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Arboleda-García A, Alarcon-Ruiz I, Boada-Acosta L, Boada Y, Vignoni A, Jantus-Lewintre E. Advancements in synthetic biology-based bacterial cancer therapy: A modular design approach. Crit Rev Oncol Hematol 2023; 190:104088. [PMID: 37541537 DOI: 10.1016/j.critrevonc.2023.104088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/18/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
Synthetic biology aims to program living bacteria cells with artificial genetic circuits for user-defined functions, transforming them into powerful tools with numerous applications in various fields, including oncology. Cancer treatments have serious side effects on patients due to the systemic action of the drugs involved. To address this, new systems that provide localized antitumoral action while minimizing damage to healthy tissues are required. Bacteria, often considered pathogenic agents, have been used as cancer treatments since the early 20th century. Advances in genetic engineering, synthetic biology, microbiology, and oncology have improved bacterial therapies, making them safer and more effective. Here we propose six modules for a successful synthetic biology-based bacterial cancer therapy, the modules include Payload, Release, Tumor-targeting, Biocontainment, Memory, and Genetic Circuit Stability Module. These will ensure antitumor activity, safety for the environment and patient, prevent bacterial colonization, maintain cell stability, and prevent loss or defunctionalization of the genetic circuit.
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Affiliation(s)
- Andrés Arboleda-García
- Systems Biology and Biosystems Control Lab, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Spain
| | - Ivan Alarcon-Ruiz
- Gene Regulation in Cardiovascular Remodeling and Inflammation Group, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Departamento de Biología Molecular, Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain
| | - Lissette Boada-Acosta
- Centro de Investigación Biomédica en Red Cáncer, CIBERONC, Madrid, Spain; TRIAL Mixed Unit, Centro de Investigación Príncipe Felipe-Fundación Investigación del Hospital General Universitario de Valencia, Valencia, Spain; Molecular Oncology Laboratory, Fundación Investigación del Hospital General Universitario de Valencia, Valencia, Spain
| | - Yadira Boada
- Systems Biology and Biosystems Control Lab, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Spain
| | - Alejandro Vignoni
- Systems Biology and Biosystems Control Lab, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Spain.
| | - Eloisa Jantus-Lewintre
- Centro de Investigación Biomédica en Red Cáncer, CIBERONC, Madrid, Spain; TRIAL Mixed Unit, Centro de Investigación Príncipe Felipe-Fundación Investigación del Hospital General Universitario de Valencia, Valencia, Spain; Molecular Oncology Laboratory, Fundación Investigación del Hospital General Universitario de Valencia, Valencia, Spain; Department of Biotechnology, Universitat Politècnica de València, Valencia, Spain
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2
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Chakraborty D, Rengaswamy R, Raman K. Designing Biological Circuits: From Principles to Applications. ACS Synth Biol 2022; 11:1377-1388. [PMID: 35320676 DOI: 10.1021/acssynbio.1c00557] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.
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Affiliation(s)
- Debomita Chakraborty
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Raghunathan Rengaswamy
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
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3
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Boada Y, Santos-Navarro FN, Picó J, Vignoni A. Modeling and Optimization of a Molecular Biocontroller for the Regulation of Complex Metabolic Pathways. Front Mol Biosci 2022; 9:801032. [PMID: 35425808 PMCID: PMC9001882 DOI: 10.3389/fmolb.2022.801032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 02/22/2022] [Indexed: 11/30/2022] Open
Abstract
Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids among others. We present an approach to use a realistic model that accounts for in vivo implementation and then propose a methodology based on multiobjective optimization for the optimal tuning of the gene circuit parts composing the biomolecular controller and biosensor devices for a dynamic regulation strategy. We show how this approach can deal with the trade-offs between the performance of the regulated pathway, robustness to perturbations, and stability of the feedback loop. Using realistic models, our results suggest that the strategies for fine-tuning the trade-offs among performance, robustness, and stability in dynamic pathway regulation are complex. It is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology valuable and necessary.
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Boada Y, Picó J, Vignoni A. Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation. Methods Mol Biol 2022; 2385:65-89. [PMID: 34888716 DOI: 10.1007/978-1-0716-1767-0_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Semi-mechanistic kinetic (i.e., dynamic) models based on first principles are particularly relevant in biology, as they can explain and predict functional behavior that arises from varying concentrations of the cellular components over time. Here, we describe a computational tuning framework to facilitate both the selection of kinetic parameters for these models and its estimation from experimental data. On the one hand, the tuning framework uses multi-objective optimization to generate a model-based set of guidelines for the selection of the kinetic parameters. These parameter values are the required ones to provide a biological system with desired behavior, while fulfilling the design criteria encoded in the optimization problem itself. On the other hand, this framework can also be used to estimate the parameter values of biological systems from experimental data, once the optimization objectives had been defined appropriately. The methodology gives accurate identification results, as it provides clear orientation on the effect of the parameter values over the system's behavior even under different experimental scenarios. It is particularly useful for easily combining time-course-averaged data and steady-state distribution data. This protocol also addresses aspects related to the appropriate description of the kinetic models and the settings of the software tools. Therefore, it supplies for hands-on testing to evaluate the validity of the underlying technical assumptions of the biological kinetic models.
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Affiliation(s)
- Yadira Boada
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Valencia, Spain
- Centro Universitario EDEM, Escuela de Empresarios, La Marina de València, Valencia, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Valencia, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Valencia, Spain.
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5
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Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11135859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.
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Stochastic Differential Equations for Practical Simulation of Gene Circuits. Methods Mol Biol 2021. [PMID: 33405216 DOI: 10.1007/978-1-0716-1032-9_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
The Chemical Langevin Equation approach allows simple stochastic simulation of gene circuits under many practical situations where the number of molecules of the species involved is not extremely low. Here, we describe methods and a computational framework to simulate a population of cells containing gene circuits of interest. These methods account for both intrinsic and extrinsic noise sources, and allow us to have both individual cell-related species and population-related ones. The protocol covers aspects related to proper description of the system and setting the software tools. It also helps to deal with the optimization of data storage and the simulation precision versus computational time issue. Finally, it also gives practical tests to assess the validity of the underlying technical assumptions.
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Tonn MK, Thomas P, Barahona M, Oyarzún DA. Computation of Single-Cell Metabolite Distributions Using Mixture Models. Front Cell Dev Biol 2020; 8:614832. [PMID: 33415109 PMCID: PMC7783310 DOI: 10.3389/fcell.2020.614832] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 11/26/2020] [Indexed: 12/30/2022] Open
Abstract
Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.
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Affiliation(s)
- Mona K. Tonn
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Philipp Thomas
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Diego A. Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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Gonzales DT, Zechner C, Tang TYD. Building synthetic multicellular systems using bottom–up approaches. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.coisb.2020.10.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Boada Y, Vignoni A, Picó J, Carbonell P. Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories. iScience 2020; 23:101305. [PMID: 32629420 PMCID: PMC7334618 DOI: 10.1016/j.isci.2020.101305] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/05/2020] [Accepted: 06/18/2020] [Indexed: 12/17/2022] Open
Abstract
Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.
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Affiliation(s)
- Yadira Boada
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain; Centro Universitario EDEM, Escuela de Empresarios, Muelle de la Aduana s/n, La Marina de València, 46024 Valencia, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Pablo Carbonell
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.
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10
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Controlling cell-to-cell variability with synthetic gene circuits. Biochem Soc Trans 2019; 47:1795-1804. [DOI: 10.1042/bst20190295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 11/05/2019] [Accepted: 11/06/2019] [Indexed: 02/05/2023]
Abstract
Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.
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11
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Boada Y, Vignoni A, Alarcon-Ruiz I, Andreu-Vilarroig C, Monfort-Llorens R, Requena A, Picó J. Characterization of Gene Circuit Parts Based on Multiobjective Optimization by Using Standard Calibrated Measurements. Chembiochem 2019; 20:2653-2665. [PMID: 31269324 DOI: 10.1002/cbic.201900272] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 06/12/2019] [Indexed: 01/08/2023]
Abstract
Standardization and characterization of biological parts is necessary for the further development of bottom-up synthetic biology. Herein, an easy-to-use methodology that embodies both a calibration procedure and a multiobjective optimization approach is proposed to characterize biological parts. The calibration procedure generates values for specific fluorescence per cell expressed as standard units of molecules of equivalent fluorescein per particle. The use of absolute standard units enhances the characterization of model parameters for biological parts by bringing measurements and estimations results from different sources into a common domain, so they can be integrated and compared faithfully. The multiobjective optimization procedure exploits these concepts by estimating the values of the model parameters, which represent biological parts of interest, while considering a varied range of experimental and circuit contexts. Thus, multiobjective optimization provides a robust characterization of them. The proposed calibration and characterization methodology can be used as a guide for good practices in dry and wet laboratories; thus allowing not only portability between models, but is also useful for generating libraries of tested and well-characterized biological parts.
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Affiliation(s)
- Yadira Boada
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain.,Centro Universitario EDEM, Escuela de Empresarios, La Marina de València, Muelle de la Aduana S/N, 46024, Valencia, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain
| | - Iván Alarcon-Ruiz
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain.,Escuela Tècnica Superior de Ingeniería Agronómica y del Medio Natural, Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain
| | - Carlos Andreu-Vilarroig
- Escuela Técnica Superior de Ingeniería Industrial, Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain
| | - Roger Monfort-Llorens
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain.,Escuela Técnica Superior de Ingeniería Industrial, Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain
| | - Adrián Requena
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain.,Escuela Tècnica Superior de Ingeniería Agronómica y del Medio Natural, Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022, Valencia, Spain
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Charlton SGV, White MA, Jana S, Eland LE, Jayathilake PG, Burgess JG, Chen J, Wipat A, Curtis TP. Regulating, Measuring, and Modeling the Viscoelasticity of Bacterial Biofilms. J Bacteriol 2019; 201:e00101-19. [PMID: 31182499 PMCID: PMC6707926 DOI: 10.1128/jb.00101-19] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Biofilms occur in a broad range of environments under heterogeneous physicochemical conditions, such as in bioremediation plants, on surfaces of biomedical implants, and in the lungs of cystic fibrosis patients. In these scenarios, biofilms are subjected to shear forces, but the mechanical integrity of these aggregates often prevents their disruption or dispersal. Biofilms' physical robustness is the result of the multiple biopolymers secreted by constituent microbial cells which are also responsible for numerous biological functions. A better understanding of the role of these biopolymers and their response to dynamic forces is therefore crucial for understanding the interplay between biofilm structure and function. In this paper, we review experimental techniques in rheology, which help quantify the viscoelasticity of biofilms, and modeling approaches from soft matter physics that can assist our understanding of the rheological properties. We describe how these methods could be combined with synthetic biology approaches to control and investigate the effects of secreted polymers on the physical properties of biofilms. We argue that without an integrated approach of the three disciplines, the links between genetics, composition, and interaction of matrix biopolymers and the viscoelastic properties of biofilms will be much harder to uncover.
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Affiliation(s)
- Samuel G V Charlton
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Michael A White
- Interdisciplinary Computing & Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Saikat Jana
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Lucy E Eland
- Interdisciplinary Computing & Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - J Grant Burgess
- School of Natural & Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jinju Chen
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- Interdisciplinary Computing & Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Thomas P Curtis
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
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Steel H, Papachristodoulou A. Probing Intercell Variability Using Bulk Measurements. ACS Synth Biol 2018; 7:1528-1537. [PMID: 29799736 DOI: 10.1021/acssynbio.8b00014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The measurement of noise is critical when assessing the design and function of synthetic biological systems. Cell-to-cell variability can be quantified experimentally using single-cell measurement techniques such as flow cytometry and fluorescent microscopy. However, these approaches are costly and impractical for high-throughput parallelized experiments, which are frequently conducted using plate-reader devices. In this paper we describe reporter systems that allow estimation of the cell-to-cell variability in a biological system's output using only measurements of a cell culture's bulk properties. We analyze one potential implementation of such a system that is based upon a fluorescent protein FRET reporter pair, finding that with typical parameters from the literature it is able to reliably estimate variability. We also briefly describe an alternate implementation based upon an activating sRNA circuit. The feasible region of parameter values for which the reporter system can function is assessed, and the dependence of its performance on both extrinsic and intrinsic noise is investigated. Experimental realization of these constructs can yield novel reporter systems that allow measurement of a synthetic gene circuit's output, as well as the intrapopulation variability of this output, at little added cost.
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Affiliation(s)
- Harrison Steel
- Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, U.K
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