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 articleCeroni F, Ellis T, 2018,
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 articleKylilis N, Tuza ZA, Stan G, et al., 2018,
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 articleTanaka G, Dominguez-Huttinger E, Christodoulides P, et al., 2018,
Bifurcation analysis of a mathematical model of atopic dermatitis to determine patient-specific effects of treatments on dynamic phenotypes, Journal of Theoretical Biology, Vol: 448, Pages: 66-79, ISSN: 0022-5193
Atopic dermatitis (AD) is a common inflammatory skin disease, whose incidence is currently increasing worldwide. AD has a complex etiology, involving genetic, environmental, immunological, and epidermal factors, andits pathogenic mechanisms have not yet been fully elucidated. Identificationof AD risk factors and systematic understanding of their interactions arerequired for exploring effective prevention and treatment strategies for AD.We recently developed a mathematical model for AD pathogenesis to clarifymechanisms underlying AD onset and progression. This model describes adynamic interplay between skin barrier, immune regulation, and environmental stress, and reproduced four types of dynamic behaviour typically observed in AD patients in response to environmental triggers. Here, we analyse bifurcations of the model to identify mathematical conditions for the system to demonstrate transitions between different types of dynamic behaviour that reflect respective severity of AD symptoms. By mathematically modelling effects of topical application of antibiotics, emollients, corticosteroids, and their combinations with different application schedules and doses, bifurcation analysis allows us to mathematically evaluate effects of the treatments on improving AD symptoms in terms of the patients' dynamic behaviour. The mathematical method developed in this study can be used to explore and improve patient-specific personalised treatment strategies to control AD symptoms.
Journal articleGoey CH, Alhuthali S, Kontoravdi K, 2018,
Host cell protein removal from biopharmaceutical preparations: toward the implementation of quality by design, Biotechnology Advances, Vol: 36, Pages: 1223-1237, ISSN: 0734-9750
Downstream processing of protein products of mammalian cell culture currently accounts for the largest fraction of the total production cost. A major challenge is the removal of host cell proteins, which are cell-derived impurities. Host cell proteins are potentially immunogenic and can compromise product integrity during processing and hold-up steps. There is an increasing body of evidence that the type of host cell proteins present in recombinant protein preparations is a function of cell culture conditions and handling of the harvest cell culture fluid. This, in turn, can affect the performance of downstream purification steps as certain species are difficult to remove and may require bespoke process solutions. Herein, we review recent research on the interplay between upstream process conditions, host cell protein composition and their downstream removal in antibody production processes, identifying opportunities for increasing process understanding and control. We further highlight advances in analytical and computational techniques that can enable the application of quality by design.
Journal articleLiu D, Mannan AA, Han Y, et al., 2018,
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.
Conference paperHurault G, Roekevisch E, Szegedi K, et al., 2018,
Journal articleNash A, Urdaneta GM, K Beaghton A, et al., 2018,
<jats:title>Abstract</jats:title><jats:p>First generation CRISPR-based gene drives have now been tested in the laboratory in a number of organisms including malaria vector mosquitoes. A number of challenges for their use in the area-wide genetic control of vector-borne disease have been identified. These include the development of target site resistance, their long-term efficacy in the field, their molecular complexity, and the practical and legal limitations for field testing of both gene drive and coupled anti-pathogen traits. To address these challenges, we have evaluated the concept of Integral Gene Drive (IGD) as an alternative paradigm for population replacement. IGDs incorporate a minimal set of molecular components, including both the drive and the anti-pathogen effector elements directly embedded within endogenous genes – an arrangement which we refer to as gene “hijacking”. This design would allow autonomous and non-autonomous IGD traits and strains to be generated, tested, optimized, regulated and imported independently. We performed quantitative modelling comparing IGDs with classical replacement drives and show that selection for the function of the hijacked host gene can significantly reduce the establishment of resistant alleles in the population while hedging drive over multiple genomic loci prolongs the duration of transmission blockage in the face of pre-existing target-site variation. IGD thus has the potential to yield more durable and flexible population replacement traits.</jats:p>
Journal articleBeal J, Haddock-Angelli T, Baldwin G, et al., 2018,
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.
Conference paperGiannari AG, van Logtestijn MDA, Christodoulides P, et al., 2018,
Model Predictive Control for Designing Proactive Therapy of Atopic Dermatitis, European Control Conference (ECC), Publisher: IEEE, Pages: 2387-2392
Journal articleEnrico Bena C, Grob A, Isalan M, et 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 articlePark YK, Dulermo T, Ledesma Amaro R, et al., 2018,
Background:Odd chain fatty acids (odd FAs) have a wide range of applications in therapeutic and nutritional industries, as well as in chemical industries including biofuel. Yarrowia lipolytica is an oleaginous yeast considered a preferred microorganism for the production of lipid-derived biofuels and chemicals. However, it naturally produces negligible amounts of odd chain fatty acids.Results:The possibility of producing odd FAs using Y. lipolytica was investigated. Y. lipolytica wild-type strain was shown able to grow on weak acids; acetate, lactate, and propionate. Maximal growth rate on propionate reached 0.24 ± 0.01 h−1 at 2 g/L, and growth inhibition occurred at concentration above 10 g/L. Wild-type strain accumulated lipids ranging from 7.39 to 8.14% (w/w DCW) depending on the carbon source composition, and odd FAs represented only 0.01–0.12 g/L. We here proved that the deletion of the PHD1 gene improved odd FAs production, which reached a ratio of 46.82% to total lipids. When this modification was transferred to an obese strain, engineered for improving lipid accumulation, further increase odd FAs production reaching a total of 0.57 g/L was shown. Finally, a fed-batch co-feeding strategy was optimized for further increase odd FAs production, which generated 0.75 g/L, the best production described so far in Y. lipolytica.Conclusions:A Y. lipolytica strain able to accumulate high level of odd chain fatty acids, mainly heptadecenoic acid, has been successfully developed. In addition, a fed-batch co-feeding strategy was optimized to further improve lipid accumulation and odd chain fatty acid content. These lipids enriched in odd chain fatty acid can (1) improve the properties of the biodiesel generated from Y. lipolytica lipids and (2) be used as renewable source of odd chain fatty acid for industrial applications. This work paves the way for further improvements in odd chain fatty acids and fatty acid-derived compound production.
Conference paperSchumacher J, Waite C, 2018,
In vivo absolute and relative Nif protein abundances of Klebsiella oxytoca, 13th European Nitrogen Fixation Conference
Journal articlePan W, Yuan Y, Ljung L, et al., 2018,
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.
Conference paperHurault G, Roekevisch E, Szegedi K, et al., 2018,
Development of computational tools to convert severity scores of atopic dermatitis for a probabilistic classification of symptom severity, Annual Meeting of the British-Society-for-Investigative-Dermatology, Publisher: WILEY, Pages: E429-E429, ISSN: 0007-0963
Journal articleCritchley B, Isalan M, Mielcarek M, 2018,
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 articleKreula SM, Kaewphan S, Ginter F, et al., 2018,
Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining, PeerJ, Vol: 6, ISSN: 2167-8359
The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.
Journal articleBlount B, Gowers G, Ho JCH, et al., 2018,
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 articleBolognesi G, Friddin MS, Salehi-Reyhani S, et al., 2018,
Constructing higher-order vesicle assemblies has discipline-spanning potential from responsive soft-matter materials to artificial cell networks in synthetic biology. This potential is ultimately derived from the ability to compartmentalise and order chemical species in space. To unlock such applications, spatial organisation of vesicles in relation to one another must be controlled, and techniques to deliver cargo to compartments developed. Herein, we use optical tweezers to assemble, reconfigure and dismantle networks of cell-sized vesicles that, in different experimental scenarios, we engineer to exhibit several interesting properties. Vesicles are connected through double-bilayer junctions formed via electrostatically controlled adhesion. Chemically distinct vesicles are linked across length scales, from several nanometres to hundreds of micrometres, by axon-like tethers. In the former regime, patterning membranes with proteins and nanoparticles facilitates material exchange between compartments and enables laser-triggered vesicle merging. This allows us to mix and dilute content, and to initiate protein expression by delivering biomolecular reaction components.
Journal articleKaramdad K, Hindley J, Friddin MS, et al., 2018,
Engineering thermoresponsive phase separated vesicles formed via emulsion phase transfer as a content-release platform, Chemical Science, Vol: 9, Pages: 4851-4858, ISSN: 2041-6520
Giant unilamellar vesicles (GUVs) are a well-established tool for the study of membrane biophysics and are increasingly used as artificial cell models and functional units in biotechnology. This trend is driven by the development of emulsion-based generation methods such as Emulsion Phase Transfer (EPT), which facilitates the encapsulation of almost any water-soluble compounds (including biomolecules) regardless of size or charge, is compatible with droplet microfluidics, and allows GUVs with asymmetric bilayers to be assembled. However, the ability to control the composition of membranes formed via EPT remains an open question; this is key as composition gives rise to an array of biophysical phenomena which can be used to add functionality to membranes. Here, we evaluate the use of GUVs constructed via this method as a platform for phase behaviour studies and take advantage of composition-dependent features to engineer thermally-responsive GUVs. For the first time, we generate ternary GUVs (DOPC/DPPC/cholesterol) using EPT, and by compensating for the lower cholesterol incorporation efficiencies, show that these possess the full range of phase behaviour displayed by electroformed GUVs. As a demonstration of the fine control afforded by this approach, we demonstrate release of dye and peptide cargo when ternary GUVs are heated through the immiscibility transition temperature, and show that release temperature can be tuned by changing vesicle composition. We show that GUVs can be individually addressed and release triggered using a laser beam. Our findings validate EPT as a suitable method for generating phase separated vesicles and provide a valuable proof-of-concept for engineering content release functionality into individually addressable vesicles, which could have a host of applications in the development of smart synthetic biosystems.
Journal articleHenrich O, Gutiérrez Fosado YA, Curk T, et 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.
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