Imperial College London

Guy-Bart Stan

Faculty of EngineeringDepartment of Bioengineering

Reader in Engineering Design for Synthetic Biology
 
 
 
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Contact

 

+44 (0)20 7594 6375g.stan Website

 
 
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Location

 

B703Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

114 results found

Haines M, Storch M, Oyarzun D, Stan G, Baldwin Get al., Riboswitch identification using Ligase-Assisted Selection for the Enrichment of Responsive Ribozymes (LigASERR), Synthetic Biology, ISSN: 2397-7000

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

Madsen C, Goni Moreno A, Palchick Z P U, Roehner N, Bartley B, Bhatia S, Bhakta S, Bissell M, Clancy K, Cox RS, Gorochowski T, Grunberg R, Luna A, McLaughlin J, Nguyen T, Le Novere N, Pocock M, Sauro H, Scott-Brown J, Sexton JT, Stan G-B, Tabor JJ, Voigt CA, Zundel Z, Myers C, Beal J, Wipat Aet al., 2019, Synthetic Biology Open Language Visual (SBOL Visual) version 2.1, Journal of Integrative Bioinformatics, 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.

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

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

, 2018,

Journal article

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.

Working paper

Kylilis N, Riangrungroj P, Lai H-E, Salema V, Fernandez LA, Stan G-B, Freemont P, Polizzi Ket 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.

Working paper

, 2018,

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

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

Tomazou M, Barahona M, Polizzi K, Stan Get 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 allowindependent modulation of amplitude and period. This need is currently unmet. Here, we demonstratecomputationally 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 tuneability,that is, a broad dynamic range of periods and amplitudes accessible through the input “dials”. Ourapproach decouples frequency and amplitude modulation by incorporating an orthogonal “sinkmodule” where the key molecular species are channelled for enzymatic degradation. This “sinkmodule” maintains fast oscillation cycles while alleviating the translational coupling between theoscillator’s transcription factors and output. We characterise the behaviour of our re-designedoscillators over a broad range of physiologically reasonable parameters, explain why this facilitatesbroader function and control, and provide general design principles for building synthetic geneticoscillators that are more precisely controllable.

Journal article

Avalos JL, Toettcher JE, Lalanne J-B, Li G-W, Gomes ALC, Johns NI, Wang HH, Ellis T, Stan G-B, Mure LS, Panda S, Cooper HM, Fernandez-Martinez J, Rout MP, Akey CW, Kim SJ, Sali A, Bastarache L, Denny JCet al., 2018, Principles of Systems Biology, No. 28, CELL SYSTEMS, Vol: 6, Pages: 397-399, ISSN: 2405-4712

Journal article

Borkowski O, Bricio C, Murgiano M, Rothschild-Mancinelli B, Stan G, Ellis Tet al., 2018, Cell-free prediction of protein expression costs for growing cells, Nature Communications, Vol: 9, 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.

Journal article

Tomazou M, Stan G-B, 2018, Portable gene expression guaranteed, NATURE BIOTECHNOLOGY, Vol: 36, Pages: 313-314, ISSN: 1087-0156

Journal article

Ceroni F, Boo A, Furini S, Gorochowski T, Ladak Y, Awan A, Gilbert C, Stan G, Ellis Tet 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.

Journal article

Cox R, Madsen C, McLaughlin J, Nguyen T, Roehner N, Bartley B, Bhatia S, Bissell M, Clancy K, Gorochowski T, Grunberg R, Luna A, Le Novere N, Pocock M, Sauro H, Sexton J, Stan G, Tabor J, Voigt C, Zundel Z, Myers C, Beal J, Wipat Aet 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.

Journal article

Jonas FRH, Royle KE, Aw R, Stan G, Polizzi KMet 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.

Journal article

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.

Working paper

Hancock E, Ang J, Papachristodoulou A, Stan Get al., 2017, The interplay between feedback and buffering in homeostasis, Cell Systems, Vol: 5, Pages: 498-508.e23, ISSN: 2405-4720

Feedback and buffering---the use of reservoirs of molecules to maintain molecular concentrations---are the primary mechanisms for robust regulation in biochemical processes. The universal principles behind their combined effect, however, have not been studied before. Here, we determine the fundamental forms of cooperation and tradeoff between buffering and feedback. We find that negative feedback regulates slow-changing components of time-varying signals, while buffering regulates fast-changing components, consistent with observations of both ATP and pH regulation. We further find that buffering stabilizes feedback and improves its effectiveness, but also introduces molecular noise. In addition, we show that rapid-acting buffering imparts negative derivative feedback, while slow-acting buffering can result in feedforward filtering of specific signals; both are control strategies widely used in technology. Finally, we discover an empirical cross-species relationship between feedback in glycolysis and ATP buffering that is consistent with our findings.

Journal article

Ceroni F, Furini S, Gorochowski T, Boo A, Borkowski O, Ladak Y, Awan A, Gilbert C, Stan G-B, Ellis Tet al., 2017, Burden-driven feedback control of gene expression, Publisher: Cold Spring Harbor Laboratory

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.

Working paper

Borkowski O, Bricio Garberi C, Murgiano M, Stan G-B, Ellis Tet al., 2017, Cell-free prediction of protein expression costs for growing cells, Publisher: bioRxiv

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 gene is a major challenge, as multiple processes and factors determine translation efficiency. Here, to enable prediction of the cost of gene expression in bacteria, we describe a standard cell-free lysate assay that determines the relationship between in vivo and cell-free measurements and γ, a relative measure of the resource consumption when a given protein is expressed. When combined with a computational model of translation, this enables prediction of 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.

Working paper

Misirli G, Madsen C, Sainz de Murieta I, Bultelle M, Flanagan K, Pocock M, Halllinan J, McLaughlin J, Clark-Casey J, Lyne M, Micklem G, Stan G, Kitney R, Wipat Aet al., 2017, Constructing synthetic biology workflows in the cloud, Engineering Biology, Vol: 1, Pages: 61-65, ISSN: 2398-6182

The synthetic biology design process has traditionally been heavily dependent upon manual searching, acquisition and integration of existing biological data. A large amount of such data is already available from Internet-based resources, but data exchange between these resources is often undertaken manually. Automating the communication between different resources can be done by the generation of computational workflows to achieve complex tasks that cannot be carried out easily or efficiently by a single resource. Computational workflows involve the passage of data from one resource, or process, to another in a distributed computing environment. In a typical bioinformatics workflow, the predefined order in which processes are invoked in a synchronous fashion and are described in a workflow definition document. However, in synthetic biology the diversity of resources and manufacturing tasks required favour a more flexible model for process execution. Here, the authors present the Protocol for Linking External Nodes (POLEN), a Cloud-based system that facilitates synthetic biology design workflows that operate asynchronously. Messages are used to notify POLEN resources of events in real time, and to log historical events such as the availability of new data, enabling networks of cooperation. POLEN can be used to coordinate the integration of different synthetic biology resources, to ensure consistency of information across distributed repositories through added support for data standards, and ultimately to facilitate the synthetic biology life cycle for designing and implementing biological systems.

Journal article

Walker BJ, stan GB, Polizzi KM, 2017, Intracellular delivery of biologic therapeutics by bacterial secretion systems, Expert Reviews in Molecular Medicine, Vol: 19, ISSN: 1462-3994

Biologics are a promising new class of drugs based on complex macromolecules such as proteins and nucleic acids. However, delivery of these macromolecules into the cytoplasm of target cells remains a significant challenge. Here we present one potential solution: bacterial nanomachines that have evolved over millions of years to efficiently deliver proteins and nucleic acids across cell membranes and between cells. In this review, we provide a brief overview of the different bacterial systems capable of direct delivery into the eukaryotic cytoplasm and the medical applications for which they are being investigated, along with a perspective on the future directions of this exciting field.

Journal article

Foo M, Sawlekar R, Kim J, Bates DG, Stan G-B, Kulkarni Vet al., 2017, Biomolecular implementation of nonlinear system theoretic operators, European Control Conference (ECC), Publisher: IEEE, Pages: 1824-1831

Synthesis of biomolecular circuits for controlling molecular-scale processes is an important goal of synthetic biology with a wide range of in vitro and in vivo applications, including biomass maximization, nanoscale drug delivery, and many others. In this paper, we present new results on how abstract chemical reactions can be used to implement commonly used system theoretic operators such as the polynomial functions, rational functions and Hill-type nonlinearity. We first describe how idealised versions of multi-molecular reactions, catalysis, annihilation, and degradation can be combined to implement these operators. We then show how such chemical reactions can be implemented using enzyme-free, entropy-driven DNA reactions. Our results are illustrated through three applications: (1) implementation of a Stan-Sepulchre oscillator, (2) the computation of the ratio of two signals, and (3) a PI+antiwindup controller for regulating the output of a static nonlinear plant.

Conference paper

Tomazou M, Stan GB, 2017, Engineering autoregulation in enzymatic degradation based systems for robust dynamics and improved host capacity, Pages: 161-162

Conference paper

Pan W, Menolascina F, Stan G, 2016, Online Model Selection for Synthetic Gene Networks, IEEE Conference on Decision and Control, Publisher: IEEE

Control algorithms combined with microfluidicdevices and microscopy have enabled in vivo real-time controlof protein expression in synthetic gene networks. Most controlalgorithms rely on the a priori availability of mathematicalmodels of the gene networks to be controlled. These modelsare typically black/grey box models, which can be obtainedthrough the use of data-driven techniques developed in thecontext of systems identification. Data-driven inference of bothmodel structure and parameters is the main focus of thispaper. There are two main challenges associated with theinference of dynamical models for real-time control of generegulatory networks in living cells. Since biological systemsare typically evolving over time, the first challenge stemsfrom the fact that model selection needs to be done online,which prevents the application of computationally expensiveidentification algorithms iterating through large amounts ofstreaming data. The second challenge consists in performingnonlinear model selection, which is typically too burdensomefor Kalman filtering related techniques due the heterogeneityand nonlinearity of the candidate models. In this paper,we combine sparse Bayesian techniques with classic Kalmanfiltering techniques to tackle these challenges

Conference paper

Kuntz J, Ottobre M, Stan G-B, Barahona Met al., 2016, Bounding stationary averages of polynomial diffusions via semidefinite programming, SIAM Journal on Scientific Computing, Vol: 38, Pages: A3891-A3920, ISSN: 1095-7197

We introduce an algorithm based on semidefinite programming that yields increasing (resp.decreasing) sequences of lower (resp. upper) bounds on polynomial stationary averages of diffusionswith polynomial drift vector and diffusion coefficients. The bounds are obtained byoptimising an objective, determined by the stationary average of interest, over the set of realvectors defined by certain linear equalities and semidefinite inequalities which are satisfied bythe moments of any stationary measure of the diffusion. We exemplify the use of the approachthrough several applications: a Bayesian inference problem; the computation of Lyapunov exponentsof linear ordinary differential equations perturbed by multiplicative white noise; and areliability problem from structural mechanics. Additionally, we prove that the bounds convergeto the infimum and supremum of the set of stationary averages for certain SDEs associated withthe computation of the Lyapunov exponents, and we provide numerical evidence of convergencein more general settings.

Journal article

Borkowski O, Ceroni F, Stan GB, Ellis Tet al., 2016, Overloaded and stressed: whole-cell considerations for bacterial synthetic biology., Current Opinion in Microbiology, Vol: 33, Pages: 123-130, ISSN: 1879-0364

The predictability and robustness of engineered bacteria depend on the many interactions between synthetic constructs and their host cells. Expression from synthetic constructs is an unnatural load for the host that typically reduces growth, triggers stresses and leads to decrease in performance or failure of engineered cells. Work in systems and synthetic biology has now begun to address this through new tools, methods and strategies that characterise and exploit host-construct interactions in bacteria. Focusing on work in E. coli, we review here a selection of the recent developments in this area, highlighting the emerging issues and describing the new solutions that are now making the synthetic biology community consider the cell just as much as they consider the construct.

Journal article

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