Publications
154 results found
Sarvari P, Ingram D, Stan G-B, 2021, A modelling framework linking resource-based stochastic translation to the optimal design of synthetic constructs, Biology, Vol: 10, ISSN: 2079-7737
The effect of gene expression burden on engineered cells has motivated the use of “whole-cell models” (WCMs) that use shared cellular resources to predict how unnatural gene expression affects cell growth. A common problem with many WCMs is their inability to capture translation in sufficient detail to consider the impact of ribosomal queue formation on mRNA transcripts. To address this, we have built a “stochastic cell calculator” (StoCellAtor) that combines a modified TASEP with a stochastic implementation of an existing WCM. We show how our framework can be used to link a synthetic construct’s modular design (promoter, ribosome binding site (RBS) and codon composition) to protein yield during continuous culture, with a particular focus on the effects of low-efficiency codons and their impact on ribosomal queues. Through our analysis, we recover design principles previously established in our work on burden-sensing strategies, namely that changing promoter strength is often a more efficient way to increase protein yield than RBS strength. Importantly, however, we show how these design implications can change depending on both the duration of protein expression, and on the presence of ribosomal queues.
Ouldridge T, Stan G-B, Bae W, 2020, In situ generation of RNA complexes for synthetic molecular strand displacement circuits in autonomous systems, Nano Letters: a journal dedicated to nanoscience and nanotechnology, Vol: 21, Pages: 265-271, ISSN: 1530-6984
Synthetic molecular circuits implementing DNA or RNA strand-displacement reactions can be used to build complex systems such as molecular computers and feedback control systems. Despite recent advances, application of nucleic acid-based circuits in vivo remains challenging due to a lack of efficient methods to produce their essential components, namely, multistranded complexes known as gates, in situ, i.e., in living cells or other autonomous systems. Here, we propose the use of naturally occurring self-cleaving ribozymes to cut a single-stranded RNA transcript into a gate complex of shorter strands, thereby opening new possibilities for the autonomous and continuous production of RNA strands in a stoichiometrically and structurally controlled way.
Frei T, Cella F, Tedeschi F, et al., 2020, Characterization and mitigation of gene expression burden in mammalian cells, Nature Communications, Vol: 11, ISSN: 2041-1723
Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.
Baig H, Fontanarrosa P, Kulkarni V, et al., 2020, Synthetic biology open language visual (SBOL visual) version 2.2, Journal of Integrative Bioinformatics, Vol: 17, Pages: 1-85, ISSN: 1613-4516
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.2 of SBOL Visual, which builds on the prior SBOL Visual 2.1 in several ways. First, the grounding of molecular species glyphs is changed from BioPAX to SBO, aligning with the use of SBO terms for interaction glyphs. Second, new glyphs are added for proteins, introns, and polypeptide regions (e. g., protein domains), the prior recommended macromolecule glyph is deprecated in favor of its alternative, and small polygons are introduced as alternative glyphs for simple chemicals.
Misirli G, Beal J, Gorochowski TE, et al., 2020, SBOL visual 2 ontology, ACS Synthetic Biology, Vol: 9, Pages: 972-977, ISSN: 2161-5063
Standardizing the visual representation of genetic parts and circuits is essential for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has up to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology.
Misirli G, Beal J, Gorochowski TE, et al., 2020, SBOL Visual 2 Ontology
<jats:title>Abstract</jats:title><jats:p>Standardising the visual representation of genetic parts and circuits is vital for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology.</jats:p><jats:sec><jats:title>Graphical TOC Entry</jats:title><jats:fig id="ufig1" position="anchor" orientation="portrait"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="918417v3_ufig1" position="float" orientation="portrait" /></jats:fig></jats:sec>
Cabello-Garcia J, Bae W, Stan GBV, et al., 2020, Handhold-mediated strand displacement: A DNA-based mechanism to generate out-of-equilibrium complexes through templated reactions, Pages: 77-78
Sootla A, Stan G-B, Ernst D, 2020, Solving Optimal Control Problems for Monotone Systems Using the Koopman Operator, Lecture Notes in Control and Information Sciences, Publisher: Springer International Publishing, Pages: 283-312, ISBN: 9783030357122
© Springer Nature Switzerland AG 2020. Koopman operator theory offers numerous techniques for analysis and control of complex systems. In particular, in this chapter we will argue that for the problem of convergence to an equilibrium, the Koopman operator can be used to take advantage of the geometric properties of controlled systems, thus making the optimal solutions more transparent, and easier to analyze and implement. The motivation for the study of the convergence problem comes from biological applications, where easy-to-implement and easy-to-analyze solutions are of particular value. At the moment, theoretical results have been developed for a class of nonlinear systems called monotone systems. However, the core ideas presented here can be applied heuristically to non-monotone systems. Furthermore, the convergence problem can serve as a building block for solving other control problems such as switching between stable equilibria or inducing oscillations. These applications are illustrated in biologically inspired numerical examples.
Plesa T, Stan G-B, Ouldridge TE, et al., 2019, Robust control of biochemical reaction networks via stochastic morphing
Synthetic biology is an interdisciplinary field aiming to design biochemicalsystems with desired behaviors. To this end, molecular controllers have beendeveloped which, when embedded into a pre-existing ambient biochemical network,control the dynamics of the underlying target molecular species. Whenintegrated into smaller compartments, such as biological cells in vivo, orvesicles in vitro, controllers have to be calibrated to factor in the intrinsicnoise. In this context, molecular controllers put forward in the literaturehave focused on manipulating the mean (first moment), and reducing the variance(second moment), of the target species. However, many critical biochemicalprocesses are realized via higher-order moments, particularly the number andconfiguration of the modes (maxima) of the probability distributions. To bridgethe gap, a controller called stochastic morpher is put forward in this paper,inspired by gene-regulatory networks, which, under suitable time-scaleseparations, morphs the probability distribution of the target species into adesired predefined form. The morphing can be performed at the lower-resolution,allowing one to achieve desired multi-modality/multi-stability, and at thehigher-resolution, allowing one to achieve arbitrary probability distributions.Properties of the controller, such as robust perfect adaptation andconvergence, are rigorously established, and demonstrated on various examples.Also proposed is a blueprint for an experimental implementation of stochasticmorpher.
Kuntz Nussio J, Thomas P, Stan GB, et al., 2019, Bounding the stationary distributions of the chemical master equation via mathematical programming, Journal of Chemical Physics, Vol: 151, 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.
Haines M, Storch M, Oyarzun D, et al., 2019, Riboswitch identification using Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR), Synthetic Biology, Vol: 4, Pages: 1-10, ISSN: 2397-7000
In vitro selection of ligand-responsive ribozymes can identify rare, functional sequences from large libraries. While powerful, key caveats of this approach include lengthy and demanding experimental workflows; unpredictable experimental outcomes and unknown functionality of enriched sequences in vivo. To address the first of these limitations we developed Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR). LigASERR is scalable, amenable to automation and requires less time to implement compared to alternative methods. To improve the predictability of experiments, we modelled the underlying selection process, predicting experimental outcomes based on sequence and population parameters. We applied this new methodology and model to the enrichment of a known, in vitro-selected sequence from a bespoke library. Prior to implementing selection, conditions were optimised and target sequence dynamics accurately predicted for the majority of the experiment. In addition to enriching the target sequence, we identified two new, theophylline-activated ribozymes. Notably, all three sequences yielded riboswitches functional in Escherichia coli, suggesting LigASERR and similar in vitro selection methods can be utilised for generating functional riboswitches in this organism.
Madsen C, Goni Moreno A, Palchick Z P U, et al., 2019, Synthetic Biology Open Language Visual (SBOL Visual) version 2.1, Journal of Integrative Bioinformatics, Vol: 16, Pages: 1-78, ISSN: 1613-4516
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species . Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.1 of SBOL Visual, which builds on the prior SBOL Visual 2.0 standard by expanding diagram syntax to include methods for showing modular structure and mappings between elements of a system, interactions arrows that can split or join (with the glyph at the split or join indicating either superposition or a chemical process), and adding new glyphs for indicating genomic context (e.g., integration into a plasmid or genome) and for stop codons.
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.
Kuntz J, Thomas P, Stan G-B, et 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.
Kylilis N, Riangrungroj P, Lai H-E, et 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.
Tuza ZA, Stan G-B, 2019, An Automatic Sparse Model Estimation Method Guided by Constraints That Encode System Properties, 18th European Control Conference (ECC), Publisher: IEEE, Pages: 2171-2176
Tuza ZA, Stan G-B, 2018, Characterization of biologically relevant network structures form time-series data, Publisher: arXiv
High-throughput data acquisition in synthetic biology leads to an abundance of data that need to be processed and aggregated into useful biological models. Building dynamical models based on this wealth of data is of paramount importance to understand and optimize designs of synthetic biology constructs. However, building models manually for each data set is inconvenient and might become infeasible for highly complex synthetic systems. In this paper, we present state-of-the-art system identification techniques and combine them with chemical reaction network theory (CRNT) to generate dynamic models automatically. On the system identification side, Sparse Bayesian Learning offers methods to learn from data the sparsest set of dictionary functions necessary to capture the dynamics of the system into ODE models; on the CRNT side, building on such sparse ODE models, all possible network structures within a given parameter uncertainty region can be computed. Additionally, the system identification process can be complemented with constraints on the parameters to, for example, enforce stability or non-negativity---thus offering relevant physical constraints over the possible network structures. In this way, the wealth of data can be translated into biologically relevant network structures, which then steers the data acquisition, thereby providing a vital step for closed-loop system identification.
Kylilis N, Riangrungroj P, Lai H-E, et al., 2018, A low-cost biological agglutination assay for medical diagnostic applications, Publisher: American Chemical Society
Affordable, easy-to-use diagnostic tests that can be readily deployed for point-of-care (POC) testing are key in addressing challenges in the diagnosis of medical conditions and for improving global health in general. Ideally, POC diagnostic tests should be highly selective for the biomarker, user-friendly, have a flexible design architecture and a low cost of production. Here we developed a novel agglutination assay based on whole E. coli cells surface-displaying nanobodies which bind selectively to a target protein analyte. As a proof-of-concept, we show the feasibility of this design as a new diagnostic platform by the detection of 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 supported using straight-forward design rules and a mathematical model. Finally, we re-engineer E. coli cells for the detection of a medically relevant biomarker by the display of two different antibodies against the 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 both field-testing in the developing world, emergency or disaster relief sites as well as routine medical testing and personalized medicine.
O'Clery N, Yuan Y, Stan G-B, et 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.
Kylilis N, Tuza ZA, Stan G, et al., 2018, Tools for engineering coordinated system behaviour in synthetic microbial consortia, Nature Communications, Vol: 9, Pages: 1-9, 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.
Pan W, Yuan Y, Ljung L, et 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.
Ceroni F, Boo A, Furini S, et al., 2018, Burden-driven feedback control of gene expression, Nature Methods, Vol: 15, Pages: 387-393, ISSN: 1548-7091
Cells use feedback regulation to ensure robust growth despite fluctuating demands for resources and differing environmental conditions. However, the expression of foreign proteins from engineered constructs is an unnatural burden that cells are not adapted for. Here we combined RNA-seq with an in vivo assay to identify the major transcriptional changes that occur in Escherichia coli when inducible synthetic constructs are expressed. We observed that native promoters related to the heat-shock response activated expression rapidly in response to synthetic expression, regardless of the construct. Using these promoters, we built a dCas9-based feedback-regulation system that automatically adjusts the expression of a synthetic construct in response to burden. Cells equipped with this general-use controller maintained their capacity for native gene expression to ensure robust growth and thus outperformed unregulated cells in terms of protein yield in batch production. This engineered feedback is to our knowledge the first example of a universal, burden-based biomolecular control system and is modular, tunable and portable.
Tomazou M, Barahona M, Polizzi K, et al., 2018, Computational re-design of synthetic genetic oscillators for independent amplitude and frequency modulation, Cell Systems, Vol: 6, Pages: 508-520.e5, ISSN: 2405-4712
To perform well in biotechnology applications, synthetic genetic oscillators must be engineered to allow independent modulation of amplitude and period. This need is currently unmet. Here, we demonstrate computationally how two classic genetic oscillators, the dual-feedback oscillator and the repressilator, can be re-designed to provide independent control of amplitude and period and improve tunability—that is, a broad dynamic range of periods and amplitudes accessible through the input “dials.” Our approach decouples frequency and amplitude modulation by incorporating an orthogonal “sink module” where the key molecular species are channeled for enzymatic degradation. This sink module maintains fast oscillation cycles while alleviating the translational coupling between the oscillator's transcription factors and output. We characterize the behavior of our re-designed oscillators over a broad range of physiologically reasonable parameters, explain why this facilitates broader function and control, and provide general design principles for building synthetic genetic oscillators that are more precisely controllable.
Avalos JL, Toettcher JE, Lalanne J-B, et al., 2018, Principles of Systems Biology, No. 28, CELL SYSTEMS, Vol: 6, Pages: 397-399, ISSN: 2405-4712
Borkowski O, Bricio C, Murgiano M, et al., 2018, Cell-free prediction of protein expression costs for growing cells, Nature Communications, Vol: 9, Pages: 1-11, ISSN: 2041-1723
Translating heterologous proteins places significant burden on host cells, consuming expression resources leading to slower cell growth and productivity. Yet predicting the cost of protein production for any given gene is a major challenge, as multiple processes and factors combine to determine translation efficiency. To enable prediction of the cost of gene expression in bacteria, we describe here a standard cell-free lysate assay that provides a relative measure of resource consumption when a protein coding sequence is expressed. These lysate measurements can then be used with a computational model of translation to predict the in vivo burden placed on growing E. coli cells for a variety of proteins of different functions and lengths. Using this approach, we can predict the burden of expressing multigene operons of different designs and differentiate between the fraction of burden related to gene expression compared to action of a metabolic pathway.
Tomazou M, Stan G-B, 2018, Portable gene expression guaranteed, NATURE BIOTECHNOLOGY, Vol: 36, Pages: 313-314, ISSN: 1087-0156
Cox R, Madsen C, McLaughlin J, et al., 2018, Synthetic Biology Open Language Visual (SBOL Visual) Version 2.0, Journal of Integrative Bioinformatics, Vol: 15, ISSN: 1613-4516
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.
Jonas FRH, Royle KE, Aw R, et al., 2018, Investigating the consequences of asymmetric endoplasmic reticulum inheritance in Saccharomyces cerevisiae under stress using a combination of single cell measurements and mathematical modelling, Synthetic and Systems Biotechnology, Vol: 3, Pages: 64-75, ISSN: 2405-805X
Adaptation allows organisms to maintain a constant internal environment, which is optimised for growth. The unfolded protein response (UPR) is an example of a feedback loop that maintains endoplasmic reticulum (ER) homeostasis, and is characteristic of how adaptation is often mediated by transcriptional networks. The more recent discovery of asymmetric division in maintaining ER homeostasis, however, is an example of how alternative non-transcriptional pathways can exist, but are overlooked by gold standard transcriptomic or proteomic population-based assays. In this study, we have used a combination of fluorescent reporters, flow cytometry and mathematical modelling to explore the relative roles of asymmetric cell division and the UPR in maintaining ER homeostasis. Under low ER stress, asymmetric division leaves daughter cells with an ER deficiency, necessitating activation of the UPR and prolonged cell cycle during which they can recover ER functionality before growth. Mathematical analysis of and simulation results from our mathematical model reinforce the experimental observations that low ER stress primarily impacts the growth rate of the daughter cells. These results demonstrate the interplay between homeostatic pathways and the importance of exploring sub-population dynamics to understand population adaptation to quantitatively different stresses.
Tuza ZA, Stan G-B, 2018, Characterization of Biologically Relevant Network Structures form Time-series Data., Publisher: IEEE, Pages: 1089-1095
Kylilis N, Stan G-B, Polizzi K, 2017, Tools for engineering coordinated system behaviour in synthetic microbial consortia, Publisher: bioRxiv
Advancing synthetic biology to the multicellular level requires the development of multiple orthogonal cell-to-cell communication channels to 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 reducing signal leakage between systems of cognate AHL/transcription factor pairs. However, the use of non-cognate signals as a design feature has received limited attention so far. Here, we demonstrate the largest library of AHL-receiver devices constructed to date with all cognate and non-cognate chemical signal interactions quantified and we develop a software tool that allows automated selection of orthogonal chemical 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 will facilitate the design of synthetic microbial consortia for novel applications including distributed bio-computation, increased bioprocess efficiency, cell specialisation, and spatial organisation.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.