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Bowyer JE, Ding C, Weinberg BH, Wong WW, Bates DG. A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits. PLoS Comput Biol 2020; 16:e1007849. [PMID: 33338034 PMCID: PMC7781486 DOI: 10.1371/journal.pcbi.1007849] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 01/04/2021] [Accepted: 11/03/2020] [Indexed: 11/26/2022] Open
Abstract
Boolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision. After calibrating the model on experimental data from two circuits, we demonstrate close agreement between model outputs and data on the other 111 circuits that have so far been experimentally constructed using the 2-input BLADE platform. Model simulations of the remaining 143 circuits that have yet to be tested experimentally predict excellent performance of the 2-input BLADE platform across the range of possible circuits. Circuits from both the tested and untested subsets that perform less well consist of a disproportionally high number of STOP sequences. Model predictions suggested that circuit performance declines with a decrease in recombinase expression and new experimental data was generated that confirms this relationship.
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Affiliation(s)
- Jack E. Bowyer
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry, United Kingdom
| | - Chloe Ding
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Benjamin H. Weinberg
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Wilson W. Wong
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Declan G. Bates
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry, United Kingdom
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Darlington APS, Bates DG. Architectures for Combined Transcriptional and Translational Resource Allocation Controllers. Cell Syst 2020; 11:382-392.e9. [PMID: 32937113 DOI: 10.1016/j.cels.2020.08.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/23/2020] [Accepted: 08/21/2020] [Indexed: 12/23/2022]
Abstract
Recent work on engineering synthetic cellular circuitry has shown that non-regulatory interactions mediated by competition for gene expression resources can result in degraded performance or even failure. Transcriptional and translational resource allocation controllers based on orthogonal circuit-specific gene expression machinery have separately been shown to improve modularity and circuit performance. Here, we investigate the potential advantages, challenges, and design trade-offs involved in combining transcriptional and translational controllers into a "dual resource allocation control system." We show that separately functional, translational, and transcriptional controllers cannot generally be combined without extensive redesign. We analyze candidate architectures for direct design of dual resource allocation controllers and propose modifications to improve their performance (in terms of decoupling and expression level) and robustness. We show that dual controllers can be built that are composed only of orthogonal gene expression resources and demonstrate that such designs offer both superior performance and robustness characteristics.
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Affiliation(s)
- Alexander P S Darlington
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, UK
| | - Declan G Bates
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, UK.
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3
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Rohrs JA, Wang P, Finley SD. Understanding the Dynamics of T-Cell Activation in Health and Disease Through the Lens of Computational Modeling. JCO Clin Cancer Inform 2020; 3:1-8. [PMID: 30689404 PMCID: PMC6593125 DOI: 10.1200/cci.18.00057] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
T cells in the immune system are activated by binding to foreign peptides (from an external pathogen) or mutant peptide (derived from endogenous proteins) displayed on the surface of a diseased cell. This triggers a series of intracellular signaling pathways, which ultimately dictate the response of the T cell. The insights from computational models have greatly improved our understanding of the mechanisms that control T-cell activation. In this review, we focus on the use of ordinary differential equation–based mechanistic models to study T-cell activation. We highlight several examples that demonstrate the models’ utility in answering specific questions related to T-cell activation signaling, from antigen discrimination to the feedback mechanisms that initiate transcription factor activation. In addition, we describe other modeling approaches that can be combined with mechanistic models to bridge time scales and better understand how intracellular signaling events, which occur on the order of seconds to minutes, influence phenotypic responses of T-cell activation, which occur on the order of hours to days. Overall, through concrete examples, we emphasize how computational modeling can be used to enable the rational design and optimization of immunotherapies.
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Affiliation(s)
| | - Pin Wang
- University of Southern California, Los Angeles, CA
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Bowyer JE, Chakravarti D, Wong WW, Bates DG. Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit. ENGINEERING BIOLOGY 2020; 4:10-19. [PMID: 36970230 PMCID: PMC9996713 DOI: 10.1049/enb.2019.0020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/30/2020] [Accepted: 02/10/2020] [Indexed: 12/30/2022] Open
Abstract
Inducible genetic switches based on tyrosine recombinase-based DNA excision are a promising platform for the regulation and control of chimeric antigen receptor (CAR) T cell activity in cancer immunotherapy. These switches exploit the increased stability of DNA excision in tyrosine recombinases through an inversion-excision circuit design. Here, the authors develop the first mechanistic mathematical model of switching dynamics in tyrosine recombinases and validate it against experimental data through both global optimisation and statistical approximation approaches. Analysis of this model provides guidelines regarding which system parameters are best suited to experimental tuning in order to establish optimal switch performance in vivo. In particular, they find that the switching response can be made significantly faster by increasing the concentration of the inducer drug 4-OHT and/or by using promoters generating higher expression levels of the FlpO recombinase.
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Affiliation(s)
- Jack E. Bowyer
- School of Engineering University of Warwick Coventry CV4 7AL UK
- Warwick Integrative Synthetic Biology Centre Coventry CV4 7AL UK
| | - Deboki Chakravarti
- Department of Biomedical Engineering Boston University Boston MA 02215 USA
- Biological Design Center Boston University Boston MA 02215 USA
| | - Wilson W. Wong
- Department of Biomedical Engineering Boston University Boston MA 02215 USA
- Biological Design Center Boston University Boston MA 02215 USA
| | - Declan G. Bates
- School of Engineering University of Warwick Coventry CV4 7AL UK
- Warwick Integrative Synthetic Biology Centre Coventry CV4 7AL UK
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Boada Y, Vignoni A, Picó J. Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise. ACS Synth Biol 2017; 6:1903-1912. [PMID: 28581725 DOI: 10.1021/acssynbio.7b00087] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Stochastic fluctuations in gene expression trigger both beneficial and harmful consequences for cell behavior. Therefore, achieving a desired mean protein expression level while minimizing noise is of interest in many applications, including robust protein production systems in industrial biotechnology. Here, we consider a synthetic gene circuit combining intracellular negative feedback and cell-to-cell communication based on quorum sensing. Accounting for both intrinsic and extrinsic noise, stochastic simulations allow us to analyze the capability of the circuit to reduce noise strength as a function of its parameters. We obtain mean expression levels and noise strengths for all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in Escherichia coli. Our in silico experiments, validated by preliminary in vivo results, reveal significant noise attenuation in gene expression through the interplay between quorum sensing and negative feedback and highlight the differential role that they play in regard to intrinsic and extrinsic noise.
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Affiliation(s)
- Yadira Boada
- Institut
d’Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - Alejandro Vignoni
- Center
for Systems Biology Dresden, Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhaurstr. 108, 01307 Dresden, Germany
| | - Jesús Picó
- Institut
d’Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
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Hancock EJ, Ang J, Papachristodoulou A, Stan GB. The Interplay between Feedback and Buffering in Cellular Homeostasis. Cell Syst 2017; 5:498-508.e23. [PMID: 29055671 DOI: 10.1016/j.cels.2017.09.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 05/09/2017] [Accepted: 09/20/2017] [Indexed: 10/18/2022]
Abstract
Buffering, the use of reservoirs of molecules to maintain concentrations of key molecular species, and negative feedback are the primary known mechanisms for robust homeostatic regulation. To our knowledge, however, the fundamental principles behind their combined effect have not been elucidated. Here, we study the interplay between buffering and negative feedback in the context of cellular homeostasis. We show that negative feedback counteracts slow-changing disturbances, whereas buffering counteracts fast-changing disturbances. Furthermore, feedback and buffering have limitations that create trade-offs for regulation: instability in the case of feedback and molecular noise in the case of buffering. However, because buffering stabilizes feedback and feedback attenuates noise from slower-acting buffering, their combined effect on homeostasis can be synergistic. These effects can be explained within a traditional control theory framework and are consistent with experimental observations of both ATP homeostasis and pH regulation in vivo. These principles are critical for studying robustness and homeostasis in biology and biotechnology.
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Affiliation(s)
- Edward J Hancock
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia; Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia.
| | - Jordan Ang
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK; Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK
| | | | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK; Centre for Synthetic Biology and Innovation, Imperial College London, London SW7 2AZ, UK.
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Otero-Muras I, Banga JR. Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality. ACS Synth Biol 2017; 6:1180-1193. [PMID: 28350462 DOI: 10.1021/acssynbio.6b00306] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC,
Spanish National Research Council, Vigo, 36208, Spain
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Boada Y, Reynoso-Meza G, Picó J, Vignoni A. Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC SYSTEMS BIOLOGY 2016; 10:27. [PMID: 26968941 PMCID: PMC4788947 DOI: 10.1186/s12918-016-0269-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 02/16/2016] [Indexed: 12/22/2022]
Abstract
Background Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. Results We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. Conclusion The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0269-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yadira Boada
- Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Valencia, Spain
| | - Gilberto Reynoso-Meza
- Industrial and Systems Engineering Graduate Program (PPGEPS), Pontificial Catholic University of Parana (PUCPR), Curitiba, Brazil
| | - Jesús Picó
- Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Valencia, Spain
| | - Alejandro Vignoni
- Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Valencia, Spain. .,Present Address: Center for Systems Biology Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
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Harris AWK, Dolan JA, Kelly CL, Anderson J, Papachristodoulou A. Designing Genetic Feedback Controllers. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2015; 9:475-484. [PMID: 26390502 DOI: 10.1109/tbcas.2015.2458435] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
By incorporating feedback around systems we wish to manipulate, it is possible to improve their performance and robustness properties to meet pre-specified design objectives. For decades control engineers have been successfully implementing feedback controllers for complex mechanical and electrical systems such as aircraft and sports cars. Natural biological systems use feedback extensively for regulation and adaptation but apart from the most basic designs, there is no systematic framework for designing feedback controllers in Synthetic Biology. In this paper we describe how classical approaches from linear control theory can be used to close the loop. This includes the design of genetic circuits using feedback control and the presentation of a biological phase lag controller.
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