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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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  • Conference paper
    Toczek M, Zielonka D, Marcinkowski J, Isalan M, Smolenski R, Mielcarek Met al.,

    An altered metabolism of nucleotides leads to huntington’s disease related cardiomyopathy

    , EHDN Plenary Meeting, Publisher: BMJ Publishing Group, Pages: A13-A13, ISSN: 1468-330X
  • Conference paper
    Puch A, Witkowski G, Isalan M, Mielcarek M, Zielonka Det al., 2018,

    A frequency of concomitant disorders in presymptomatic huntington’s disease patients

    , EHDN Plenary Meeting 2018, Publisher: BMJ Publishing Group, Pages: A42-A43, ISSN: 1468-330X
  • Journal article
    Schaerli Y, Jiménez A, Duarte JM, Mihajlovic L, Renggli J, Isalan M, Sharpe J, Wagner Aet al., 2018,

    Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution

    , Molecular Systems Biology, Vol: 14, ISSN: 1744-4292

    Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such biases are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most pertinent evidence is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in Escherichia coli that produce a gene expression stripe—a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.

  • Journal article
    Aw R, McKay P, Shattock R, Polizzi Ket al., 2018,

    A systematic analysis of the expression of the anti-HIV VRC01 antibody in Pichia pastoris through signal peptide optimization

    , Protein Expression and Purification, Vol: 149, Pages: 43-50, ISSN: 1046-5928

    Pichia pastoris (Komagataella phaffi) has been used for recombinant protein production for over 30 years with over 5000 proteins reported to date. However, yields of antibody are generally low. We have evaluated the effect of secretion signal peptides on the production of a broadly neutralizing antibody (VRC01) to increase yield. Eleven different signal peptides, including the murine IgG1 signal peptide, were combinatorially evaluated for their effect on antibody titer. Strains using different combinations of signal peptides were identified that secreted approximately 2-7 fold higher levels of VRC01 than the previous best secretor, with the highest yield of 6.50 mg L-1 in shake flask expression. Interestingly it was determined that the highest yields were achieved when the murine IgG1 signal peptide was fused to the light chain, with several different signal peptides leading to high yield when fused to the heavy chain. Finally, we have evaluated the effect of using a 2A signal peptide to create a bicistronic vector in the attempt to reduce burden and increase transformation efficiency, but found it to give reduced yields compared to using two independent vectors.

  • Journal article
    O'Clery N, Yuan Y, Stan G-B, Barahona Met al., 2018,

    Global Network Prediction from Local Node Dynamics

    The study of dynamical systems on networks, describing complex interactiveprocesses, provides insight into how network structure affects globalbehaviour. Yet many methods for network dynamics fail to cope with large orpartially-known networks, a ubiquitous situation in real-world applications.Here we propose a localised method, applicable to a broad class of dynamicalmodels on networks, whereby individual nodes monitor and store the evolution oftheir own state and use these values to approximate, via a simple computation,their own steady state solution. Hence the nodes predict their own final statewithout actually reaching it. Furthermore, the localised formulation enablesnodes to compute global network metrics without knowledge of the full networkstructure. The method can be used to compute global rankings in the networkfrom local information; to detect community detection from fast, localtransient dynamics; and to identify key nodes that compute global networkmetrics ahead of others. We illustrate some of the applications of thealgorithm by efficiently performing web-page ranking for a large internetnetwork and identifying the dynamic roles of inter-neurons in the C. Elegansneural network. The mathematical formulation is simple, widely applicable andeasily scalable to real-world datasets suggesting how local computation canprovide an approach to the study of large-scale network dynamics.

  • Journal article
    Yunus IS, Jones PR, 2018,

    Photosynthesis-dependent biosynthesis of medium chain-length fatty acids and alcohols

    , Metabolic Engineering, Vol: 49, Pages: 59-68, ISSN: 1096-7176

    Cyanobacteria can directly channel atmospheric CO2 into a wide range of versatile carbon products such as fatty acids and fatty alcohols with applications including fuel, cosmetics, and health products. Works on alcohol production in cyanobacteria have so far focused on either long (C12-C18) or short (C2-C4) chain-length products. In the present work, we report the first synthetic pathway for 1-octanol (C8) biosynthesis in Synechocystis sp. PCC 6803, employing a carboxylic acid reductase and C8-preferring fatty acyl-ACP thioesterase. The first engineered strain produced 1-octanol but exhibited poor productivity and cellular health issues. We therefore proceeded to systematically optimize the strain and cultivation conditions in order to understand what the limiting factors were. The identification of optimal promoters and ribosomal binding sites, in combination with isopropyl myristate solvent overlay, resulted in a combined (C8-OH and C10-OH) titer of more than 100 mg/L (a 25-fold improvement relative to the first engineered strain) and a restoration of cellular health. Additionally, more than 905 mg/L 1-octanol was produced when the strain expressing sfp (phosphopantetheinyl transferase) and car (carboxylic acid reductase) was fed with octanoic acid. A combination of feeding experiments and protein quantification indicated that the supply of octanoic acid from the introduced thioesterase, and possibly also native fatty acid synthesis pathway, were the main bottlenecks of the pathway.

  • Journal article
    Oling D, Lawenius L, Shaw W, Clark S, Kettleborough R, Ellis T, Larsson N, Wigglesworth Met al., 2018,

    Large Scale Synthetic Site Saturation GPCR Libraries Reveal Novel Mutations That Alter Glucose Signaling

    , ACS SYNTHETIC BIOLOGY, Vol: 7, Pages: 2317-2321, ISSN: 2161-5063
  • Journal article
    Ceroni F, Ellis T, 2018,

    The challenges facing synthetic biology in eukaryotes

    , Nature Reviews Molecular Cell Biology, Vol: 19, Pages: 481-482, ISSN: 1471-0072

    Synthetic biology is maturing into a true engineering discipline for model microorganisms, but remains far from straightforward for most eukaryotes. Here, we outline the key challenges facing those trying to engineer biology across eukaryota and suggest areas of focus that will aid future progress.

  • Journal article
    Kylilis N, Tuza ZA, Stan G, Polizzi KMet al., 2018,

    Tools for engineering coordinated system behaviour in synthetic microbial consortia

    , Nature Communications, Vol: 9, ISSN: 2041-1723

    Advancing synthetic biology to the multicellular level requires the development of multiple cell-to-cell communication channels that propagate information with minimal signal interference. The development of quorum-sensing devices, the cornerstone technology for building microbial communities with coordinated system behaviour, has largely focused on cognate acyl-homoserine lactone (AHL)/transcription factor pairs, while the use of non-cognate pairs as a design feature has received limited attention. Here, we demonstrate a large library of AHL-receiver devices, with all cognate and non-cognate chemical signal interactions quantified, and we develop a software tool that automatically selects orthogonal communication channels. We use this approach to identify up to four orthogonal channels in silico, and experimentally demonstrate the simultaneous use of three channels in co-culture. The development of multiple non-interfering cell-to-cell communication channels is an enabling step that facilitates the design of synthetic consortia for applications including distributed bio-computation, increased bioprocess efficiency, cell specialisation and spatial organisation.

  • Journal article
    Liu D, Mannan AA, Han Y, Oyarzun DA, Zhang Fet al., 2018,

    Dynamic metabolic control: towards precision engineering of metabolism

    , Journal of Industrial Microbiology and Biotechnology, Vol: 45, Pages: 535-543, ISSN: 1367-5435

    Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

  • Journal article
    Beal J, Haddock-Angelli T, Baldwin G, Gershater M, Dwijayanti A, Storch M, de Mora K, Lizarazo M, Rettberg Ret al., 2018,

    Quantification of bacterial fluorescence using independent calibrants

    , PLoS ONE, Vol: 13, ISSN: 1932-6203

    Fluorescent reporters are commonly used to quantify activities or properties of both natural and engineered cells. Fluorescence is still typically reported only in arbitrary or normalized units, however, rather than in units defined using an independent calibrant, which is problematic for scientific reproducibility and even more so when it comes to effective engineering. In this paper, we report an interlaboratory study showing that simple, low-cost unit calibration protocols can remedy this situation, producing comparable units and dramatic improvements in precision over both arbitrary and normalized units. Participants at 92 institutions around the world measured fluorescence from E. coli transformed with three engineered test plasmids, plus positive and negative controls, using simple, low-cost unit calibration protocols designed for use with a plate reader and/or flow cytometer. In addition to providing comparable units, use of an independent calibrant allows quantitative use of positive and negative controls to identify likely instances of protocol failure. The use of independent calibrants thus allows order of magnitude improvements in precision, narrowing the 95% confidence interval of measurements in our study up to 600-fold compared to normalized units.

  • Journal article
    Enrico Bena C, Grob A, Isalan M, Bosia C, Ceroni Fet al., 2018,

    Commentary: Synthetic Addiction Extends the Productive Life Time of Engineered Escherichia coli Populations

    , Frontiers in Bioengineering and Biotechnology, Vol: 6, ISSN: 2296-4185

    A commentary on Synthetic addiction extends the productive life time of engineered Escherichia coli populations by Rugbjerg, P., Sarup-Lytzen, K., Nagy, M., and Sommer, M. O. A. (2018). Proc. Natl. Acad. Sci. U.S.A. 115, 2347–2352. doi: 10.1073/pnas.1718622115Bioproduction is the process of producing added-value chemicals on large-scale using cells as biological factories. Cellular burden represents a significant problem in the scaling of fermentation processes from proof-of-concept to long-term cultures, as the load of heterologous gene expression and depletion of the cell intracellular resources cause unpredictable cellular physiological changes that can lead to decreased growth and lower production yields (Borkowski et al., 2016; Liu et al., 2018). One possible cause of the observed decreased bioproduct recovery in many bioprocessing applications is the accumulation of mutations in the employed genetic program. These mutations often lead to loss of production and rise of non-producing populations that grow better and easily overtake the growth of producing cells (Rugbjerg et al., 2018b).In a recent paper in PNAS, Rugbjerg et al. (2018b) developed a strategy to limit the enrichment of non-producing cell populations in bioproduction-employed cell cultures by placing the genes for key growth intermediates under the control of a promoter responsive to the bioproduct being made. This strategy known as product addiction was tested in E. coli engineered to produce mevalonic acid in long-term cultivations (Figure 1).

  • Journal article
    Pan W, Yuan Y, Ljung L, Goncalves J, Stan Get al., 2018,

    Identification of nonlinear state-space systems from heterogeneous datasets

    , IEEE Transactions on Control of Network Systems, Vol: 5, Pages: 737-747, ISSN: 2325-5870

    This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through 1) replicate measurements from the same biological system or 2) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed heterogeneous data. To enable scale up to large number of features, the resulting nonconvex optimization problem is relaxed to a reweighted Group Lasso problem using a convex–concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalized eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise.

  • Journal article
    Critchley B, Isalan M, Mielcarek M, 2018,

    Neuro-Cardio mechanisms in Huntington’s disease and other neurodegenerative disorders

    , Frontiers in Physiology, Vol: 9, ISSN: 1664-042X

    Although Huntington’s disease is generally considered to be aneurological disorder, there is mounting evidence that heart malfunction plays an important role in disease progression. This is perhaps not unexpected since both cardiovascular and nervous systems are strongly connected—both development ally and subsequently inhealth and disease. This connection occurs through a systemof central and peripheral neurons that control cardiovascular performance, while in return the cardiovascular system worksas a sensor for the nervous system to react to physiological events. Hence, given their permanent interconnectivity, any pathological events occurring in one system might affect the second. In addition, some pathological signals fromHuntington’s disease might occur simultaneously in both the cardiovascular and nervous systems, since mutant Huntingtin protein is expressedin both. Here we aim to review the source of HD-related cardiomyopathy in the light of recently-published studies, and to identify similarities between HD-related cardiomyopathy andother neuro-cardio disorders.

  • Journal article
    Blount B, Gowers G, Ho JCH, Ledesma-Amaro R, Jovicevic D, McKiernan R, Xie ZX, Li BZ, Yuan YJ, Ellis Tet al., 2018,

    Rapid host strain improvement by in vivo rearrangement of a synthetic yeast chromosome

    , Nature Communications, Vol: 9, ISSN: 2041-1723

    Synthetic biology tools, such as modular parts and combinatorial DNA assembly, are routinely used to optimise the productivity of heterologous metabolic pathways for biosynthesis or substrate utilisation, yet, it is well established that host strain background is just as important for determining productivity. Here we report that in vivo combinatorial genomic rearrangement of Saccharomyces cerevisiae yeast with a synthetic chromosome V can rapidly generate new, improved host strains with genetic backgrounds favourable to diverse heterologous pathways, including those for violacein and penicillin biosynthesis and for xylose utilisation. We show how the modular rearrangement of synthetic chromosomes by SCRaMbLE can be easily determined using long-read nanopore sequencing and we explore experimental conditions that optimise diversification and screening. This new synthetic genome approach to metabolic engineering provides productivity improvements in a fast, simple and accessible way, making it a valuable addition to existing strain improvement techniques.

  • Journal article
    Henrich O, Gutiérrez Fosado YA, Curk T, Ouldridge TEet al., 2018,

    Coarse-grained simulation of DNA using LAMMPS : an implementation of the oxDNA model and its applications

    , European Physical Journal E. Soft Matter, Vol: 41, Pages: 57-57, ISSN: 1292-8941

    During the last decade coarse-grained nucleotide models have emerged that allow us to study DNA and RNA on unprecedented time and length scales. Among them is oxDNA, a coarse-grained, sequence-specific model that captures the hybridisation transition of DNA and many structural properties of single- and double-stranded DNA. oxDNA was previously only available as standalone software, but has now been implemented into the popular LAMMPS molecular dynamics code. This article describes the new implementation and analyses its parallel performance. Practical applications are presented that focus on single-stranded DNA, an area of research which has been so far under-investigated. The LAMMPS implementation of oxDNA lowers the entry barrier for using the oxDNA model significantly, facilitates future code development and interfacing with existing LAMMPS functionality as well as other coarse-grained and atomistic DNA models.

  • Journal article
    Freemont PS, Moore S, MacDonald J, Wienecke S, Ishwarbhai A, Tsipa A, Aw R, Kylilis N, Bell D, McCymont D, Jensen K, Polizzi K, Biedendieck Ret al., 2018,

    Rapid acquisition and model-based analysis of cell-free transcription-translation reactions from non-model bacteria

    , Proceedings of the National Academy of Sciences, Vol: 115, Pages: E4340-E4349, ISSN: 0027-8424

    Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications.

  • Journal article
    Kogenaru M, Isalan M, 2018,

    Drug-inducible control of lethality genes: a low background destabilizing domain architecture applied to the Gal4-UAS system in Drosophila

    , ACS Synthetic Biology, Vol: 7, Pages: 1496-1506, ISSN: 2161-5063

    Destabilizing domains (DDs) are genetic tags that conditionally control the level of abundance of proteins-of-interest (POI) with specific stabilizing small-molecule drugs, rapidly and reversibly, in a wide variety of organisms. The amount of the DD-tagged fusion protein directly impacts its molecular function. Hence, it is important that the background levels be tightly regulated in the absence of any drug. This is especially true for classes of proteins that function at extremely low levels, such as lethality genes involved in tissue development and certain transcriptional activator proteins. Here, we establish the uninduced background and induction levels for two widely used DDs (FKBP and DHFR) by developing an accurate quantification method. We show that both DDs exhibit functional background levels in the absence of a drug, but each to a different degree. To overcome this limitation, we systematically test a double architecture for these DDs (DD-POI-DD) that completely suppresses the protein’s function in an uninduced state, while allowing tunable functional levels upon adding a drug. As an example, we generate a drug-stabilizable Gal4 transcriptional activator with extremely low background levels. We show that this functions in vivo in the widely used Gal4-UAS bipartite expression system in Drosophila melanogaster. By regulating a cell death gene, we demonstrate that only the low background double architecture enables tight regulation of the lethal phenotype in vivo. These improved tools will enable applications requiring exceptionally tight control of protein function in living cells and organisms.

  • Journal article
    De Porcellinis AJ, Norgaard H, Brey LMF, Erstad SM, Jones PR, Heazlewood JL, Sakuragi Yet al., 2018,

    Overexpression of bifunctional fructose-1,6-bisphosphatase/sedoheptulose-1,7-bisphosphatase leads to enhanced photosynthesis and global reprogramming of carbon metabolism in Synechococcus sp PCC 7002

    , Metabolic Engineering, Vol: 47, Pages: 170-183, ISSN: 1096-7176

    Cyanobacteria fix atmospheric CO2 to biomass and through metabolic engineering can also act as photosynthetic factories for sustainable productions of fuels and chemicals. The Calvin Benson cycle is the primary pathway for CO2 fixation in cyanobacteria, algae and C3 plants. Previous studies have overexpressed the Calvin Benson cycle enzymes, ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and bifunctional sedoheptulose-1,7-bisphosphatase/fructose-1,6-bisphosphatase (hereafter BiBPase), in both plants and algae, although their impacts on cyanobacteria have not yet been rigorously studied. Here, we show that overexpression of BiBPase and RuBisCO have distinct impacts on carbon metabolism in the cyanobacterium Synechococcus sp. PCC 7002 through physiological, biochemical, and proteomic analyses. The former enhanced growth, cell size, and photosynthetic O2 evolution, and coordinately upregulated enzymes in the Calvin Benson cycle including RuBisCO and fructose-1,6-bisphosphate aldolase. At the same time it downregulated enzymes in respiratory carbon metabolism (glycolysis and the oxidative pentose phosphate pathway) including glucose-6-phosphate dehydrogenase (G6PDH). The content of glycogen was also significantly reduced while the soluble carbohydrate content increased. These results indicate that overexpression of BiBPase leads to global reprogramming of carbon metabolism in Synechococcus sp. PCC 7002, promoting photosynthetic carbon fixation and carbon partitioning towards non-storage carbohydrates. In contrast, whilst overexpression of RuBisCO had no measurable impact on growth and photosynthetic O2 evolution, it led to coordinated increase in the abundance of proteins involved in pyruvate metabolism and fatty acid biosynthesis. Our results underpin that singular genetic modifications in the Calvin Benson cycle can have far broader cellular impact than previously expected. These features could be exploited to more efficiently direct carbons towards desired

  • Journal article
    Perez-Carrasco R, Barnes CP, Schaerli Y, Isalan M, Briscoe J, Page KMet al., 2018,

    Combining a toggle switch and a repressilator within the AC-DC circuit generates distinct dynamical behaviors

    , Cell Systems, Vol: 6, Pages: 521-530.e3, ISSN: 2405-4712

    Although the structure of a genetically encoded regulatory circuit is an important determinant of its function, the relationship between circuit topology and the dynamical behaviors it can exhibit is not well understood. Here, we explore the range of behaviors available to the AC-DC circuit. This circuit consists of three genes connected as a combination of a toggle switch and a repressilator. Using dynamical systems theory, we show that the AC-DC circuit exhibits both oscillations and bistability within the same region of parameter space; this generates emergent behaviors not available to either the toggle switch or the repressilator alone. The AC-DC circuit can switch on oscillations via two distinct mechanisms, one of which induces coherence into ensembles of oscillators. In addition, we show that in the presence of noise, the AC-DC circuit can behave as an excitable system capable of spatial signal propagation or coherence resonance. Together, these results demonstrate how combinations of simple motifs can exhibit multiple complex behaviors.

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