Publications
154 results found
Hancock E, Ang J, Papachristodoulou A, et al., 2017, The interplay between feedback and buffering in homeostasis, Cell Systems, Vol: 5, Pages: 498-508.e23, ISSN: 2405-4712
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.
Ceroni F, Furini S, Gorochowski T, et 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.
Borkowski O, Bricio Garberi C, Murgiano M, et 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.
Misirli G, Madsen C, Sainz de Murieta I, et 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.
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.
Foo M, Sawlekar R, Kim J, et 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.
Tomazou M, Stan GB, 2017, Engineering autoregulation in enzymatic degradation based systems for robust dynamics and improved host capacity, Pages: 161-162
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
Kuntz J, Ottobre M, Stan G-B, et 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.
Borkowski O, Ceroni F, Stan GB, et 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.
Pan W, Yuan Y, Ljung L, et al., 2016, Identifying biochemical reaction networks from heterogeneous datasets, 2015 IEEE 54th Annual Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 2525-2530
In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.
Sootla A, Oyarzun DA, Angeli D, et al., 2016, Shaping Pulses to Control Bistable Systems: Analysis, Computation and Counterexamples, Automatica, Vol: 63, Pages: 254-264, ISSN: 1873-2836
In this paper we study how to shape temporal pulses to switch a bistable system between its stable steady states. Our motivation forpulse-based control comes from applications in synthetic biology, where it is generally difficult to implement real-time feedback controlsystems due to technical limitations in sensors and actuators. We show that for monotone bistable systems, the estimation of the set ofall pulses that switch the system reduces to the computation of one non-increasing curve. We provide an efficient algorithm to computethis curve and illustrate the results with a genetic bistable system commonly used in synthetic biology. We also extend these results tomodels with parametric uncertainty and provide a number of examples and counterexamples that demonstrate the power and limitationsof the current theory. In order to show the full potential of the framework, we consider the problem of inducing oscillations in a monotonebiochemical system using a combination of temporal pulses and event-based control. Our results provide an insight into the dynamics ofbistable systems under external inputs and open up numerous directions for future investigation.
Stan GB, 2016, Better, faster, stronger: Shared resources and biomolecular feedback considerations for better engineering of bacterial cells
Pan W, Yuan Y, Goncalves J, et al., 2015, A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems, IEEE Transactions on Automatic Control, Vol: 61, Pages: 182-187, ISSN: 1558-2523
This technical note considers the identification ofnonlinear discrete-time systems with additive process noise butwithout measurement noise. In particular, we propose a methodand its associated algorithm to identify the system nonlinear functionalforms and their associated parameters from a limited numberof time-series data points. For this, we cast this identificationproblem as a sparse linear regression problem and take a Bayesianviewpoint to solve it. As such, this approach typically leads tononconvex optimizations. We propose a convexification procedurerelying on an efficient iterative re-weighted 1-minimization algorithmthat uses general sparsity inducing priors on the parametersof the system and marginal likelihood maximisation. Using thisapproach, we also show how convex constraints on the parameterscan be easily added to the proposed iterative re-weighted1-minimization algorithm. In the supplementary material availableonline (arXiv:1408.3549), we illustrate the effectiveness of theproposed identification method on two classical systems in biologyand physics, namely, a genetic repressilator network and a largescale network of interconnected Kuramoto oscillators.
Quinn JY, Cox RS, Adler A, et al., 2015, SBOL Visual: A Graphical Language for Genetic Designs., PLOS Biology, Vol: 13, ISSN: 1545-7885
Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.
Yuan Y, Rai A, Yeung E, et al., 2015, A Minimal Realization Technique for the Dynamical Structure Function of a Class of LTI Systems, IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, Vol: 4, Pages: 301-311, ISSN: 2325-5870
The dynamical structure function of a linear time invariant (LTI) system reveals causal dependencies among manifest variables without specifying any particular relationships among the unmeasured states of the system. As such, it is a useful representation for complex networks where a coarse description of global system structure is desired without detailing the intricacies of a full state realization. In this paper, we consider the problem of finding a minimal state realization for a given dynamical structure function. Interestingly, some dynamical structure functions require uncontrollable modes in their state realizations to deliver the desired input-output behavior while respecting a specified system structure. As a result, the minimal order necessary to realize a particular dynamical structure function may be greater than that necessary to realize its associated transfer function. Although finding a minimal realization for a given dynamical structure function is difficult in general, we present a straightforward procedure here that works for a simplified class of systems.
Sootla A, Oyarzun DA, Angeli D, et al., 2015, Shaping Pulses to Control Bi-Stable Biological Systems, American Control Conference 2015, Publisher: IEEE, Pages: 3138-3143
In this paper, we present a framework for shaping pulses to control biological systems, and specifically systems in synthetic biology. By shaping we mean computing the magnitude and the length of a pulse, application of which results in reaching the desired control objective. Hence the control signals have only two parameters, which makes these signals amenable to wetlab implementations. We focus on the problem of switching between steady states in a bistable system. We show how to estimate the set of the switching pulses, if the trajectories of the controlled system can be bounded from above and below by the trajectories of monotone systems. We then generalise this result to systems with parametric uncertainty under some mild assumptions on the set of admissible parameters, thus providing some robustness guarantees. We illustrate the results on some example genetic circuits.
Hancock E, Stan G-B, Arpino J, et al., 2015, Simplified mechanistic models of gene regulation for analysis and design, Journal of the Royal Society Interface, Vol: 12, ISSN: 1742-5689
Simplified mechanistic models of gene regulation are fundamental to systems biology and essential for synthetic biology. However, conventional simplified models typically have outputs that are not directly measurable and are based on assumptions that do not often hold under experimental conditions. To resolve these issues, we propose a ‘model reduction’ methodology and simplified kinetic models of total mRNA and total protein concentration, which link measurements, models and biochemical mechanisms. The proposed approach is based on assumptions that hold generally and include typical cases in systems and synthetic biology where conventional models do not hold. We use novel assumptions regarding the ‘speed of reactions’, which are required for the methodology to be consistent with experimental data. We also apply the methodology to propose simplified models of gene regulation in the presence of multiple protein binding sites, providing both biological insights and an illustration of the generality of the methodology. Lastly, we show that modelling total protein concentration allows us to address key questions on gene regulation, such as efficiency, burden, competition and modularity.
Dickens L, Caldas B, Schoenhense B, et al., 2015, The Moveable Feast of Predictive Reward Discounting in Humans, 2nd Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)
Ceroni F, Algar R, Stan G-B, et al., 2015, Quantifying cellular capacity identifies gene expression designs with reduced burden, Nature Methods, Vol: 12, Pages: 415-418, ISSN: 1548-7105
Heterologous gene expression can be a significant burden forcells. Here we describe an in vivo monitor that tracks changesin the capacity of Escherichia coli in real time and can be usedto assay the burden imposed by synthetic constructs and theirparts. We identify construct designs with reduced burden thatpredictably outperformed less efficient designs, despite havingequivalent output.
Stan G, Pan W, Yuan Y, et al., 2015, Online Fault Diagnosis for Nonlinear Power Systems, Automatica, Vol: 55, Pages: 27-36, ISSN: 1873-2836
This paper considers the problem of automatic fault diagnosis for transmission lines in large scale power networks. Since faults in transmission lines threatens stability of the entire power network, fast and reliable fault diagnosis is an important problem in transmission line protection. This work is the first paper exploiting sparse signal recovery for the fault-diagnosis problem in power networks with nonlinear swing-type dynamics. It presents a novel and scalable technique to detect, isolate and identify transmission faults using a relatively small number of observations by exploiting the sparse nature of the faults. Buses in power networks are typically described by second-order nonlinear swing equations. Based on this description, the problem of fault diagnosis for transmission lines is formulated as a compressive sensing or sparse signal recovery problem, which is then solved using a sparse Bayesian formulation. An iterative reweighted `1-minimisation algorithm based on the sparse Bayesian learning update is then derived to solve the fault diagnosis problem efficiently. With the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles at the buses.
Wright O, Delmans M, Stan G-B, et al., 2015, Gene Guard: A Modular Plasnnid System Designed for Biosafety, ACS SYNTHETIC BIOLOGY, Vol: 4, Pages: 307-316, ISSN: 2161-5063
- Author Web Link
- Cite
- Citations: 74
Kelwick R, Kopniczky M, Bower I, et al., 2015, A Forward-Design Approach to Increase the Production of Poly-3-Hydroxybutyrate in Genetically Engineered <i>Escherichia coli</i>, PLOS ONE, Vol: 10, ISSN: 1932-6203
- Author Web Link
- Open Access Link
- Cite
- Citations: 8
Baldwin G, Bayer T, Dickinson R, et al., 2015, Synthetic biology - a primer, ISBN: 9781783268801
Synthetic Biology - A Primer (Revised Edition) presents an updated overview of the field of synthetic biology and the foundational concepts on which it is built. This revised edition includes new literature references, working and updated URL links, plus some new figures and text where progress in the field has been made. The book introduces readers to fundamental concepts in molecular biology and engineering and then explores the two major themes for synthetic biology, namely ‘bottom-up’ and ‘top-down’ engineering approaches. ‘Top-down’ engineering uses a conceptual framework of systematic design and engineering principles focused around the Design-Build-Test cycle and mathematical modelling. The ‘bottom-up’ approach involves the design and building of synthetic protocells using basic chemical and biochemical building blocks from scratch exploring the fundamental basis of living systems. Examples of cutting-edge applications designed using synthetic biology principles are presented, including: the production of novel, microbial synthesis of pharmaceuticals and fine chemicals the design and implementation of biosensors to detect infections and environmental waste. The book also describes the Internationally Genetically Engineered Machine (iGEM) competition, which brings together students and young researchers from around the world to carry out summer projects in synthetic biology. Finally, the primer includes a chapter on the ethical, legal and societal issues surrounding synthetic biology, illustrating the integration of social sciences into synthetic biology research. Final year undergraduates, postgraduates and established researchers interested in learning about the interdisciplinary field of synthetic biology will benefit from this up-to-date primer on synthetic biology.
Pan W, Yuan Y, Ljung L, et al., 2015, Identifying Biochemical Reaction Networks From Heterogeneous Datasets, 2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), Pages: 2525-2530, ISSN: 0743-1546
- Author Web Link
- Cite
- Citations: 7
Sootla A, Oyarzún DA, Angeli D, et al., 2015, Shaping pulses to control bistable biological systems., Publisher: IEEE, Pages: 3138-3143
Rivadeneira PS, Moog CH, Stan G-B, et al., 2014, Mathematical Modeling of HIV Dynamics After Antiretroviral Therapy Initiation: A Review., Biores Open Access, Vol: 3, Pages: 233-241, ISSN: 2164-7844
This review shows the potential ground-breaking impact that mathematical tools may have in the analysis and the understanding of the HIV dynamics. In the first part, early diagnosis of immunological failure is inferred from the estimation of certain parameters of a mathematical model of the HIV infection dynamics. This method is supported by clinical research results from an original clinical trial: data just after 1 month following therapy initiation are used to carry out the model identification. The diagnosis is shown to be consistent with results from monitoring of the patients after 6 months. In the second part of this review, prospective research results are given for the design of individual anti-HIV treatments optimizing the recovery of the immune system and minimizing side effects. In this respect, two methods are discussed. The first one combines HIV population dynamics with pharmacokinetics and pharmacodynamics models to generate drug treatments using impulsive control systems. The second one is based on optimal control theory and uses a recently published differential equation to model the side effects produced by highly active antiretroviral therapy therapies. The main advantage of these revisited methods is that the drug treatment is computed directly in amounts of drugs, which is easier to interpret by physicians and patients.
Rivadeneira PS, Moog CH, Stan G-B, et al., 2014, Mathematical Modeling of HIV Dynamics After Antiretroviral Therapy Initiation: A Clinical Research Study, AIDS RESEARCH AND HUMAN RETROVIRUSES, Vol: 30, Pages: 831-834, ISSN: 0889-2229
- Author Web Link
- Cite
- Citations: 8
Galdzicki M, Clancy KP, Oberortner E, et al., 2014, The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology, NATURE BIOTECHNOLOGY, Vol: 32, Pages: 545-550, ISSN: 1087-0156
- Author Web Link
- Cite
- Citations: 182
Oyarzun DA, Lugagne J-B, Stan G-B, 2014, Noise propagation in synthetic gene circuits for metabolic control, ACS Synthetic Biology, Vol: 4, Pages: 116-125, ISSN: 2161-5063
Dynamic control of enzyme expression can be an effective strategy to engineer robust metabolic pathways. It allows a synthetic pathway to self-regulate in response to changes in bioreactor conditions or the metabolic state of the host. The implementation of this regulatory strategy requires gene circuits that couple metabolic signals with the genetic machinery, which is known to be noisy and one of the main sources of cell-to-cell variability. One of the unexplored design aspects of these circuits is the propagation of biochemical noise between enzyme expression and pathway activity. In this article, we quantify the impact of a synthetic feedback circuit on the noise in a metabolic product in order to propose design criteria to reduce cell-to-cell variability. We consider a stochastic model of a catalytic reaction under negative feedback from the product to enzyme expression. On the basis of stochastic simulations and analysis, we show that, depending on the repression strength and promoter strength, transcriptional repression of enzyme expression can amplify or attenuate the noise in the number of product molecules. We obtain analytic estimates for the metabolic noise as a function of the model parameters and show that noise amplification/attenuation is a structural property of the model. We derive an analytic condition on the parameters that lead to attenuation of metabolic noise, suggesting that a higher promoter sensitivity enlarges the parameter design space. In the theoretical case of a switch-like promoter, our analysis reveals that the ability of the circuit to attenuate noise is subject to a trade-off between the repression strength and promoter strength.
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.