific Computing, Vol: 41, Pages: A748-A769, ISSN: 1064-8275

    We introduce the exit time finite state projection (ETFSP) scheme, a truncation- based method that yields approximations to the exit distribution and occupation measure associated with the time of exit from a domain (i.e., the time of first passage to the complement of the domain) of time-homogeneous continuous-time Markov chains. We prove that: (i) the computed approximations bound the measures from below; (ii) the total variation distances between the approximations and the measures decrease monotonically as states are added to the truncation; and (iii) the scheme converges, in the sense that, as the truncation tends to the entire state space, the total variation distances tend to zero. Furthermore, we give a computable bound on the total variation distance between the exit distribution and its approximation, and we delineate the cases in which the bound is sharp. We also revisit the related finite state projection scheme and give a comprehensive account of its theoretical properties. We demonstrate the use of the ETFSP scheme by applying it to two biological examples: the computation of the first passage time associated with the expression of a gene, and the fixation times of competing species subject to demographic noise.

  • Journal article
    Aw R, Polizzi KM, 2019,

    Biosensor‐assisted engineering of a high‐yield Pichia pastoris cell‐free protein synthesis platform

    , Biotechnology and Bioengineering, Vol: 116, Pages: 656-666, ISSN: 0006-3592

    Cell‐free protein synthesis (CFPS) has recently undergone a resurgence partly due to the proliferation of synthetic biology. The variety of hosts used for cell‐free extract production has increased, which harnesses the diversity of cellular biosynthetic, protein folding, and posttranslational modification capabilities available. Here we describe a CFPS platform derived from Pichia pastoris, a popular recombinant protein expression host both in academia and the biopharmaceutical industry. A novel ribosome biosensor was developed to optimize the cell extract harvest time. Using this biosensor we identified a potential bottleneck in ribosome content. Therefore, we undertook strain engineering to overexpress global regulators of ribosome biogenesis to increase in vitro protein production. CFPS extracts from the strain overexpressing FHL1 had a 3‐fold increase in recombinant protein yield compared to those from the wild‐type X33 strain. Furthermore, our novel CFPS platform can produce complex therapeutic proteins, as exemplified by the production of human serum albumin to a final yield of 48.1 μg mL‐1. Therefore, this work not only adds to the growing number of CFPS systems from diverse organisms, but also provides a blueprint for rapidly engineering new strains with increased productivity in vitro that could be applied to other organisms.

  • Journal article
    Kylilis N, Riangrungroj P, Lai H-E, Salema V, Fernández LÁ, Stan G-B, Freemont PS, Polizzi KMet al., 2019,

    Whole-cell biosensor with tuneable limit of detection enables low-cost agglutination assays for medical diagnostic applications

    , ACS Sensors, Vol: 4, Pages: 370-378, ISSN: 2379-3694

    Whole-cell biosensors can form the basis of affordable, easy-to-use diagnostic tests that can be readily deployed for point-of-care (POC) testing, but to date, the detection of analytes such as proteins that cannot easily diffuse across the cell membrane has been challenging. Here we developed a novel biosensing platform based on cell agglutination using an E. coli whole-cell biosensor surface-displaying nanobodies which bind selectively to a target protein analyte. As a proof-of-concept, we show the feasibility of this design can detect a model analyte at nanomolar concentrations. Moreover, we show that the design architecture is flexible by building assays optimized to detect a range of model analyte concentrations using straight-forward design rules and a mathematical model. Finally, we re-engineer our whole-cell biosensor for the detection of a medically relevant biomarker by the display of two different nanbodies against human fibrinogen and demonstrate a detection limit as low as 10 pM in diluted human plasma. Overall, we demonstrate that our agglutination technology fulfills the requirement of POC testing by combining low-cost nanobody production, customizable detection range and low detection limits. This technology has the potential to produce affordable diagnostics for field-testing in the developing world, emergency or disaster relief sites as well as routine medical testing and personalized medicine.

  • Journal article
    Shaw W, Yamauchi H, Mead J, Gowers G, Bell D, Oling D, Larsson N, Wigglesworth M, Ladds G, Ellis Tet al.,

    Engineering a model cell for rational tuning of GPCR signaling

    , Cell, ISSN: 0092-8674

    G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond tospecific cues in their environment. However, the relationship between stimulus and response for eachGPCR is difficult to predict due to diversity in natural signal transduction architecture and expression.Using genome engineering in yeast, we here constructed an insulated, modular GPCR signaltransduction system to study how the response to stimuli can be predictably tuned using synthetictools. We delineated the contributions of a minimal set of key components via computational andexperimental refactoring, identifying simple design principles for rationally tuning the dose-response.Using five different GPCRs, we demonstrate how this enables cells and consortia to be engineeredto respond to desired concentrations of peptides, metabolites, and hormones relevant to humanhealth. This work enables rational tuning of cell sensing, while providing a framework to guidereprogramming of GPCR-based signaling in other systems.

  • Journal article
    Poulton J, Wolde PRT, Ouldridge TE, 2019,

    Non-equilibrium correlations in minimal dynamical models of polymer copying

    , Proceedings of the National Academy of Sciences, Vol: 116, Pages: 1946-1951, ISSN: 0027-8424

    Living systems produce "persistent" copies of information-carrying polymers, in which template and copy sequences remain correlated after physically decoupling. We identify a general measure of the thermodynamic efficiency with which these non-equilibrium states are created, and analyze the accuracy and efficiency of a family of dynamical models that produce persistent copies. For the weakest chemical driving, when polymer growth occurs in equilibrium, both the copy accuracy and, more surprisingly, the efficiency vanish. At higher driving strengths, accuracy and efficiency both increase, with efficiency showing one or more peaks at moderate driving. Correlations generated within the copy sequence, as well as between template and copy, store additional free energy in the copied polymer and limit the single-site accuracy for a given chemical work input. Our results provide insight in the design of natural self-replicating systems and can aid the design of synthetic replicators.

  • Journal article
    Gilbert C, Ellis T, 2019,

    Biological engineered living materials - growing functional materials with genetically-programmable properties

    , ACS Synthetic Biology, Vol: 8, Pages: 1-15, ISSN: 2161-5063

    Natural biological materials exhibit remarkable properties: self-assembly from simple raw materials, precise control of morphology, diverse physical and chemical properties, self-repair and the ability to sense-and-respond to environmental stimuli. Despite having found numerous uses in human industry and society, the utility of natural biological materials is limited. But, could it be possible to genetically program microbes to create entirely new and useful biological materials? At the intersection between microbiology, material science and synthetic biology, the emerging field of biological Engineered Living Materials (ELMs) aims to answer this question. Here we review recent efforts to program cells to produce living materials with novel functional properties, focussing on microbial systems that can be engineered to grow materials and on new genetic circuits for pattern formation that could be used to produce the more complex systems of the future.

  • Journal article
    Ellis T, 2019,

    Predicting how evolution will beat us

    , MICROBIAL BIOTECHNOLOGY, Vol: 12, Pages: 41-43, ISSN: 1751-7915
  • Journal article
    Blount B, Ellis T, 2019,

    The Synthetic Genome Summer Course

    , Synthetic Biology, Vol: 3, ISSN: 2397-7000

    The Synthetic Genome Summer Course was convened with the aim of teaching a wide range of researchers the theory and practical skills behind recent advances in synthetic biology and synthetic genome science, with a focus on Sc2.0, the synthetic yeast genome project. Through software workshops, tutorials and research talks from leading members of the field, the 30 attendees learnt about relevant principles and techniques that they were then able to implement first-hand in laboratory-based practical sessions. Participants SCRaMbLEd semi-synthetic yeast strains to diversify heterologous pathways, used automation to build combinatorial pathway libraries and used CRISPR to debug fitness defects caused by synthetic chromosome design changes. Societal implications of synthetic chromosomes were explored and industrial stakeholders discussed synthetic biology from a commercial standpoint. Over the 5 days, participants gained valuable insight and acquired skills to aid them in future synthetic genome research.

  • Journal article
    Weenink T, van der Hilst J, McKiernan R, Ellis Tet al., 2019,

    Design of RNA hairpin modules that predictably tune translation in yeast

    , Synthetic Biology, Vol: 3, ISSN: 2397-7000

    Modular parts for tuning translation are prevalent in prokaryotic synthetic biology but lacking for eukaryotic synthetic biology. Working in Saccharomyces cerevisiae yeast, we here describe how hairpin RNA structures inserted into the 5′ untranslated region (5′UTR) of mRNAs can be used to tune expression levels by 100-fold by inhibiting translation. We determine the relationship between the calculated free energy of folding in the 5′UTR and in vivo protein abundance, and show that this enables rational design of hairpin libraries that give predicted expression outputs. Our approach is modular, working with different promoters and protein coding sequences, and outperforms promoter mutation as a way to predictably generate a library where a protein is induced to express at a range of different levels. With this new tool, computational RNA sequence design can be used to predictably fine-tune protein production for genes expressed in yeast.

  • Book chapter
    Ouldridge TE, Brittain R, ten Wolde PR, 2018,

    The power of being explicit: demystifying work, heat, and free energy in the physics of computation

    , The Interplay of Thermodynamics and Computation in Both Natural and Artificial Systems
  • Conference paper
    Toczek M, Zielonka D, Zukowska P, Marcinkowski JT, Kutryb-Zajac B, Slominska E, Isalan M, Mielcarek M, Smolenski RTet al., 2018,

    An altered nucleotide metabolism as a novel mechanism leading to Huntington disease related cardiomyopathy

    , Purines 2018 Basic and Translational Science on Purinergic Signaling and its Components for a Healthy and Better World, Publisher: Springer Verlag, Pages: S53-S53, ISSN: 1573-9538
  • Journal article
    Walker K, Goosens V, Das A, Graham A, Ellis Tet al., 2018,

    Engineered cell-to-cell signalling within growing bacterial cellulose pellicles

    , Microbial Biotechnology, ISSN: 1751-7915

    Bacterial cellulose is a strong and flexible biomaterial produced at high yields by Acetobacter species and has applications in health care, biotechnology and electronics. Naturally, bacterial cellulose grows as a large unstructured polymer network around the bacteria that produce it, and tools to enable these bacteria to respond to different locations are required to grow more complex structured materials. Here, we introduce engineered cell‐to‐cell communication into a bacterial cellulose‐producing strain of Komagataeibacter rhaeticus to enable different cells to detect their proximity within growing material and trigger differential gene expression in response. Using synthetic biology tools, we engineer Sender and Receiver strains of K. rhaeticus to produce and respond to the diffusible signalling molecule, acyl‐homoserine lactone. We demonstrate that communication can occur both within and between growing pellicles and use this in a boundary detection experiment, where spliced and joined pellicles sense and reveal their original boundary. This work sets the basis for synthetic cell‐to‐cell communication within bacterial cellulose and is an important step forward for pattern formation within engineered living materials.

  • Journal article
    Taylor G, Mordaka P, Heap J, 2018,

    Start-stop assembly: a functionally scarless DNA assembly system optimized for metabolic engineering

    , Nucleic Acids Research, ISSN: 0305-1048

    DNA assembly allows individual DNA constructs or libraries to be assembled quickly and reliably. Most methods are either: (i) Modular, easily scalable and suitable for combinatorial assembly, but leave undesirable ‘scar’ sequences; or (ii) bespoke (non-modular), scarless but less suitable for construction of combinatorial libraries. Both have limitations for metabolic engineering. To overcome this trade-off we devised Start-Stop Assembly, a multi-part, modular DNA assembly method which is both functionally scarless and suitable for combinatorial assembly. Crucially, 3 bp overhangs corresponding to start and stop codons are used to assemble coding sequences into expression units, avoiding scars at sensitive coding sequence boundaries. Building on this concept, a complete DNA assembly framework was designed and implemented, allowing assembly of up to 15 genes from up to 60 parts (or mixtures); monocistronic, operon-based or hybrid configurations; and a new streamlined assembly hierarchy minimising the number of vectors. Only one destination vector is required per organism, reflecting our optimisation of the system for metabolic engineering in diverse organisms. Metabolic engineering using Start-Stop Assembly was demonstrated by combinatorial assembly of carotenoid pathways in E. coli resulting in a wide range of carotenoid production and colony size phenotypes indicating the intended exploration of design space.

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