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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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Soleimani M, Szafranski SP, Qu T, Mukherjee R, Stiesch M, Wriggers P, Junker P. Numerical and experimental investigation of multi-species bacterial co-aggregation. Sci Rep 2023; 13:11839. [PMID: 37481628 PMCID: PMC10363141 DOI: 10.1038/s41598-023-38806-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/14/2023] [Indexed: 07/24/2023] Open
Abstract
This paper deals with the mathematical modeling of bacterial co-aggregation and its numerical implementation in a FEM framework. Since the concept of co-aggregation refers to the physical binding between cells of different microbial species, a system composed of two species is considered in the modeling framework. The extension of the model to an arbitrary number of species is straightforward. In addition to two-species (multi-species growth) dynamics, the transport of a nutritional substance and the extent of co-aggregation are introduced into the model as the third and fourth primary variables. A phase-field modeling approach is employed to describe the co-aggregation between the two species. The mathematical model is three-dimensional and fully based on the continuum description of the problem without any need for discrete agents which are the key elements of the individual-based modeling approach. It is shown that the use of a phase-field-based model is equivalent to a particular form of classical diffusion-reaction systems. Unlike the so-called mixture models, the evolution of each component of the multi-species system is captured thanks to the inherent capability of phase-field modeling in treating systems consisting of distinct multi-phases. The details of numerical implementation in a FEM framework are also presented. Indeed, a new multi-field user element is developed and implemented in ANSYS for this multiphysics problem. Predictions of the model are compared with the experimental observations. By that, the versatility and applicability of the model and the numerical tool are well established.
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Affiliation(s)
- Meisam Soleimani
- Institute of Continuum Mechanics (IKM), Leibniz Universität Hannover, Hannover, Germany.
| | | | - Taoran Qu
- Medical School Hannover (MHH), Hannover, Germany
| | | | | | - Peter Wriggers
- Institute of Continuum Mechanics (IKM), Leibniz Universität Hannover, Hannover, Germany
| | - Philipp Junker
- Institute of Continuum Mechanics (IKM), Leibniz Universität Hannover, Hannover, Germany
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Novel Ground-Up 3D Multicellular Simulators for Synthetic Biology CAD Integrating Stochastic Gillespie Simulations Benchmarked with Topologically Variable SBML Models. Genes (Basel) 2023; 14:genes14010154. [PMID: 36672895 PMCID: PMC9859520 DOI: 10.3390/genes14010154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023] Open
Abstract
The elevation of Synthetic Biology from single cells to multicellular simulations would be a significant scale-up. The spatiotemporal behavior of cellular populations has the potential to be prototyped in silico for computer assisted design through ergonomic interfaces. Such a platform would have great practical potential across medicine, industry, research, education and accessible archiving in bioinformatics. Existing Synthetic Biology CAD systems are considered limited regarding population level behavior, and this work explored the in silico challenges posed from biological and computational perspectives. Retaining the connection to Synthetic Biology CAD, an extension of the Infobiotics Workbench Suite was considered, with potential for the integration of genetic regulatory models and/or chemical reaction networks through Next Generation Stochastic Simulator (NGSS) Gillespie algorithms. These were executed using SBML models generated by in-house SBML-Constructor over numerous topologies and benchmarked in association with multicellular simulation layers. Regarding multicellularity, two ground-up multicellular solutions were developed, including the use of Unreal Engine 4 contrasted with CPU multithreading and Blender visualization, resulting in a comparison of real-time versus batch-processed simulations. In conclusion, high-performance computing and client-server architectures could be considered for future works, along with the inclusion of numerous biologically and physically informed features, whilst still pursuing ergonomic solutions.
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A Self-Controlled and Self-Healing Model of Bacterial Cells. MEMBRANES 2022; 12:membranes12070678. [PMID: 35877878 PMCID: PMC9324567 DOI: 10.3390/membranes12070678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022]
Abstract
A new kind of self-assembly model, morphogenetic (M) systems, assembles spatial units into larger structures through local interactions of simpler components and enables discovery of new principles for cellular membrane assembly, development, and its interface function. The model is based on interactions among three kinds of constitutive objects such as tiles and protein-like elements in discrete time and continuous 3D space. It was motivated by achieving a balance between three conflicting goals: biological, physical-chemical, and computational realism. A recent example is a unified model of morphogenesis of a single biological cell, its membrane and cytoskeleton formation, and finally, its self-reproduction. Here, a family of dynamic M systems (Mbac) is described with similar characteristics, modeling the process of bacterial cell formation and division that exhibits bacterial behaviors of living cells at the macro-level (including cell growth that is self-controlled and sensitive to the presence/absence of nutrients transported through membranes), as well as self-healing properties. Remarkably, it consists of only 20 or so developmental rules. Furthermore, since the model exhibits membrane formation and septic mitosis, it affords more rigorous definitions of concepts such as injury and self-healing that enable quantitative analyses of these kinds of properties. Mbac shows that self-assembly and interactions of living organisms with their environments and membrane interfaces are critical for self-healing, and that these properties can be defined and quantified more rigorously and precisely, despite their complexity.
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Du B, Wang S, Chen G, Wang G, Liu L. Nutrient starvation intensifies chlorine disinfection-stressed biofilm formation. CHEMOSPHERE 2022; 295:133827. [PMID: 35122818 DOI: 10.1016/j.chemosphere.2022.133827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 12/09/2021] [Accepted: 01/29/2022] [Indexed: 06/14/2023]
Abstract
Bacterial surface attachment and subsequent biofilm expansion represent an essential adaptation to environmental signals and stresses, which are of great concern for many natural and engineered ecosystems. Yet the underlying mechanisms and driving forces of biofilm formation in a chlorinated and nutrient-restricted system remain sketchy. In this study, we coupled an experimental investigation and modeling simulation to understand how chlorination and nutrient limitation conspire to form biofilm using Pseudomonas aeruginosa as a model bacterium. Experimental results showed that moderate chlorination at 1.0 mg/L led to biofilm development amplified to 2.6 times of those without chlorine, while additional nutrient limitation (of 1/50-diluted or 0.4 g/L LB broth culture) achieved 4.6 times increment as compared to those of undiluted scenarios (of 20 g/L LB broth culture) with absence of chlorination after 24 h exposure. Meanwhile, intermediate chlorination stimulated instant flagellar motility and subsequently extracellular polymeric substances (EPS) secretion, particularly under limited nutrient condition (of 1/50-diluted or 0.4 g/L LB broth culture) that retarded chlorine consumption and provoked bacterial nutrient-limitation response. From our simulations, chlorine and resource levels along with associated spatio-temporal variations collectively drove bacterial cell movement and EPS excretion. Our results demonstrated that restraining nutrient intensified chlorination-excited cell movement and EPS production that reinforced biological and cell-surface interactions, thereby encouraging bacterial surface attachment and subsequent biofilm development. The findings provide the insights into the linkage of disinfectant and nutrient-regulated bacterial functional responses with consequent micro-habitats and biofilm dynamics.
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Affiliation(s)
- Bang Du
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Shudong Wang
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Guowei Chen
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Gang Wang
- Department of Soil and Water Sciences, China Agricultural University, Beijing, 100193, China
| | - Li Liu
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China.
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Agmon E, Spangler RK, Skalnik CJ, Poole W, Peirce SM, Morrison JH, Covert MW. Vivarium: an interface and engine for integrative multiscale modeling in computational biology. Bioinformatics 2022; 38:1972-1979. [PMID: 35134830 PMCID: PMC8963310 DOI: 10.1093/bioinformatics/btac049] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 12/14/2021] [Accepted: 01/28/2022] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION This article introduces Vivarium-software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists. RESULTS Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium's discrete-event simulation engine. Vivarium's utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible-Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales. AVAILABILITY AND IMPLEMENTATION The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ryan K Spangler
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | - William Poole
- Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA
| | - Shayn M Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Jerry H Morrison
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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Bogdanowski A, Banitz T, Muhsal LK, Kost C, Frank K. McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems. PLoS Comput Biol 2022; 18:e1009777. [PMID: 35073313 PMCID: PMC8830788 DOI: 10.1371/journal.pcbi.1009777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/10/2022] [Accepted: 12/20/2021] [Indexed: 01/30/2023] Open
Abstract
Individual-based modeling is widely applied to investigate the ecological mechanisms driving microbial community dynamics. In such models, the population or community dynamics emerge from the behavior and interplay of individual entities, which are simulated according to a predefined set of rules. If the rules that govern the behavior of individuals are based on generic and mechanistically sound principles, the models are referred to as next-generation individual-based models. These models perform particularly well in recapitulating actual ecological dynamics. However, implementation of such models is time-consuming and requires proficiency in programming or in using specific software, which likely hinders a broader application of this powerful method. Here we present McComedy, a modeling tool designed to facilitate the development of next-generation individual-based models of microbial consumer-resource systems. This tool allows flexibly combining pre-implemented building blocks that represent physical and biological processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems is demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. With this article, we provide a versatile tool for developing next-generation individual-based models that can foster understanding of microbial ecology in both research and education. Microorganisms such as bacteria and fungi can be found in virtually any natural environment. To better understand the ecology of these microorganisms–which is important for several research fields including medicine, biotechnology, and conservation biology–researchers often use computer models to simulate and predict the behavior of microbial communities. Commonly, a particular technique called individual-based modeling is used to generate structurally realistic models of these communities by explicitly simulating each individual microorganism. Here we developed a tool called McComedy that helps researchers applying individual-based modeling efficiently without having to program low-level processes, thus allowing them to focus on their actual research questions. To test whether McComedy is not only convenient to use but also generates meaningful models, we used it to reproduce previously reported findings of two other research groups. Given that our results could well recapitulate and furthermore extend the original findings, we are confident that McComedy is a powerful and versatile tool that can help to address fundamental questions in microbial ecology.
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Affiliation(s)
- André Bogdanowski
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
| | - Thomas Banitz
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
| | - Linea Katharina Muhsal
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
| | - Christian Kost
- Osnabrück University, Department of Ecology, School of Biology/Chemistry, Osnabrück, Germany
| | - Karin Frank
- Helmholtz-Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
- Osnabrück University, Institute for Environmental Systems Research, Osnabrück, Germany
- iDiv – German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, Germany
- * E-mail:
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8
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Konur S, Mierla L, Fellermann H, Ladroue C, Brown B, Wipat A, Twycross J, Dun BP, Kalvala S, Gheorghe M, Krasnogor N. Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation. ACS Synth Biol 2021; 10:1931-1945. [PMID: 34339602 DOI: 10.1021/acssynbio.1c00143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.
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Affiliation(s)
- Savas Konur
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K
| | - Laurentiu Mierla
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K
| | - Harold Fellermann
- Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K
| | - Christophe Ladroue
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K
| | - Bradley Brown
- Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K
| | - Anil Wipat
- Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K
| | - Jamie Twycross
- School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, U.K
| | - Boyang Peter Dun
- Department of Computer Science, Stanford University, Stanford, California 94305, United States
| | - Sara Kalvala
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K
| | - Marian Gheorghe
- Department of Computer Science, University of Bradford, Bradford, BD7 1DP, U.K
| | - Natalio Krasnogor
- Interdisciplinary Computing and Complex Biosystems Research Group, Newcastle University, Newcastle, NE1 7RU, U.K
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Koshy-Chenthittayil S, Archambault L, Senthilkumar D, Laubenbacher R, Mendes P, Dongari-Bagtzoglou A. Agent Based Models of Polymicrobial Biofilms and the Microbiome-A Review. Microorganisms 2021; 9:417. [PMID: 33671308 PMCID: PMC7922883 DOI: 10.3390/microorganisms9020417] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/05/2021] [Accepted: 02/16/2021] [Indexed: 02/06/2023] Open
Abstract
The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.
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Affiliation(s)
- Sherli Koshy-Chenthittayil
- Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT 06030, USA; (S.K.-C.); (L.A.); (P.M.)
| | - Linda Archambault
- Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT 06030, USA; (S.K.-C.); (L.A.); (P.M.)
- Department of Oral Health and Diagnostic Sciences, University of Connecticut Health Center, Farmington, CT 06030, USA
| | | | | | - Pedro Mendes
- Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT 06030, USA; (S.K.-C.); (L.A.); (P.M.)
- Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Anna Dongari-Bagtzoglou
- Department of Oral Health and Diagnostic Sciences, University of Connecticut Health Center, Farmington, CT 06030, USA
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10
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Agmon E, Spangler RK. A Multi-Scale Approach to Modeling E. coli Chemotaxis. ENTROPY 2020; 22:e22101101. [PMID: 33286869 PMCID: PMC7597207 DOI: 10.3390/e22101101] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/25/2020] [Accepted: 09/25/2020] [Indexed: 12/25/2022]
Abstract
The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium-an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium's approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.
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11
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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: 15] [Impact Index Per Article: 3.8] [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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Naylor J, Fellermann H, Krasnogor N. Easybiotics: a GUI for 3D physical modelling of multi-species bacterial populations. Bioinformatics 2020; 35:3859-3860. [PMID: 30796819 DOI: 10.1093/bioinformatics/btz131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 02/12/2019] [Accepted: 02/21/2019] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION 3D physical modelling is a powerful computational technique that allows for the simulation of complex systems such as consortia of mixed bacterial species. The complexities in physical modelling reside in the knowledge intensive model building process and the computational expense in calculating their numerical solutions. These models can offer insights into microbiology, both in understanding natural systems and as design tools for developing novel synthetic bacterial systems. Developing a robust synthetic system typically requires multiple iterations around the specify→design→build→test cycle to meet specifications. This process is laborious and expensive for both the computational and laboratory aspects, hence any improvement in any of the workflow steps would be welcomed. We have previously introduced Simbiotics, a powerful and flexible platform for designing and analyzing 3D simulations of mixed species bacterial populations. Simbiotics requires programming experience to use which creates barriers to entry for use of the tool. RESULTS In the spirit of enabling biologists who may not have programming skills to install and utilize Simbiotics, we present in this application note Easybiotics, a user-friendly graphical user interface for Simbiotics. Users may design, simulate and analyze models from within the graphical user interface, with features such as live graph plotting and parameter sweeps. Easybiotics provides full access to all of Simbiotics simulation features, such as cell growth, motility and gene regulation. AVAILABILITY AND IMPLEMENTATION Easybiotics and Simbiotics are free to use under the GPL3.0 licence, and can be found at: http://ico2s.org/software/simbiotics.html. We also provide readily downloadable virtual machine sandboxes to facilitate rapid installation.
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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 NE4 5TG, UK
| | - Harold Fellermann
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Natalio Krasnogor
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
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Du B, Gu Y, Chen G, Wang G, Liu L. Flagellar motility mediates early-stage biofilm formation in oligotrophic aquatic environment. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 194:110340. [PMID: 32135377 DOI: 10.1016/j.ecoenv.2020.110340] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 02/12/2020] [Accepted: 02/14/2020] [Indexed: 06/10/2023]
Abstract
Flagellar motility enables resource acquisition and noxious substance evasion, underpinning imperative ecological processes in aquatic environments. Yet the underlying mechanism that links flagellar motility with surface attachment and thereby biofilm formation, especially in conditions of limited resource availability, remains elusive. Here, we present experimental and modeling evidence to unveil bacterial motility and biofilm formation under nutrient-limited stresses with Pseudomonas aeruginosa (WT) and its nonflagellated isogenic mutant (ΔfliC) as model bacteria. Results revealed that boosted flagellar motility of WT strain promoted biofilm initialization to a peak value of 0.99 × 107 cells/cm2 at 1/50 dilution after 20 min incubation. We hypothesized that bacteria can invoke instant motility acceleration for survival confronting nutrient-limited stress, accompanied by optimized chemotactic foraging through sensing ambient chemical gradients. Accordingly, accelerated cell motility in oligotrophic environment created increased cell-cell and cell-surface interactions and thereof facilitated biofilm initialization. It was confirmed by the consistence of modeling predictions and experimental results of cell velocity and surface attachment. With the development of biofilm, promotion effect of flagellar motility responding to nutrient deprivation-stress faded out. Instead, loss of motility profiting increased growth rates and extracellular protein excretion, associated with an enhancement of biofilm development for the mutant in oligotrophic aquatic environment. For both strains, nutrient limitation evidently reduced planktonic cell propagation as expected. Our results offer new insights into the mechanical understanding of biofilm formation shaped by environmental stresses and associating biological responses.
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Affiliation(s)
- Bang Du
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Yue Gu
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Guowei Chen
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Gang Wang
- Department of Soil and Water Sciences, China Agricultural University, Beijing, 100193, China
| | - Li Liu
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China.
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Li B, Taniguchi D, Gedara JP, Gogulancea V, Gonzalez-Cabaleiro R, Chen J, McGough AS, Ofiteru ID, Curtis TP, Zuliani P. NUFEB: A massively parallel simulator for individual-based modelling of microbial communities. PLoS Comput Biol 2019; 15:e1007125. [PMID: 31830032 PMCID: PMC6932830 DOI: 10.1371/journal.pcbi.1007125] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 12/26/2019] [Accepted: 10/30/2019] [Indexed: 12/02/2022] Open
Abstract
We present NUFEB (Newcastle University Frontiers in Engineering Biology), a flexible, efficient, and open source software for simulating the 3D dynamics of microbial communities. The tool is based on the Individual-based Modelling (IbM) approach, where microbes are represented as discrete units and their behaviour changes over time due to a variety of processes. This approach allows us to study population behaviours that emerge from the interaction between individuals and their environment. NUFEB is built on top of the classical molecular dynamics simulator LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), which we extended with IbM features. A wide range of biological, physical and chemical processes are implemented to explicitly model microbial systems, with particular emphasis on biofilms. NUFEB is fully parallelised and allows for the simulation of large numbers of microbes (107 individuals and beyond). The parallelisation is based on a domain decomposition scheme that divides the domain into multiple sub-domains which are distributed to different processors. NUFEB also offers a collection of post-processing routines for the visualisation and analysis of simulation output. In this article, we give an overview of NUFEB’s functionalities and implementation details. We provide examples that illustrate the type of microbial systems NUFEB can be used to model and simulate. Individual-based Models (IbM) are one of the most promising frameworks to study microbial communities, as they can explicitly describe the behaviour of each cell. The development of a general-purpose IbM solver should focus on efficiency and flexibility due to the unique characteristics of microbial systems. However, available tools for these purposes present significant limitations. Most of them only facilitate serial computing for single simulation, or only focus on biological processes, but do not model mechanical and chemical processes in detail. In this work, we introduce the IbM solver NUFEB that addresses some of these shortcomings. The tool facilitates the modelling of much needed biological, chemical, physical and individual microbes in detail, and offers the flexibility of model extension and customisation. NUFEB is also fully parallelised and allows for the simulation of large complex microbial system. In this paper, we first give an overview of NUFEB’s functionalities and implementation details. Then, we use NUFEB to model and simulate a biofilm system with fluid dynamics, and a large and complex biofilm system with multiple microbial functional groups and multiple nutrients.
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Affiliation(s)
- Bowen Li
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Denis Taniguchi
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Valentina Gogulancea
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
- Chemical and Biochemical Engineering Department, University Politehnica of Bucharest, Bucharest, Romania
| | | | - Jinju Chen
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Irina Dana Ofiteru
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Thomas P. Curtis
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
- * E-mail: (TC); (PZ)
| | - Paolo Zuliani
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, Newcastle upon Tyne, United Kingdom
- * E-mail: (TC); (PZ)
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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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17
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Gogulancea V, González-Cabaleiro R, Li B, Taniguchi D, Jayathilake PG, Chen J, Wilkinson D, Swailes D, McGough AS, Zuliani P, Ofiteru ID, Curtis TP. Individual Based Model Links Thermodynamics, Chemical Speciation and Environmental Conditions to Microbial Growth. Front Microbiol 2019; 10:1871. [PMID: 31456784 PMCID: PMC6700366 DOI: 10.3389/fmicb.2019.01871] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 07/29/2019] [Indexed: 11/13/2022] Open
Abstract
Individual based Models (IbM) must transition from research tools to engineering tools. To make the transition we must aspire to develop large, three dimensional and physically and biologically credible models. Biological credibility can be promoted by grounding, as far as possible, the biology in thermodynamics. Thermodynamic principles are known to have predictive power in microbial ecology. However, this in turn requires a model that incorporates pH and chemical speciation. Physical credibility implies plausible mechanics and a connection with the wider environment. Here, we propose a step toward that ideal by presenting an individual based model connecting thermodynamics, pH and chemical speciation and environmental conditions to microbial growth for 5·105 individuals. We have showcased the model in two scenarios: a two functional group nitrification model and a three functional group anaerobic community. In the former, pH and connection to the environment had an important effect on the outcomes simulated. Whilst in the latter pH was less important but the spatial arrangements and community productivity (that is, methane production) were highly dependent on thermodynamic and reactor coupling. We conclude that if IbM are to attain their potential as tools to evaluate the emergent properties of engineered biological systems it will be necessary to combine the chemical, physical, mechanical and biological along the lines we have proposed. We have still fallen short of our ideals because we cannot (yet) calculate specific uptake rates and must develop the capacity for longer runs in larger models. However, we believe such advances are attainable. Ideally in a common, fast and modular platform. For future innovations in IbM will only be of use if they can be coupled with all the previous advances.
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Affiliation(s)
- Valentina Gogulancea
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
- Chemical and Biochemical Department, School of Applied Chemistry and Materials Science, University Politehnica of Bucharest, Bucharest, Romania
| | | | - Bowen Li
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Denis Taniguchi
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Jinju Chen
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Darren Wilkinson
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - David Swailes
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Paolo Zuliani
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Irina Dana Ofiteru
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Thomas P. Curtis
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
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Population Dynamics of Autocatalytic Sets in a Compartmentalized Spatial World. Life (Basel) 2018; 8:life8030033. [PMID: 30126201 PMCID: PMC6161236 DOI: 10.3390/life8030033] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/13/2018] [Accepted: 08/18/2018] [Indexed: 12/30/2022] Open
Abstract
Autocatalytic sets are self-sustaining and collectively catalytic chemical reaction networks which are believed to have played an important role in the origin of life. Simulation studies have shown that autocatalytic sets are, in principle, evolvable if multiple autocatalytic subsets can exist in different combinations within compartments, i.e., so-called protocells. However, these previous studies have so far not explicitly modeled the emergence and dynamics of autocatalytic sets in populations of compartments in a spatial environment. Here, we use a recently developed software tool to simulate exactly this scenario, as an important first step towards more realistic simulations and experiments on autocatalytic sets in protocells.
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Phage mobility is a core determinant of phage-bacteria coexistence in biofilms. ISME JOURNAL 2017; 12:531-543. [PMID: 29125597 PMCID: PMC5776469 DOI: 10.1038/ismej.2017.190] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 09/17/2017] [Accepted: 09/26/2017] [Indexed: 12/12/2022]
Abstract
Many bacteria are adapted for attaching to surfaces and for building complex communities, termed biofilms. The biofilm mode of life is predominant in bacterial ecology. So too is the exposure of bacteria to ubiquitous viral pathogens, termed bacteriophages. Although biofilm-phage encounters are likely to be common in nature, little is known about how phages might interact with biofilm-dwelling bacteria. It is also unclear how the ecological dynamics of phages and their hosts depend on the biological and physical properties of the biofilm environment. To make headway in this area, we develop a biofilm simulation framework that captures key mechanistic features of biofilm growth and phage infection. Using these simulations, we find that the equilibrium state of interaction between biofilms and phages is governed largely by nutrient availability to biofilms, infection likelihood per host encounter and the ability of phages to diffuse through biofilm populations. Interactions between the biofilm matrix and phage particles are thus likely to be of fundamental importance, controlling the extent to which bacteria and phages can coexist in natural contexts. Our results open avenues to new questions of host-parasite coevolution and horizontal gene transfer in spatially structured biofilm contexts.
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