1
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Chen YC, Destouches L, Cook A, Fedorec AJH. Synthetic microbial ecology: engineering habitats for modular consortia. J Appl Microbiol 2024; 135:lxae158. [PMID: 38936824 DOI: 10.1093/jambio/lxae158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/13/2024] [Accepted: 06/26/2024] [Indexed: 06/29/2024]
Abstract
Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.
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Affiliation(s)
- Yue Casey Chen
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Louie Destouches
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Alice Cook
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Alex J H Fedorec
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
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2
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Blee JA, Gorochowski TE, Hauert S. Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach. J R Soc Interface 2024; 21:20240078. [PMID: 38593842 PMCID: PMC11003776 DOI: 10.1098/rsif.2024.0078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Biofilms are responsible for most chronic infections and are highly resistant to antibiotic treatments. Previous studies have demonstrated that periodic dosing of antibiotics can help sensitize persistent subpopulations and reduce the overall dosage required for treatment. Because the dynamics and mechanisms of biofilm growth and the formation of persister cells are diverse and are affected by environmental conditions, it remains a challenge to design optimal periodic dosing regimens. Here, we develop a computational agent-based model to streamline this process and determine key parameters for effective treatment. We used our model to test a broad range of persistence switching dynamics and found that if periodic antibiotic dosing was tuned to biofilm dynamics, the dose required for effective treatment could be reduced by nearly 77%. The biofilm architecture and its response to antibiotics were found to depend on the dynamics of persister cells. Despite some differences in the response of biofilm governed by different persister switching rates, we found that a general optimized periodic treatment was still effective in significantly reducing the required antibiotic dose. As persistence becomes better quantified and understood, our model has the potential to act as a foundation for more effective strategies to target bacterial infections.
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Affiliation(s)
- Johanna A. Blee
- School of Engineering Mathematics and Technology, University of Bristol, Ada Lovelace Building, Tankard's Close, Bristol BS8 1TW, UK
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
| | - Sabine Hauert
- School of Engineering Mathematics and Technology, University of Bristol, Ada Lovelace Building, Tankard's Close, Bristol BS8 1TW, UK
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
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3
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Cockx BJR, Foster T, Clegg RJ, Alden K, Arya S, Stekel DJ, Smets BF, Kreft JU. Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0. PLoS Comput Biol 2024; 20:e1011303. [PMID: 38422165 PMCID: PMC10947719 DOI: 10.1371/journal.pcbi.1011303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 03/18/2024] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.
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Affiliation(s)
- Bastiaan J. R. Cockx
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Tim Foster
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Robert J. Clegg
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Kieran Alden
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Sankalp Arya
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Dov J. Stekel
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, United Kingdom
| | - Barth F. Smets
- Department of Environmental and Resource Engineering, Technical University of Demark, DTU Lyngby campus, Kgs. Lyngby, Denmark
| | - Jan-Ulrich Kreft
- Centre for Computational Biology & Institute of Microbiology and Infection & School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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4
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Cavero Rozas GM, Mandujano JMC, Chombo YAF, Rencoret DVM, Ortiz Mora YM, Pescarmona MEG, Torres AJD. pyBrick-DNA: A Python-Based Environment for Automated Genetic Component Assembly. J Comput Biol 2023; 30:1315-1321. [PMID: 38010519 DOI: 10.1089/cmb.2023.0008] [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: 11/29/2023] Open
Abstract
Genetic component assembly is key in the simulation and implementation of genetic circuits. Automating this process, thus accelerating prototyping, is a necessity. We present pyBrick-DNA, a software written in Python, that assembles components for the construction of genetic circuits. pyBrick-DNA (colab.pyBrick.com) is a user-friendly environment where scientists can select genetic sequences or input custom sequences to build genetic assemblies. All components are modularly fused to generate a ready-to-go single DNA fragment. It includes Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and plant gene-editing components. Hence, pyBrick-DNA can generate a functional CRISPR construct composed of a single-guided RNA integrated with Cas9, promoters, and terminator elements. The outcome is a DNA sequence, along with a graphical representation, composed of user-selected genetic parts, ready to be synthesized and cloned in vivo. Moreover, the sequence can be exported as a GenBank file allowing its use with other synthetic biology tools.
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Affiliation(s)
- Gladys M Cavero Rozas
- Department of Bioengineering and Chemical Engineering, University of Engineering and Technology (UTEC), Barranco, Lima, Peru
| | - Jose M Cisneros Mandujano
- Department of Bioengineering and Chemical Engineering, University of Engineering and Technology (UTEC), Barranco, Lima, Peru
| | - Yomali A Ferreyra Chombo
- Department of Bioengineering and Chemical Engineering, University of Engineering and Technology (UTEC), Barranco, Lima, Peru
| | - Daniela V Moreno Rencoret
- School of Informatics and Telecommunications, Faculty of Engineering and Sciences, Universidad Diego Portales, Santiago, Chile
| | - Yerko M Ortiz Mora
- School of Informatics and Telecommunications, Faculty of Engineering and Sciences, Universidad Diego Portales, Santiago, Chile
| | - Martín E Gutiérrez Pescarmona
- School of Informatics and Telecommunications, Faculty of Engineering and Sciences, Universidad Diego Portales, Santiago, Chile
| | - Alberto J Donayre Torres
- Department of Bioengineering and Chemical Engineering, University of Engineering and Technology (UTEC), Barranco, Lima, Peru
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5
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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6
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Caringella G, Bandiera L, Menolascina F. Recent advances, opportunities and challenges in cybergenetic identification and control of biomolecular networks. Curr Opin Biotechnol 2023; 80:102893. [PMID: 36706519 DOI: 10.1016/j.copbio.2023.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 01/26/2023]
Abstract
Cybergenetics is a new area of research aimed at developing digital and biological controllers for living systems. Synthetic biologists have begun exploiting cybergenetic tools and platforms to both accelerate the development of mathematical models and develop control strategies for complex biological phenomena. Here, we review the state of the art in cybergenetic identification and control. Our aim is to lower the entry barrier to this field and foster the adoption of methods and technologies that will accelerate the pace at which Synthetic Biology progresses toward applications.
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Affiliation(s)
- Gianpio Caringella
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK
| | - Lucia Bandiera
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK; Centre for Engineering Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Filippo Menolascina
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK; Centre for Engineering Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK.
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7
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Duan Y, Tan Y, Wei X, Pei X, Li M. Versatile Strategy for the Construction of a Transcription Factor-Based Orthogonal Gene Expression Toolbox in Monascus spp. ACS Synth Biol 2023; 12:213-223. [PMID: 36625512 DOI: 10.1021/acssynbio.2c00500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Gene expression is needed to be conducted in an orthogonal manner and controllable independently from the host's native regulatory system. However, there is a shortage of gene expression regulatory toolboxes that function orthogonally from each other and toward the host. Herein, we developed a strategy based on the mutant library to generate orthogonal gene expression toolboxes. A transcription factor, MaR, located in the Monascus azaphilone biosynthetic gene cluster, was taken as a typical example. Nine DNA-binding residues of MaR were identified by molecular simulation and site-directed mutagenesis. We created five MaR multi-site saturation mutagenesis libraries consisting of 10743 MaR variants on the basis of five cognate promoters. A functional analysis revealed that all five tested promoters were orthogonally regulated by five different MaR variants, respectively. Furthermore, fine gene expression tunability and high signal sensitivity of this toolbox are demonstrated by introducing chemically inducible expression modules, designing synthetic promoter elements, and creating protein-protein interaction between MaRs. This study paves the way for a bottom-up approach to build orthogonal gene expression toolboxes.
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Affiliation(s)
- Yali Duan
- Hubei International Scientific and Technological Cooperation Base of Traditional Fermented Foods, Huazhong Agricultural University, Wuhan, Hubei Province430070, China.,College of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province430070, China
| | - Yingao Tan
- Hubei International Scientific and Technological Cooperation Base of Traditional Fermented Foods, Huazhong Agricultural University, Wuhan, Hubei Province430070, China.,College of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province430070, China
| | - Xuetuan Wei
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province430070, China
| | - Xiaolin Pei
- College of Material, Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou310012, China
| | - Mu Li
- Hubei International Scientific and Technological Cooperation Base of Traditional Fermented Foods, Huazhong Agricultural University, Wuhan, Hubei Province430070, China.,College of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province430070, China
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8
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Zills G, Datta T, Malmi-Kakkada AN. Enhanced mechanical heterogeneity of cell collectives due to temporal fluctuations in cell elasticity. Phys Rev E 2023; 107:014401. [PMID: 36797877 DOI: 10.1103/physreve.107.014401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 12/08/2022] [Indexed: 06/18/2023]
Abstract
Cells are dynamic systems characterized by temporal variations in biophysical properties such as stiffness and contractility. Recent studies show that the recruitment and release of actin filaments into and out of the cell cortex-a network of proteins underneath the cell membrane-leads to cell stiffening prior to division and softening immediately afterward. In three-dimensional (3D) cell collectives, it is unclear whether the stiffness change during division at the single-cell scale controls the spatial structure and dynamics at the multicellular scale. This is an important question to understand because cell stiffness variations impact cell spatial organization and cancer progression. Using a minimal 3D model incorporating cell birth, death, and cell-to-cell elastic and adhesive interactions, we investigate the effect of mechanical heterogeneity-variations in individual cell stiffnesses that make up the cell collective-on tumor spatial organization and cell dynamics. We discover that spatial mechanical heterogeneity characterized by a spheroid core composed of stiffer cells and softer cells in the periphery emerges within dense 3D cell collectives, which may be a general feature of multicellular tumor growth. We show that heightened spatial mechanical heterogeneity enhances single-cell dynamics and volumetric tumor growth driven by fluctuations in cell elasticity. Our results could have important implications in understanding how spatiotemporal variations in single-cell stiffness determine tumor growth and spread.
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Affiliation(s)
- Garrett Zills
- Department of Chemistry and Physics, Augusta University, 1120 15th Street, Augusta, Georgia 30912, USA
| | - Trinanjan Datta
- Department of Chemistry and Physics, Augusta University, 1120 15th Street, Augusta, Georgia 30912, USA
- Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California 93106, USA
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9
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Abstract
Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.
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Affiliation(s)
- Karthik Nagarajan
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Congjian Ni
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Ting Lu
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,National Center for Supercomputing Applications, Urbana, Illinois 61801, United States
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10
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Ni C, Lu T. Individual-Based Modeling of Spatial Dynamics of Chemotactic Microbial Populations. ACS Synth Biol 2022; 11:3714-3723. [PMID: 36336839 PMCID: PMC10129442 DOI: 10.1021/acssynbio.2c00322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
One important direction of synthetic biology is to establish desired spatial structures from microbial populations. Underlying this structural development process are different driving factors, among which bacterial motility and chemotaxis serve as a major force. Here, we present an individual-based, biophysical computational framework for mechanistic and multiscale simulation of the spatiotemporal dynamics of motile and chemotactic microbial populations. The framework integrates cellular movement with spatial population growth, mechanical and chemical cellular interactions, and intracellular molecular kinetics. It is validated by a statistical comparison of single-cell chemotaxis simulations with reported experiments. The framework successfully captures colony range expansion of growing isogenic populations and also reveals chemotaxis-modulated, spatial patterns of a two-species amensal community. Partial differential equation-based models subsequently validate these simulation findings. This study provides a versatile computational tool to uncover the fundamentals of microbial spatial ecology as well as to facilitate the design of synthetic consortia for desired spatial patterns.
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Affiliation(s)
- Congjian Ni
- Center of Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Ting Lu
- Center of Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Physics, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.,National Center for Supercomputing Applications, Urbana, Illinois 61801, United States
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11
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Senthilkumar I, Howley E, McEvoy E. Thermodynamically-motivated chemo-mechanical models and multicellular simulation to provide new insight into active cell and tumour remodelling. Exp Cell Res 2022; 419:113317. [PMID: 36028058 DOI: 10.1016/j.yexcr.2022.113317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/19/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022]
Abstract
Computational models can shape our understanding of cell and tissue remodelling, from cell spreading, to active force generation, adhesion, and growth. In this mini-review, we discuss recent progress in modelling of chemo-mechanical cell behaviour and the evolution of multicellular systems. In particular, we highlight recent advances in (i) free-energy based single cell models that can provide new fundamental insight into cell spreading, cancer cell invasion, stem cell differentiation, and remodelling in disease, and (ii) mechanical agent-based models to simulate large numbers of discrete interacting cells in proliferative tumours. We describe how new biological understanding has emerged from such theoretical models, and the trade-offs and constraints associated with current approaches. Ultimately, we aim to make a case for why theory should be integrated with an experimental workflow to optimise new in-vitro studies, to predict feedback between cells and their microenvironment, and to deepen understanding of active cell behaviour.
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Affiliation(s)
- Irish Senthilkumar
- School of Computer Science, College of Science and Engineering, National University of Ireland Galway, Ireland; Biomedical Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland
| | - Enda Howley
- School of Computer Science, College of Science and Engineering, National University of Ireland Galway, Ireland
| | - Eoin McEvoy
- Biomedical Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland.
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12
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Kuznetsov IA, Berlew EE, Glantz ST, Hannanta-Anan P, Chow BY. Computational framework for single-cell spatiotemporal dynamics of optogenetic membrane recruitment. CELL REPORTS METHODS 2022; 2:100245. [PMID: 35880018 PMCID: PMC9308134 DOI: 10.1016/j.crmeth.2022.100245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 05/18/2022] [Accepted: 06/09/2022] [Indexed: 10/27/2022]
Abstract
We describe a modular computational framework for analyzing cell-wide spatiotemporal signaling dynamics in single-cell microscopy experiments that accounts for the experiment-specific geometric and diffractive complexities that arise from heterogeneous cell morphologies and optical instrumentation. Inputs are unique cell geometries and protein concentrations derived from confocal stacks and spatiotemporally varying environmental stimuli. After simulating the system with a model of choice, the output is convolved with the microscope point-spread function for direct comparison with the observable image. We experimentally validate this approach in single cells with BcLOV4, an optogenetic membrane recruitment system for versatile control over cell signaling, using a three-dimensional non-linear finite element model with all parameters experimentally derived. The simulations recapitulate observed subcellular and cell-to-cell variability in BcLOV4 signaling, allowing for inter-experimental differences of cellular and instrumentation origins to be elucidated and resolved for improved interpretive robustness. This single-cell approach will enhance optogenetics and spatiotemporally resolved signaling studies.
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Affiliation(s)
- Ivan A. Kuznetsov
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erin E. Berlew
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Spencer T. Glantz
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pimkhuan Hannanta-Anan
- Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Brian Y. Chow
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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13
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Amos M, Webster J. Crowd-Sourced Identification of Characteristics of Collective Human Motion. ARTIFICIAL LIFE 2022; 28:401-422. [PMID: 35984431 DOI: 10.1162/artl_a_00381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Crowd simulations are used extensively to study the dynamics of human collectives. Such studies are underpinned by specific movement models, which encode rules and assumptions about how people navigate a space and handle interactions with others. These models often give rise to macroscopic simulated crowd behaviours that are statistically valid, but which lack the noisy microscopic behaviours that are the signature of believable real crowds. In this article, we use an existing Turing test for crowds to identify realistic features of real crowds that are generally omitted from simulation models. Our previous study using this test established that untrained individuals have difficulty in classifying movies of crowds as real or simulated, and that such people often have an idealised view of how crowds move. In this follow-up study (with new participants) we perform a second trial, which now includes a training phase (showing participants movies of real crowds). We find that classification performance significantly improves after training, confirming the existence of features that allow participants to identify real crowds. High-performing individuals are able to identify the features of real crowds that should be incorporated into future simulations if they are to be considered realistic.
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Affiliation(s)
- Martyn Amos
- Northumbria University, Department of Computer and Information Sciences.
| | - Jamie Webster
- Northumbria University, Department of Computer and Information Sciences
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14
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Shallom D, Naiger D, Weiss S, Tuller T. Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware. ACS Synth Biol 2021; 10:3489-3506. [PMID: 34813269 PMCID: PMC8689694 DOI: 10.1021/acssynbio.1c00415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In recent years, intracellular biophysical simulations have been used with increasing frequency not only for answering basic scientific questions but also in the field of synthetic biology. However, since these models include networks of interaction between millions of components, they are extremely time-consuming and cannot run easily on parallel computers. In this study, we demonstrate for the first time a novel approach addressing this challenge by using a dedicated hardware designed specifically to simulate such processes. As a proof of concept, we specifically focus on mRNA translation, which is the process consuming most of the energy in the cell. We design a hardware that simulates translation in Escherichia coli and Saccharomyces cerevisiae for thousands of mRNAs and ribosomes, which is in orders of magnitude faster than a similar software solution. With the sharp increase in the amount of genomic data available today and the complexity of the corresponding models inferred from them, we believe that the strategy suggested here will become common and can be used among others for simulating entire cells with all gene expression steps.
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Affiliation(s)
- David Shallom
- School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Danny Naiger
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Shlomo Weiss
- School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
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15
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Clark CJ, Scott-Brown J, Gorochowski TE. paraSBOLv: a foundation for standard-compliant genetic design visualization tools. Synth Biol (Oxf) 2021; 6:ysab022. [PMID: 34712845 PMCID: PMC8546602 DOI: 10.1093/synbio/ysab022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/30/2021] [Accepted: 08/09/2021] [Indexed: 11/13/2022] Open
Abstract
Diagrams constructed from standardized glyphs are central to communicating complex design information in many engineering fields. For example, circuit diagrams are commonplace in electronics and allow for a suitable abstraction of the physical system that helps support the design process. With the development of the Synthetic Biology Open Language Visual (SBOLv), bioengineers are now positioned to better describe and share their biological designs visually. However, the development of computational tools to support the creation of these diagrams is currently hampered by an excessive burden in maintenance due to the large and expanding number of glyphs present in the standard. Here, we present a Python package called paraSBOLv that enables access to the full suite of SBOLv glyphs through the use of machine-readable parametric glyph definitions. These greatly simplify the rendering process while allowing extensive customization of the resulting diagrams. We demonstrate how the adoption of paraSBOLv can accelerate the development of highly specialized biodesign visualization tools or even form the basis for more complex software by removing the burden of maintaining glyph-specific rendering code. Looking forward, we suggest that incorporation of machine-readable parametric glyph definitions into the SBOLv standard could further simplify the development of tools to produce standard-compliant diagrams and the integration of visual standards across fields.
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Affiliation(s)
- Charlie J Clark
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - James Scott-Brown
- Nuffield Department of Population Health, University of Oxford, Oxford, Oxfordshire, UK
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Bristol, UK
- BrisSynBio, University of Bristol, Bristol, UK
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16
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Khuu S, Fernandez JW, Handsfield GG. A Coupled Mechanobiological Model of Muscle Regeneration In Cerebral Palsy. Front Bioeng Biotechnol 2021; 9:689714. [PMID: 34513808 PMCID: PMC8429491 DOI: 10.3389/fbioe.2021.689714] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/06/2021] [Indexed: 01/05/2023] Open
Abstract
Cerebral palsy is a neuromusculoskeletal disorder associated with muscle weakness, altered muscle architecture, and progressive musculoskeletal symptoms that worsen with age. Pathological changes at the level of the whole muscle have been shown; however, it is unclear why this progression of muscle impairment occurs at the cellular level. The process of muscle regeneration is complex, and the interactions between cells in the muscle milieu should be considered in the context of cerebral palsy. In this work, we built a coupled mechanobiological model of muscle damage and regeneration to explore the process of muscle regeneration in typical and cerebral palsy conditions, and whether a reduced number of satellite cells in the cerebral palsy muscle environment could cause the muscle regeneration cycle to lead to progressive degeneration of muscle. The coupled model consisted of a finite element model of a muscle fiber bundle undergoing eccentric contraction, and an agent-based model of muscle regeneration incorporating satellite cells, inflammatory cells, muscle fibers, extracellular matrix, fibroblasts, and secreted cytokines. Our coupled model simulated damage from eccentric contraction followed by 28 days of regeneration within the muscle. We simulated cyclic damage and regeneration for both cerebral palsy and typically developing muscle milieus. Here we show the nonlinear effects of altered satellite cell numbers on muscle regeneration, where muscle repair is relatively insensitive to satellite cell concentration above a threshold, but relatively sensitive below that threshold. With the coupled model, we show that the fiber bundle geometry undergoes atrophy and fibrosis with too few satellite cells and excess extracellular matrix, representative of the progression of cerebral palsy in muscle. This work uses in silico modeling to demonstrate how muscle degeneration in cerebral palsy may arise from the process of cellular regeneration and a reduced number of satellite cells.
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Affiliation(s)
- Stephanie Khuu
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Justin W. Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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17
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Abstract
This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.
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18
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Moškon M, Komac R, Zimic N, Mraz M. Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05711-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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19
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Perrino G, Hadjimitsis A, Ledesma-Amaro R, Stan GB. Control engineering and synthetic biology: working in synergy for the analysis and control of microbial systems. Curr Opin Microbiol 2021; 62:68-75. [PMID: 34062481 DOI: 10.1016/j.mib.2021.05.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/30/2021] [Accepted: 05/06/2021] [Indexed: 01/12/2023]
Abstract
The implementation of novel functionalities in living cells is a key aspect of synthetic biology. In the last decade, the field of synthetic biology has made progress working in synergy with control engineering, whose solid framework has provided concepts and tools to analyse biological systems and guide their design. In this review, we briefly highlight recent work focused on the application of control theoretical concepts and tools for the analysis and design of synthetic biology systems in microbial cells.
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Affiliation(s)
- Giansimone Perrino
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK
| | - Andreas Hadjimitsis
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK
| | - Guy-Bart Stan
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK.
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20
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Cao Y, Neu J, Blanchard AE, Lu T, You L. Repulsive expansion dynamics in colony growth and gene expression. PLoS Comput Biol 2021; 17:e1008168. [PMID: 33735192 PMCID: PMC8009408 DOI: 10.1371/journal.pcbi.1008168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 03/30/2021] [Accepted: 02/15/2021] [Indexed: 01/05/2023] Open
Abstract
Spatial expansion of a population of cells can arise from growth of microorganisms, plant cells, and mammalian cells. It underlies normal or dysfunctional tissue development, and it can be exploited as the foundation for programming spatial patterns. This expansion is often driven by continuous growth and division of cells within a colony, which in turn pushes the peripheral cells outward. This process generates a repulsion velocity field at each location within the colony. Here we show that this process can be approximated as coarse-grained repulsive-expansion kinetics. This framework enables accurate and efficient simulation of growth and gene expression dynamics in radially symmetric colonies with homogenous z-directional distribution. It is robust even if cells are not spherical and vary in size. The simplicity of the resulting mathematical framework also greatly facilitates generation of mechanistic insights. Spatiotemporal dynamics are ubiquitous in biology. To understand these phenomena in nature or to program them using synthetic gene circuits, it is critical to resort to mathematical modeling to deduce mechanistic insights or to explore plausible outcomes. Historically, modeling of spatiotemporal dynamics depends on the use of agent-based models or their continuum counterparts consisting of partial differential equations. Here, we show that a class of colony expansion can be treated as being driven by the steric force generated by growing and diving cells. This approximation leads to a drastically simplified framework consisting of only ordinary differential equations. This framework greatly improves the computational efficiency and facilitates development of mechanistic insights into the dynamics of colony growth and pattern formation.
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Affiliation(s)
- Yangxiaolu Cao
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - John Neu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Andrew E. Blanchard
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Ting Lu
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina
- * E-mail:
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21
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Grobas I, Bazzoli DG, Asally M. Biofilm and swarming emergent behaviours controlled through the aid of biophysical understanding and tools. Biochem Soc Trans 2020; 48:2903-2913. [PMID: 33300966 PMCID: PMC7752047 DOI: 10.1042/bst20200972] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023]
Abstract
Bacteria can organise themselves into communities in the forms of biofilms and swarms. Through chemical and physical interactions between cells, these communities exhibit emergent properties that individual cells alone do not have. While bacterial communities have been mainly studied in the context of biochemistry and molecular biology, recent years have seen rapid advancements in the biophysical understanding of emergent phenomena through physical interactions in biofilms and swarms. Moreover, new technologies to control bacterial emergent behaviours by physical means are emerging in synthetic biology. Such technologies are particularly promising for developing engineered living materials (ELM) and devices and controlling contamination and biofouling. In this minireview, we overview recent studies unveiling physical and mechanical cues that trigger and affect swarming and biofilm development. In particular, we focus on cell shape, motion and density as the key parameters for mechanical cell-cell interactions within a community. We then showcase recent studies that use physical stimuli for patterning bacterial communities, altering collective behaviours and preventing biofilm formation. Finally, we discuss the future potential extension of biophysical and bioengineering research on microbial communities through computational modelling and deeper investigation of mechano-electrophysiological coupling.
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Affiliation(s)
- Iago Grobas
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, U.K
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, U.K
| | - Dario G. Bazzoli
- School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, U.K
| | - Munehiro Asally
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, U.K
- Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry CV4 7AL, U.K
- Bio-Electrical Engineering Innovation Hub, University of Warwick, Coventry CV4 7AL, U.K
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22
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Groaz A, Moghimianavval H, Tavella F, Giessen TW, Vecchiarelli AG, Yang Q, Liu AP. Engineering spatiotemporal organization and dynamics in synthetic cells. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2020; 13:e1685. [PMID: 33219745 DOI: 10.1002/wnan.1685] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 10/13/2020] [Accepted: 10/30/2020] [Indexed: 12/28/2022]
Abstract
Constructing synthetic cells has recently become an appealing area of research. Decades of research in biochemistry and cell biology have amassed detailed part lists of components involved in various cellular processes. Nevertheless, recreating any cellular process in vitro in cell-sized compartments remains ambitious and challenging. Two broad features or principles are key to the development of synthetic cells-compartmentalization and self-organization/spatiotemporal dynamics. In this review article, we discuss the current state of the art and research trends in the engineering of synthetic cell membranes, development of internal compartmentalization, reconstitution of self-organizing dynamics, and integration of activities across scales of space and time. We also identify some research areas that could play a major role in advancing the impact and utility of engineered synthetic cells. This article is categorized under: Biology-Inspired Nanomaterials > Lipid-Based Structures Biology-Inspired Nanomaterials > Protein and Virus-Based Structures.
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Affiliation(s)
| | | | | | | | | | - Qiong Yang
- University of Michigan, Ann Arbor, Michigan, USA
| | - Allen P Liu
- University of Michigan, Ann Arbor, Michigan, USA
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23
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Yáñez Feliú G, Vidal G, Muñoz Silva M, Rudge TJ. Novel Tunable Spatio-Temporal Patterns From a Simple Genetic Oscillator Circuit. Front Bioeng Biotechnol 2020; 8:893. [PMID: 33014996 PMCID: PMC7509427 DOI: 10.3389/fbioe.2020.00893] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/13/2020] [Indexed: 11/13/2022] Open
Abstract
Multicellularity, the coordinated collective behavior of cell populations, gives rise to the emergence of self-organized phenomena at many different spatio-temporal scales. At the genetic scale, oscillators are ubiquitous in regulation of multicellular systems, including during their development and regeneration. Synthetic biologists have successfully created simple synthetic genetic circuits that produce oscillations in single cells. Studying and engineering synthetic oscillators in a multicellular chassis can therefore give us valuable insights into how simple genetic circuits can encode complex multicellular behaviors at different scales. Here we develop a study of the coupling between the repressilator synthetic genetic ring oscillator and constraints on cell growth in colonies. We show in silico how mechanical constraints generate characteristic patterns of growth rate inhomogeneity in growing cell colonies. Next, we develop a simple one-dimensional model which predicts that coupling the repressilator to this pattern of growth rate via protein dilution generates traveling waves of gene expression. We show that the dynamics of these spatio-temporal patterns are determined by two parameters; the protein degradation and maximum expression rates of the repressors. We derive simple relations between these parameters and the key characteristics of the traveling wave patterns: firstly, wave speed is determined by protein degradation and secondly, wavelength is determined by maximum gene expression rate. Our analytical predictions and numerical results were in close quantitative agreement with detailed individual based simulations of growing cell colonies. Confirming published experimental results we also found that static ring patterns occur when protein stability is high. Our results show that this pattern can be induced simply by growth rate dilution and does not require transition to stationary phase as previously suggested. Our method generalizes easily to other genetic circuit architectures thus providing a framework for multi-scale rational design of spatio-temporal patterns from genetic circuits. We use this method to generate testable predictions for the synthetic biology design-build-test-learn cycle.
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Affiliation(s)
- Guillermo Yáñez Feliú
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Timothy J. Rudge
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
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24
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Quantifying the Biophysical Impact of Budding Cell Division on the Spatial Organization of Growing Yeast Colonies. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175780] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Spatial patterns in microbial colonies are the consequence of cell-division dynamics coupled with cell-cell interactions on a physical media. Agent-based models (ABMs) are a powerful tool for understanding the emergence of large scale structure from these individual cell processes. However, most ABMs have focused on fission, a process by which cells split symmetrically into two daughters. The yeast, Saccharomyces cerevisiae, is a model eukaryote which commonly undergoes an asymmetric division process called budding. The resulting mother and daughter cells have unequal sizes and the daughter cell does not inherit the replicative age of the mother. In this work, we develop and analyze an ABM to study the impact of budding cell division and nutrient limitation on yeast colony structure. We find that while budding division does not impact large-scale properties of the colony (such as shape and size), local spatial organization of cells with respect to spatial layout of mother-daughter cell pairs and connectivity of subcolonies is greatly impacted. In addition, we find that nutrient limitation further promotes local spatial organization of cells and changes global colony organization by driving variation in subcolony sizes. Moreover, resulting differences in spatial organization, coupled with differential growth rates from nutrient limitation, create distinct sectoring patterns within growing yeast colonies. Our findings offer novel insights into mechanisms driving experimentally observed sectored yeast colony phenotypes. Furthermore, our work illustrates the need to include relevant biophysical mechanisms when using ABMs to compare to experimental studies.
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25
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Karkaria BD, Treloar NJ, Barnes CP, Fedorec AJH. From Microbial Communities to Distributed Computing Systems. Front Bioeng Biotechnol 2020; 8:834. [PMID: 32793576 PMCID: PMC7387671 DOI: 10.3389/fbioe.2020.00834] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/29/2020] [Indexed: 12/15/2022] Open
Abstract
A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.
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Affiliation(s)
- Behzad D. Karkaria
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Neythen J. Treloar
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Chris P. Barnes
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
- UCL Genetics Institute, University College London, London, United Kingdom
| | - Alex J. H. Fedorec
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
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26
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Gorochowski TE, Hauert S, Kreft JU, Marucci L, Stillman NR, Tang TYD, Bandiera L, Bartoli V, Dixon DOR, Fedorec AJH, Fellermann H, Fletcher AG, Foster T, Giuggioli L, Matyjaszkiewicz A, McCormick S, Montes Olivas S, Naylor J, Rubio Denniss A, Ward D. Toward Engineering Biosystems With Emergent Collective Functions. Front Bioeng Biotechnol 2020; 8:705. [PMID: 32671054 PMCID: PMC7332988 DOI: 10.3389/fbioe.2020.00705] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 06/05/2020] [Indexed: 12/31/2022] Open
Abstract
Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.
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Affiliation(s)
| | - Sabine Hauert
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Jan-Ulrich Kreft
- School of Biosciences and Institute of Microbiology and Infection and Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Namid R. Stillman
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - T.-Y. Dora Tang
- Max Plank Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Physics of Life, Cluster of Excellence, Technische Universität Dresden, Dresden, Germany
| | - Lucia Bandiera
- School of Engineering, University of Edinburgh, Edinburgh, United Kingdom
| | - Vittorio Bartoli
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | | | - Alex J. H. Fedorec
- Division of Biosciences, University College London, London, United Kingdom
| | - Harold Fellermann
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alexander G. Fletcher
- Bateson Centre and School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Tim Foster
- School of Biosciences and Institute of Microbiology and Infection and Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom
| | - Luca Giuggioli
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | | | - Scott McCormick
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Sandra Montes Olivas
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Jonathan Naylor
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Ana Rubio Denniss
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Daniel Ward
- School of Biological Sciences, University of Bristol, Bristol, United Kingdom
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27
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Yu JS, Bagheri N. Agent-Based Models Predict Emergent Behavior of Heterogeneous Cell Populations in Dynamic Microenvironments. Front Bioeng Biotechnol 2020; 8:249. [PMID: 32596213 PMCID: PMC7301008 DOI: 10.3389/fbioe.2020.00249] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 03/10/2020] [Indexed: 01/18/2023] Open
Abstract
Computational models are most impactful when they explain and characterize biological phenomena that are non-intuitive, unexpected, or difficult to study experimentally. Countless equation-based models have been built for these purposes, but we have yet to realize the extent to which rules-based models offer an intuitive framework that encourages computational and experimental collaboration. We develop ARCADE, a multi-scale agent-based model to interrogate emergent behavior of heterogeneous cell agents within dynamic microenvironments and demonstrate how complexity of intracellular metabolism and signaling modules impacts emergent dynamics. We perform in silico case studies on context, competition, and heterogeneity to demonstrate the utility of our model for gaining computational and experimental insight. Notably, there exist (i) differences in emergent behavior between colony and tissue contexts, (ii) linear, non-linear, and multimodal consequences of parameter variation on competition in simulated co-cultures, and (iii) variable impact of cell and population heterogeneity on emergent outcomes. Our extensible framework is easily modified to explore numerous biological systems, from tumor microenvironments to microbiomes.
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Affiliation(s)
- Jessica S Yu
- Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
| | - Neda Bagheri
- Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States.,Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States.,Center for Synthetic Biology, Northwestern University, Evanston, IL, United States.,Biology, University of Washington, Seattle, WA, United States.,Chemical Engineering, University of Washington, Seattle, WA, United States
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28
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Leydon AR, Gala HP, Guiziou S, Nemhauser JL. Engineering Synthetic Signaling in Plants. ANNUAL REVIEW OF PLANT BIOLOGY 2020; 71:767-788. [PMID: 32092279 DOI: 10.1146/annurev-arplant-081519-035852] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Synthetic signaling is a branch of synthetic biology that aims to understand native genetic regulatory mechanisms and to use these insights to engineer interventions and devices that achieve specified design parameters. Applying synthetic signaling approaches to plants offers the promise of mitigating the worst effects of climate change and providing a means to engineer crops for entirely novel environments, such as those in space travel. The ability to engineer new traits using synthetic signaling methods will require standardized libraries of biological parts and methods to assemble them; the decoupling of complex processes into simpler subsystems; and mathematical models that can accelerate the design-build-test-learn cycle. The field of plant synthetic signaling is relatively new, but it is poised for rapid advancement. Translation from the laboratory to the field is likely to be slowed, however, by the lack of constructive dialogue between researchers and other stakeholders.
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Affiliation(s)
- Alexander R Leydon
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
| | - Hardik P Gala
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
| | - Sarah Guiziou
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
| | - Jennifer L Nemhauser
- Department of Biology, University of Washington, Seattle, Washington 98195, USA; , , ,
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29
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Abstract
Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found. Synthetic biology uses cells as its computing substrate, often based on the genetic circuit concept. In this Perspective, the authors argue that existing synthetic biology approaches based on classical models of computation limit the potential of biocomputing, and propose that living organisms have under-exploited capabilities.
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30
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Leaman EJ, Geuther BQ, Behkam B. Quantitative Investigation of the Role of Intra-/Intercellular Dynamics in Bacterial Quorum Sensing. ACS Synth Biol 2018; 7:1030-1042. [PMID: 29579377 DOI: 10.1021/acssynbio.7b00406] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Bacteria utilize diffusible signals to regulate population density-dependent coordinated gene expression in a process called quorum sensing (QS). While the intracellular regulatory mechanisms of QS are well-understood, the effect of spatiotemporal changes in the population configuration on the sensitivity and robustness of the QS response remains largely unexplored. Using a microfluidic device, we quantitatively characterized the emergent behavior of a population of swimming E. coli bacteria engineered with the lux QS system and a GFP reporter. We show that the QS activation time follows a power law with respect to bacterial population density, but this trend is disrupted significantly by microscale variations in population configuration and genetic circuit noise. We then developed a computational model that integrates population dynamics with genetic circuit dynamics to enable accurate (less than 7% error) quantitation of the bacterial QS activation time. Through modeling and experimental analyses, we show that changes in spatial configuration of swimming bacteria can drastically alter the QS activation time, by up to 22%. The integrative model developed herein also enables examination of the performance robustness of synthetic circuits with respect to growth rate, circuit sensitivity, and the population's initial size and spatial structure. Our framework facilitates quantitative tuning of microbial systems performance through rational engineering of synthetic ribosomal binding sites. We have demonstrated this through modulation of QS activation time over an order of magnitude. Altogether, we conclude that predictive engineering of QS-based bacterial systems requires not only the precise temporal modulation of gene expression (intracellular dynamics) but also accounting for the spatiotemporal changes in population configuration (intercellular dynamics).
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Affiliation(s)
- Eric J. Leaman
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Brian Q. Geuther
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Bahareh Behkam
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia 24061, United States
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31
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Seekhao N, Shung C, JaJa J, Mongeau L, Li-Jessen NYK. High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair. Front Physiol 2018; 9:304. [PMID: 29706894 PMCID: PMC5906585 DOI: 10.3389/fphys.2018.00304] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 03/13/2018] [Indexed: 01/13/2023] Open
Abstract
Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed.
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Affiliation(s)
- Nuttiiya Seekhao
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
| | - Caroline Shung
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Joseph JaJa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
| | - Luc Mongeau
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Nicole Y K Li-Jessen
- School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
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32
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Carvalho G, Balestrino D, Forestier C, Mathias JD. How do environment-dependent switching rates between susceptible and persister cells affect the dynamics of biofilms faced with antibiotics? NPJ Biofilms Microbiomes 2018; 4:6. [PMID: 29560270 PMCID: PMC5854711 DOI: 10.1038/s41522-018-0049-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 02/06/2018] [Accepted: 02/09/2018] [Indexed: 01/24/2023] Open
Abstract
Persisters form sub-populations of stress-tolerant cells that play a major role in the capacity of biofilms to survive and recover from disturbances such as antibiotic treatments. The mechanisms of persistence are diverse and influenced by environmental conditions, and persister populations are more heterogeneous than formerly suspected. We used computational modeling to assess the impact of three switching strategies between susceptible and persister cells on the capacity of bacterial biofilms to grow, survive and recover from antibiotic treatments. The strategies tested were: (1) constant switches, (2) substrate-dependent switches and (3) antibiotic-dependent switches. We implemented these strategies in an individual-based biofilm model and simulated antibiotic shocks on virtual biofilms. Because of limited available data on switching rates in the literature, nine parameter sets were assessed for each strategy. Substrate and antibiotic-dependent switches allowed high switching rates without affecting the growth of the biofilms. Compared to substrate-dependent switches, constant and antibiotic-dependent switches were associated with higher proportions of persisters in the top of the biofilms, close to the substrate source, which probably confers a competitive advantage within multi-species biofilms. The constant and substrate-dependent strategies need a compromise between limiting the wake-up and death of persisters during treatments and leaving the persister state fast enough to recover quickly after antibiotic-removal. Overall, the simulations gave new insights into the relationships between the dynamics of persister populations in biofilms and their dynamics of growth, survival and recovery when faced with disturbances.
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Affiliation(s)
- Gabriel Carvalho
- UR LISC Laboratoire d'Ingénierie pour les Systèmes Complexes, Irstea, Aubière, France
| | - Damien Balestrino
- 2LMGE, UMR6023 CNRS, Université Clermont Auvergne, Clermont-Ferrand, France
| | | | - Jean-Denis Mathias
- UR LISC Laboratoire d'Ingénierie pour les Systèmes Complexes, Irstea, Aubière, France
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33
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Ginovart M, Carbó R, Blanco M, Portell X. Digital Image Analysis of Yeast Single Cells Growing in Two Different Oxygen Concentrations to Analyze the Population Growth and to Assist Individual-Based Modeling. Front Microbiol 2018; 8:2628. [PMID: 29354112 PMCID: PMC5758558 DOI: 10.3389/fmicb.2017.02628] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 12/15/2017] [Indexed: 11/22/2022] Open
Abstract
Nowadays control of the growth of Saccharomyces to obtain biomass or cellular wall components is crucial for specific industrial applications. The general aim of this contribution is to deal with experimental data obtained from yeast cells and from yeast cultures to attempt the integration of the two levels of information, individual and population, to progress in the control of yeast biotechnological processes by means of the overall analysis of this set of experimental data, and to assist in the improvement of an individual-based model, namely, INDISIM-Saccha. Populations of S. cerevisiae growing in liquid batch culture, in aerobic and microaerophilic conditions, were studied. A set of digital images was taken during the population growth, and a protocol for the treatment and analyses of the images obtained was established. The piecewise linear model of Buchanan was adjusted to the temporal evolutions of the yeast populations to determine the kinetic parameters and changes of growth phases. In parallel, for all the yeast cells analyzed, values of direct morphological parameters, such as area, perimeter, major diameter, minor diameter, and derived ones, such as circularity and elongation, were obtained. Graphical and numerical methods from descriptive statistics were applied to these data to characterize the growth phases and the budding state of the yeast cells in both experimental conditions, and inferential statistical methods were used to compare the diverse groups of data achieved. Oxidative metabolism of yeast in a medium with oxygen available and low initial sugar concentration can be taken into account in order to obtain a greater number of cells or larger cells. Morphological parameters were analyzed statistically to identify which were the most useful for the discrimination of the different states, according to budding and/or growth phase, in aerobic and microaerophilic conditions. The use of the experimental data for subsequent modeling work was then discussed and compared to simulation results generated with INDISIM-Saccha, which allowed us to advance in the development of this yeast model, and illustrated the utility of data at different levels of observation and the needs and logic behind the development of a microbial individual-based model.
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Affiliation(s)
- Marta Ginovart
- Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Rosa Carbó
- Department of Agri-Food Engineering and Biotechnology, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Mónica Blanco
- Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Xavier Portell
- Cranfield Soil and Agrifood Institute, Cranfield University, Bedfordshire, United Kingdom
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Bardini R, Politano G, Benso A, Di Carlo S. Computational Tools for Applying Multi-level Models to Synthetic Biology. Synth Biol (Oxf) 2018. [DOI: 10.1007/978-981-10-8693-9_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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35
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Fogel JL, Lakeland DL, Mah IK, Mariani FV. A minimally sufficient model for rib proximal-distal patterning based on genetic analysis and agent-based simulations. eLife 2017; 6:e29144. [PMID: 29068314 PMCID: PMC5693115 DOI: 10.7554/elife.29144] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 10/24/2017] [Indexed: 12/19/2022] Open
Abstract
For decades, the mechanism of skeletal patterning along a proximal-distal axis has been an area of intense inquiry. Here, we examine the development of the ribs, simple structures that in most terrestrial vertebrates consist of two skeletal elements-a proximal bone and a distal cartilage portion. While the ribs have been shown to arise from the somites, little is known about how the two segments are specified. During our examination of genetically modified mice, we discovered a series of progressively worsening phenotypes that could not be easily explained. Here, we combine genetic analysis of rib development with agent-based simulations to conclude that proximal-distal patterning and outgrowth could occur based on simple rules. In our model, specification occurs during somite stages due to varying Hedgehog protein levels, while later expansion refines the pattern. This framework is broadly applicable for understanding the mechanisms of skeletal patterning along a proximal-distal axis.
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Affiliation(s)
- Jennifer L Fogel
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell ResearchUniversity of Southern CaliforniaLos AngelesUnited States
| | | | - In Kyoung Mah
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell ResearchUniversity of Southern CaliforniaLos AngelesUnited States
| | - Francesca V Mariani
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell ResearchUniversity of Southern CaliforniaLos AngelesUnited States
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36
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Matyjaszkiewicz A, Fiore G, Annunziata F, Grierson CS, Savery NJ, Marucci L, di Bernardo M. BSim 2.0: An Advanced Agent-Based Cell Simulator. ACS Synth Biol 2017; 6:1969-1972. [PMID: 28585809 DOI: 10.1021/acssynbio.7b00121] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Agent-based models (ABMs) provide a number of advantages relative to traditional continuum modeling approaches, permitting incorporation of great detail and realism into simulations, allowing in silico tracking of single-cell behaviors and correlation of these with emergent effects at the macroscopic level. In this study we present BSim 2.0, a radically new version of BSim, a computational ABM framework for modeling dynamics of bacteria in typical experimental environments including microfluidic chemostats. This is facilitated through the implementation of new methods including cells with capsular geometry that are able to physically and chemically interact with one another, a realistic model of cellular growth, a delay differential equation solver, and realistic environmental geometries.
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Affiliation(s)
- Antoni Matyjaszkiewicz
- Department of Engineering
Mathematics, University of Bristol, Merchant Venturers’ Building,
Woodland Road, Bristol BS8 1UB, U.K
- BrisSynBio, Life Sciences Building, Tyndall
Avenue, Bristol BS8 1TQ, U.K
| | - Gianfranco Fiore
- Department of Engineering
Mathematics, University of Bristol, Merchant Venturers’ Building,
Woodland Road, Bristol BS8 1UB, U.K
- BrisSynBio, Life Sciences Building, Tyndall
Avenue, Bristol BS8 1TQ, U.K
| | - Fabio Annunziata
- BrisSynBio, Life Sciences Building, Tyndall
Avenue, Bristol BS8 1TQ, U.K
- School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, U.K
| | - Claire S. Grierson
- BrisSynBio, Life Sciences Building, Tyndall
Avenue, Bristol BS8 1TQ, U.K
- School of Biological Sciences, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, U.K
| | - Nigel J. Savery
- BrisSynBio, Life Sciences Building, Tyndall
Avenue, Bristol BS8 1TQ, U.K
- School of Biochemistry, University of Bristol, Biomedical Sciences Building, University Walk, Bristol BS8 1TD, U.K
| | - Lucia Marucci
- Department of Engineering
Mathematics, University of Bristol, Merchant Venturers’ Building,
Woodland Road, Bristol BS8 1UB, U.K
- BrisSynBio, Life Sciences Building, Tyndall
Avenue, Bristol BS8 1TQ, U.K
| | - Mario di Bernardo
- Department of Engineering
Mathematics, University of Bristol, Merchant Venturers’ Building,
Woodland Road, Bristol BS8 1UB, U.K
- BrisSynBio, Life Sciences Building, Tyndall
Avenue, Bristol BS8 1TQ, U.K
- Department
of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
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37
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Gutiérrez M, Gregorio-Godoy P, Pérez del Pulgar G, Muñoz LE, Sáez S, Rodríguez-Patón A. A New Improved and Extended Version of the Multicell Bacterial Simulator gro. ACS Synth Biol 2017; 6:1496-1508. [PMID: 28438021 DOI: 10.1021/acssynbio.7b00003] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.
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Affiliation(s)
- Martín Gutiérrez
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Escuela
de Informática y Telecomunicaciones, Universidad Diego Portales, 8370190 Santiago, Chile
| | - Paula Gregorio-Godoy
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Guillermo Pérez del Pulgar
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Luis E. Muñoz
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Sandra Sáez
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Alfonso Rodríguez-Patón
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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38
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Godwin S, Ward D, Pedone E, Homer M, Fletcher AG, Marucci L. An extended model for culture-dependent heterogenous gene expression and proliferation dynamics in mouse embryonic stem cells. NPJ Syst Biol Appl 2017; 3:19. [PMID: 28794899 PMCID: PMC5543144 DOI: 10.1038/s41540-017-0020-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Revised: 05/31/2017] [Accepted: 06/20/2017] [Indexed: 12/18/2022] Open
Abstract
During development, pluripotency is a transient state describing a cell's ability to give rise to all three germ layers and germline. Recent studies have shown that, in vitro, pluripotency is highly dynamic: exogenous stimuli provided to cultures of mouse embryonic stem cells, isolated from pre-implantation blastocysts, significantly affect the spectrum of pluripotency. 2i/LIF, a recently defined serum-free medium, forces mouse embryonic stem cells into a ground-state of pluripotency, while serum/LIF cultures promote the co-existence of ground-like and primed-like mouse embryonic stem cell subpopulations. The latter heterogeneity correlates with temporal fluctuations of pluripotency markers, including the master regulator Nanog, in single cells. We propose a mathematical model of Nanog dynamics in both media, accounting for recent experimental data showing the persistence of a small Nanog Low subpopulation in ground-state pluripotency mouse embryonic stem cell cultures. The model integrates into the core pluripotency Gene Regulatory Network both inhibitors present in 2i/LIF (PD and Chiron), and feedback interactions with genes found to be differentially expressed in the two media. Our simulations and bifurcation analysis show that, in ground-state cultures, Nanog dynamics result from the combination of reduced noise in gene expression and the shift of the system towards a monostable, but still excitable, regulation. Experimental data and agent-based modelling simulations indicate that mouse embryonic stem cell proliferation dynamics vary in the two media, and cannot be reproduced by accounting only for Nanog-dependent cell-cycle regulation. We further demonstrate that both PD and Chiron play a key role in regulating heterogeneity in transcription factor expression and, ultimately, mouse embryonic stem cell fate decision.
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Affiliation(s)
- Simon Godwin
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK
| | - Daniel Ward
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK
| | - Elisa Pedone
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK.,School of Cellular & Molecular Medicine, University of Bristol, Bristol, BS8 1TD UK
| | - Martin Homer
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK
| | - Alexander G Fletcher
- School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH UK.,Bateson Centre, University of Sheffield, Sheffield, S10 2TN UK
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB UK.,School of Cellular & Molecular Medicine, University of Bristol, Bristol, BS8 1TD UK.,BrisSynBio, University of Bristol, Bristol, BS8 1TQ UK
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How Behaviour and the Environment Influence Transmission in Mobile Groups. TEMPORAL NETWORK EPIDEMIOLOGY 2017. [PMCID: PMC7123459 DOI: 10.1007/978-981-10-5287-3_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The movement of individuals living in groups leads to the formation of physical interaction networks over which signals such as information or disease can be transmitted. Direct contacts represent the most obvious opportunities for a signal to be transmitted. However, because signals that persist after being deposited into the environment may later be acquired by other group members, indirect environmentally-mediated transmission is also possible. To date, studies of signal transmission within groups have focused on direct physical interactions and ignored the role of indirect pathways. Here, we use an agent-based model to study how the movement of individuals and characteristics of the signal being transmitted modulate transmission. By analysing the dynamic interaction networks generated from these simulations, we show that the addition of indirect pathways speeds up signal transmission, while the addition of physically-realistic collisions between individuals in densely packed environments hampers it. Furthermore, the inclusion of spatial biases that induce the formation of individual territories, reveals the existence of a trade-off such that optimal signal transmission at the group level is only achieved when territories are of intermediate sizes. Our findings provide insight into the selective pressures guiding the evolution of behavioural traits in natural groups, and offer a means by which multi-agent systems can be engineered to achieve desired transmission capabilities.
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