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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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2
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Kim J, de Lorenzo V, Goñi‐Moreno Á. Pressure-dependent growth controls 3D architecture of Pseudomonas putida microcolonies. ENVIRONMENTAL MICROBIOLOGY REPORTS 2023; 15:708-715. [PMID: 37231623 PMCID: PMC10667634 DOI: 10.1111/1758-2229.13182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Colony formation is key to many ecological and biotechnological processes. In its early stages, colony formation involves the concourse of a number of physical and biological parameters for generation of a distinct 3D structure-the specific influence of which remains unclear. We focused on a thus far neglected aspect of the process, specifically the consequences of the differential pressure experienced by cells in the middle of a colony versus that endured by bacteria located in the growing periphery. This feature was characterized experimentally in the soil bacterium Pseudomonas putida. Using an agent-based model we recreated the growth of microcolonies in a scenario in which pressure was the only parameter affecting proliferation of cells. Simulations exposed that, due to constant collisions with other growing bacteria, cells have virtually no free space to move sideways, thereby delaying growth and boosting chances of overlapping on top of each other. This scenario was tested experimentally on agar surfaces. Comparison between experiments and simulations suggested that the inside/outside differential pressure determines growth, both timewise and in terms of spatial directions, eventually moulding colony shape. We thus argue that-at least in the case studied-mere physical pressure of growing cells suffices to explain key dynamics of colony formation.
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Affiliation(s)
- Juhyun Kim
- School of Life ScienceBK21 FOUR KNU Creative BioResearch Group Kyungpook National UniversityDaeguRepublic of Korea
| | - Víctor de Lorenzo
- Systems Biology DepartmentCentro Nacional de Biotecnología (CNB‐CSIC)Cantoblanco‐MadridSpain
| | - Ángel Goñi‐Moreno
- Centro de Biotecnología y Genómica de PlantasUniversidad Politécnica de Madrid (UPM)‐Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC)MadridSpain
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3
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Calibrating spatiotemporal models of microbial communities to microscopy data: A review. PLoS Comput Biol 2022; 18:e1010533. [PMID: 36227846 PMCID: PMC9560168 DOI: 10.1371/journal.pcbi.1010533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.
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4
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Stoof R, Goñi-Moreno Á. Modelling co-translational dimerization for programmable nonlinearity in synthetic biology. J R Soc Interface 2020; 17:20200561. [PMID: 33143595 DOI: 10.1098/rsif.2020.0561] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basis of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization-a well-known source of nonlinear dynamics. Specifically, our approach focuses on modelling co-translational dimerization: the formation of protein dimers during-and not after-translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e. post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics in vivo. The design, build and test of genetic circuits with on-demand nonlinear dynamics will greatly improve the programmability of synthetic biological systems.
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Affiliation(s)
- Ruud Stoof
- School of Computing, Newcastle University, Urban Sciences Building, Science Square, Newcastle upon Tyne NE4 5TG, UK
| | - Ángel Goñi-Moreno
- School of Computing, Newcastle University, Urban Sciences Building, Science Square, Newcastle upon Tyne NE4 5TG, UK.,Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politénica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
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5
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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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6
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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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7
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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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8
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Stoof R, Wood A, Goñi-Moreno Á. A Model for the Spatiotemporal Design of Gene Regulatory Circuits †. ACS Synth Biol 2019; 8:2007-2016. [PMID: 31429541 DOI: 10.1021/acssynbio.9b00022] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Mathematical modeling assists the design of synthetic regulatory networks by providing a detailed mechanistic understanding of biological systems. Models that can predict the performance of a design are fundamental for synthetic biology since they minimize iterations along the design-build-test lifecycle. Such predictability depends crucially on what assumptions (i.e., biological simplifications) the model considers. Here, we challenge a common assumption when it comes to the modeling of bacterial-based gene regulation: considering negligible the effects of intracellular physical space. It is commonly assumed that molecules, such as transcription factors (TF), are homogeneously distributed inside a cell, so there is no need to model their diffusion. We describe a mathematical model that accounts for molecular diffusion and show how simulations of network performance are decisively affected by the distance between its components. Specifically, the model focuses on the search by a TF for its target promoter. The combination of local searches, via one-dimensional sliding along the chromosome, and global searches, via three-dimensional diffusion through the cytoplasm, determine TF-promoter interplay. Previous experimental results with engineered bacteria in which the distance between TF source and target was minimized or enlarged were successfully reproduced by the spatially resolved model we introduce here. This suggests that the spatial specification of the circuit alone can be exploited as a design parameter in synthetic biology to select programmable output levels.
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Affiliation(s)
- Ruud Stoof
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, U.K
| | - Alexander Wood
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, U.K
| | - Ángel Goñi-Moreno
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, U.K
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9
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Hallinan JS, Wipat A, Kitney R, Woods S, Taylor K, Goñi‐Moreno A. Future‐proofing synthetic biology: educating the next generation. ENGINEERING BIOLOGY 2019. [DOI: 10.1049/enb.2019.0001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
| | - Anil Wipat
- School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Richard Kitney
- Department of BioengineeringImperial College LondonLondonUK
| | - Simon Woods
- Policy, Ethics and Life Sciences (PEALS) Research CentreNewcastle UniversityNewcastle upon TyneUK
| | - Ken Taylor
- Policy, Ethics and Life Sciences (PEALS) Research CentreNewcastle UniversityNewcastle upon TyneUK
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10
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Goñi-Moreno A, Nikel PI. High-Performance Biocomputing in Synthetic Biology-Integrated Transcriptional and Metabolic Circuits. Front Bioeng Biotechnol 2019; 7:40. [PMID: 30915329 PMCID: PMC6421265 DOI: 10.3389/fbioe.2019.00040] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 02/18/2019] [Indexed: 12/03/2022] Open
Abstract
Biocomputing uses molecular biology parts as the hardware to implement computational devices. By following pre-defined rules, often hard-coded into biological systems, these devices are able to process inputs and return outputs—thus computing information. Key to the success of any biocomputing endeavor is the availability of a wealth of molecular tools and biological motifs from which functional devices can be assembled. Synthetic biology is a fabulous playground for such purpose, offering numerous genetic parts that allow for the rational engineering of genetic circuits that mimic the behavior of electronic functions, such as logic gates. A grand challenge, as far as biocomputing is concerned, is to expand the molecular hardware available beyond the realm of genetic parts by tapping into the host metabolism. This objective requires the formalization of the interplay of genetic constructs with the rest of the cellular machinery. Furthermore, the field of metabolic engineering has had little intersection with biocomputing thus far, which has led to a lack of definition of metabolic dynamics as computing basics. In this perspective article, we advocate the conceptualization of metabolism and its motifs as the way forward to achieve whole-cell biocomputations. The design of merged transcriptional and metabolic circuits will not only increase the amount and type of information being processed by a synthetic construct, but will also provide fundamental control mechanisms for increased reliability.
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Affiliation(s)
- Angel Goñi-Moreno
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
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11
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Goñi-Moreno Á, Benedetti I, Kim J, de Lorenzo V. Deconvolution of Gene Expression Noise into Spatial Dynamics of Transcription Factor-Promoter Interplay. ACS Synth Biol 2017; 6:1359-1369. [PMID: 28355056 DOI: 10.1021/acssynbio.6b00397] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Gene expression noise is not only the mere consequence of stochasticity, but also a signal that reflects the upstream physical dynamics of the cognate molecular machinery. Soil bacteria facing recalcitrant pollutants exploit noise of catabolic promoters to deploy beneficial phenotypes such as metabolic bet-hedging and/or division of biochemical labor. Although the role of upstream promoter-regulator interplay in the origin of this noise is little understood, its specifications are probably ciphered in flow cytometry data patterns. We studied Pm promoter activity of the environmental bacterium Pseudomonas putida and its cognate regulator XylS by following expression of Pm-gfp fusions in single cells. Using mathematical modeling and computational simulations, we determined the kinetic properties of the system and used them as a baseline code to interpret promoter activity in terms of upstream regulator dynamics. Transcriptional noise was predicted to depend on the intracellular physical distance between regulator source (where XylS is produced) and the target promoter. Experiments with engineered bacteria in which this distance is minimized or enlarged confirmed the predicted effects of source/target proximity on noise patterns. This approach allowed deconvolution of cytometry data into mechanistic information on gene expression flow. It also provided a basis for selecting programmable noise levels in synthetic regulatory circuits.
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Affiliation(s)
- Ángel Goñi-Moreno
- Systems Biology Program, Centro Nacional de Biotecnología CSIC, Campus de Cantoblanco, Madrid 28049, Spain
| | - Ilaria Benedetti
- Systems Biology Program, Centro Nacional de Biotecnología CSIC, Campus de Cantoblanco, Madrid 28049, Spain
| | - Juhyun Kim
- Systems Biology Program, Centro Nacional de Biotecnología CSIC, Campus de Cantoblanco, Madrid 28049, Spain
| | - Víctor de Lorenzo
- Systems Biology Program, Centro Nacional de Biotecnología CSIC, Campus de Cantoblanco, Madrid 28049, Spain
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12
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Naylor J, Fellermann H, Ding Y, Mohammed WK, Jakubovics NS, Mukherjee J, Biggs CA, Wright PC, Krasnogor N. Simbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial Populations. ACS Synth Biol 2017; 6:1194-1210. [PMID: 28475309 DOI: 10.1021/acssynbio.6b00315] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Simbiotics is a spatially explicit multiscale modeling platform for the design, simulation and analysis of bacterial populations. Systems ranging from planktonic cells and colonies, to biofilm formation and development may be modeled. Representation of biological systems in Simbiotics is flexible, and user-defined processes may be in a variety of forms depending on desired model abstraction. Simbiotics provides a library of modules such as cell geometries, physical force dynamics, genetic circuits, metabolic pathways, chemical diffusion and cell interactions. Model defined processes are integrated and scheduled for parallel multithread and multi-CPU execution. A virtual lab provides the modeler with analysis modules and some simulated lab equipment, enabling automation of sample interaction and data collection. An extendable and modular framework allows for the platform to be updated as novel models of bacteria are developed, coupled with an intuitive user interface to allow for model definitions with minimal programming experience. Simbiotics can integrate existing standards such as SBML, and process microscopy images to initialize the 3D spatial configuration of bacteria consortia. Two case studies, used to illustrate the platform flexibility, focus on the physical properties of the biosystems modeled. These pilot case studies demonstrate Simbiotics versatility in modeling and analysis of natural systems and as a CAD tool for synthetic biology.
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Affiliation(s)
- Jonathan Naylor
- Interdisciplinary
Computing and Complex Biosystems (ICOS) research group, School of
Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Harold Fellermann
- Interdisciplinary
Computing and Complex Biosystems (ICOS) research group, School of
Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Yuchun Ding
- Interdisciplinary
Computing and Complex Biosystems (ICOS) research group, School of
Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Waleed K. Mohammed
- School of Dental Sciences, Newcastle University, Newcastle upon Tyne NE2 4BW, U.K
| | | | - Joy Mukherjee
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S10 2TN, U.K
| | - Catherine A. Biggs
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S10 2TN, U.K
| | - Phillip C. Wright
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Natalio Krasnogor
- Interdisciplinary
Computing and Complex Biosystems (ICOS) research group, School of
Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
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13
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Malwade A, Nguyen A, Sadat-Mousavi P, Ingalls BP. Predictive Modeling of a Batch Filter Mating Process. Front Microbiol 2017; 8:461. [PMID: 28377756 PMCID: PMC5359259 DOI: 10.3389/fmicb.2017.00461] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 03/06/2017] [Indexed: 01/21/2023] Open
Abstract
Quantitative characterizations of horizontal gene transfer are needed to accurately describe gene transfer processes in natural and engineered systems. A number of approaches to the quantitative description of plasmid conjugation have appeared in the literature. In this study, we seek to extend that work, motivated by the question of whether a mathematical model can accurately predict growth and conjugation dynamics in a batch process. We used flow cytometry to make time-point observations of a filter-associated mating between two E. coli strains, and fit ordinary differential equation models to the data. A model comparison analysis identified the model formulation that is best supported by the data. Identifiability analysis revealed that the parameters were estimated with acceptable accuracy. The predictive power of the model was assessed by comparison with test data that demanded extrapolation from the training experiments. This study represents the first attempt to assess the quality of model predictions for plasmid conjugation. Our successful application of this approach lays a foundation for predictive modeling that can be used both in the study of natural plasmid transmission and in model-based design of engineering approaches that employ conjugation, such as plasmid-mediated bioaugmentation.
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Affiliation(s)
- Akshay Malwade
- Department of Applied Mathematics, University of Waterloo Waterloo, ON, Canada
| | - Angel Nguyen
- Department of Applied Mathematics, University of Waterloo Waterloo, ON, Canada
| | | | - Brian P Ingalls
- Department of Applied Mathematics, University of Waterloo Waterloo, ON, Canada
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