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Xing W, Gao D, Wang Y, Li B, Zhang Z, Zuliani P, Yao H, Curtis TP. Cooperation between autotrophic and heterotrophic denitrifiers under low C/N ratios revealed by individual-based modelling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:171091. [PMID: 38387566 DOI: 10.1016/j.scitotenv.2024.171091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/17/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Denitrifying biofilms, in which autotrophic denitrifiers (AD) and heterotrophic denitrifiers (HD) coexist, play a crucial role in removing nitrate from water or wastewater. However, it is difficult to elucidate the interactions between HD and AD through sequencing-based experimental methods. Here, we developed an individual-based model to describe the interspecies dynamics and priority effects between sulfur-based AD (Thiobacillus denitrificans) and HD (Thauera phenylcarboxya) under different C/N ratios. In test I (coexistence simulation), AD and HD were initially inoculated at a ratio of 1:1. The simulation results showed excellent denitrification performance and a coaggregation pattern of denitrifiers, indicating that cooperation was the predominant interaction at a C/N ratio of 0.25 to 1.5. In test II (invasion simulation), in which only one type of denitrifier was initially inoculated and the other was added at the invasion time, denitrifiers exhibited a stratification pattern in biofilms. When HD invaded AD, the final HD abundance decreased with increasing invasion time, indicating an enhanced priority effect. When AD invaded HD, insufficient organic carbon sources weakened the priority effect by limiting the growth of HD populations. This study reveals the interaction between autotrophic and heterotrophic denitrifiers, providing guidance for optimizing wastewater treatment process.
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Affiliation(s)
- Wei Xing
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing 100044, PR China; Tangshan Research Institute of Beijing Jiaotong University, Hebei 063000, PR China.
| | - Daoqing Gao
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing 100044, PR China
| | - Yan Wang
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing 100044, PR China
| | - Bowen Li
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
| | - Zexi Zhang
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing 100044, PR China
| | - Paolo Zuliani
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom; Dipartimento di Informatica Università di Roma "La Sapienza", Rome 00198, Italy
| | - Hong Yao
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing 100044, PR China; Tangshan Research Institute of Beijing Jiaotong University, Hebei 063000, PR China.
| | - Thomas P Curtis
- School of Engineering, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
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2
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Sloan WT, Gómez-Borraz TL. Engineering biology in the face of uncertainty. Interface Focus 2023; 13:20230001. [PMID: 37303745 PMCID: PMC10251114 DOI: 10.1098/rsfs.2023.0001] [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: 01/02/2023] [Accepted: 03/24/2023] [Indexed: 06/13/2023] Open
Abstract
Combining engineering and biology surely must be a route to delivering solutions to the world's most pressing problems in depleting resources, energy and the environment. Engineers and biologists have long recognized the power in coupling their disciplines and have evolved a healthy variety of approaches to realizing technologies. Yet recently, there has been a movement to narrow the remit of engineering biology. Its definition as 'the application of engineering principles to the design of biological systems' ought to encompass a broad church. However, the emphasis is firmly on construction '…of novel biological devices and systems from standardized artificial parts' within cells. Thus, engineering biology has become synonymous with synthetic biology, despite the many longstanding technologies that use natural microbial communities. The focus on the nuts and bolts of synthetic organisms may be deflecting attention from the significant challenge of delivering solutions at scale, which cuts across all engineering biology, synthetic and natural. Understanding, let alone controlling, every component of an engineered system is an unrealistic goal. To realize workable solutions in a timely manner we must develop systematic ways of engineering biology in the face of the uncertainties that are inherent in biological systems and that arise through lack of knowledge.
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Affiliation(s)
- William T. Sloan
- James Watt School of Engineering, University of Glasgow, Glasgow, UK
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3
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Zhai H, Yeo J. Computational Design of Antimicrobial Active Surfaces via Automated Bayesian Optimization. ACS Biomater Sci Eng 2023; 9:269-279. [PMID: 36537745 DOI: 10.1021/acsbiomaterials.2c01079] [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: 12/24/2022]
Abstract
Biofilms pose significant problems for engineers in diverse fields, such as marine science, bioenergy, and biomedicine, where effective biofilm control is a long-term goal. The adhesion and surface mechanics of biofilms play crucial roles in generating and removing biofilm. Designing customized nanosurfaces with different surface topologies can alter the adhesive properties to remove biofilms more easily and greatly improve long-term biofilm control. To rapidly design such topologies, we employ individual-based modeling and Bayesian optimization to automate the design process and generate different active surfaces for effective biofilm removal. Our framework successfully generated optimized functional nanosurfaces for improved biofilm removal through applied shear and vibration. Densely distributed short pillar topography is the optimal geometry to prevent biofilm formation. Under fluidic shearing, the optimal topography is to sparsely distribute tall, slim, pillar-like structures. When subjected to either vertical or lateral vibrations, thick trapezoidal cones are found to be optimal. Optimizing the vibrational loading indicates a small vibration magnitude with relatively low frequencies is more efficient in removing biofilm. Our results provide insights into various engineering fields that require surface-mediated biofilm control. Our framework can also be applied to more general materials design and optimization.
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Affiliation(s)
- Hanfeng Zhai
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York14850, United States
| | - Jingjie Yeo
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York14850, United States
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4
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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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5
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van den Berg NI, Machado D, Santos S, Rocha I, Chacón J, Harcombe W, Mitri S, Patil KR. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat Ecol Evol 2022; 6:855-865. [PMID: 35577982 PMCID: PMC7613029 DOI: 10.1038/s41559-022-01746-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/23/2022] [Indexed: 12/20/2022]
Abstract
Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.
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Affiliation(s)
| | - Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sophia Santos
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Jeremy Chacón
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - William Harcombe
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - Sara Mitri
- Département de Microbiologie Fondamentale, University of Lausanne, Lausanne, Switzerland
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
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6
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Chakrawal A, Calabrese S, Herrmann AM, Manzoni S. Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth. Front Microbiol 2022; 13:859063. [PMID: 35656001 PMCID: PMC9152356 DOI: 10.3389/fmicb.2022.859063] [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] [Received: 01/20/2022] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Microorganisms function as open systems that exchange matter and energy with their surrounding environment. Even though mass (carbon and nutrients) and energy exchanges are tightly linked, there is a lack of integrated approaches that combine these fluxes and explore how they jointly impact microbial growth. Such links are essential to predicting how the growth rate of microorganisms varies, especially when the stoichiometry of carbon- (C) and nitrogen (N)-uptake is not balanced. Here, we present a theoretical framework to quantify the microbial growth rate for conditions of C-, N-, and energy-(co-) limitations. We use this framework to show how the C:N ratio and the degree of reduction of the organic matter (OM), which is also the electron donor, availability of electron acceptors (EAs), and the different sources of N together control the microbial growth rate under C, nutrient, and energy-limited conditions. We show that the growth rate peaks at intermediate values of the degree of reduction of OM under oxic and C-limited conditions, but not under N-limited conditions. Under oxic conditions and with N-poor OM, the growth rate is higher when the inorganic N (NInorg)-source is ammonium compared to nitrate due to the additional energetic cost involved in nitrate reduction. Under anoxic conditions, when nitrate is both EA and NInorg-source, the growth rates of denitrifiers and microbes performing the dissimilatory nitrate reduction to ammonia (DNRA) are determined by both OM degree of reduction and nitrate-availability. Consistent with the data, DNRA is predicted to foster growth under extreme nitrate-limitation and with a reduced OM, whereas denitrifiers are favored as nitrate becomes more available and in the presence of oxidized OM. Furthermore, the growth rate is reduced when catabolism is coupled to low energy yielding EAs (e.g., sulfate) because of the low carbon use efficiency (CUE). However, the low CUE also decreases the nutrient demand for growth, thereby reducing N-limitation. We conclude that bioenergetics provides a useful conceptual framework for explaining growth rates under different metabolisms and multiple resource-limitations.
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Affiliation(s)
- Arjun Chakrawal
- Department of Physical Geography, Stockholm University, Stockholm, Sweden.,Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Salvatore Calabrese
- Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, United States
| | - Anke M Herrmann
- Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Stefano Manzoni
- Department of Physical Geography, Stockholm University, Stockholm, Sweden.,Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
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7
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Ofițeru ID, Picioreanu C. No model is perfect, but some are useful. Science 2022; 376:914-916. [PMID: 35617381 DOI: 10.1126/science.abq0956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Agent-based model should inform the action plan to curb algal blooms in Lake Erie.
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Affiliation(s)
- Irina D Ofițeru
- School of Engineering, Newcastle University, Merz Court, Newcastle upon Tyne NE1 7RU, UK
| | - Cristian Picioreanu
- Water Desalination and Reuse Center, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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8
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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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9
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González-Cabaleiro R, Martinez-Rabert E, Argiz L, van Kessel MA, Smith CJ. A framework based on fundamental biochemical principles to engineer microbial community dynamics. Curr Opin Biotechnol 2021; 67:111-118. [PMID: 33540361 DOI: 10.1016/j.copbio.2021.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/18/2020] [Accepted: 01/03/2021] [Indexed: 11/26/2022]
Abstract
Microbial communities are complex but there are basic principles we can apply to constrain the assumed stochasticity of their activity. By understanding the trade-offs behind the kinetic parameters that define microbial growth, we can explain how local interspecies dependencies arise and shape the emerging properties of a community. If we integrate these theoretical descriptions with experimental 'omics' data and bioenergetics analysis of specific environmental conditions, predictions on activity, assembly and spatial structure can be obtained reducing the a priori unpredictable complexity of microbial communities. This information can be used to define the appropriate selective pressures to engineer bioprocesses and propose new hypotheses which can drive experimental research to accelerate innovation in biotechnology.
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Affiliation(s)
- Rebeca González-Cabaleiro
- James Watt School of Engineering, Infrastructure and Environment Research Division, University of Glasgow, Rankine Building, Glasgow, G12 8LT, UK.
| | - Eloi Martinez-Rabert
- James Watt School of Engineering, Infrastructure and Environment Research Division, University of Glasgow, Rankine Building, Glasgow, G12 8LT, UK
| | - Lucia Argiz
- CRETUS Institute, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Galicia, Spain
| | - Maartje Ahj van Kessel
- Radboud University, Department of Microbiology, Institute of Water and Wetland Research, Radboud University, Nijmegen, The Netherlands
| | - Cindy J Smith
- James Watt School of Engineering, Infrastructure and Environment Research Division, University of Glasgow, Rankine Building, Glasgow, G12 8LT, UK
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10
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Abbasi M, Aminian-Dehkordi J, Mousavi SM. A novel computational simulation approach to study biofilm significance in a packed-bed biooxidation reactor. CHEMOSPHERE 2021; 262:127680. [PMID: 32763572 DOI: 10.1016/j.chemosphere.2020.127680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/01/2020] [Accepted: 07/10/2020] [Indexed: 06/11/2023]
Abstract
Fe (II) biooxidation has recently gained significant interest. It plays a key role in a number of environmental and industrial processes such as bioleaching, acid mine drainage treatment, desulphurization of sour gases, and coal desulphurization. In this work, a three-dimensional CFD model for gas-liquid flow in a lab-scale packed-bed biooxidation reactor is used. The reactor is randomly packed with spherical particles, and the particles are covered with Leptospirillum ferrooxidans biofilm for Fe (II) biooxidation. A modified Jodrey-Tory algorithm is used to generate random packing with actual porosity of 0.42, and biofilm layer with constant thickness is considered over the particles. A simplified Eulerian-Eulerian model is used to obtain detailed flow field. The concentration profile in the reactor and the conversion of Fe (II) from the present simulations are obtained and validated using experimental data reported in the literature. The results of the study indicate that about three-quarters of the conversion occurs in the upper half of the reactor and Fe (II) concentration on the biofilm surface at the lower quarter of the reactor does not exceed 5 mM (The inlet concentration is 89.6 mM). The findings reveal that rate-limiting phenomena may vary in different parts of the reactor. The results obtained through the simulations represent advantages for the design and optimization of packed-bed biofilm reactors.
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Affiliation(s)
- Mohammad Abbasi
- Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran
| | - Javad Aminian-Dehkordi
- Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran
| | - Seyyed Mohammad Mousavi
- Biotechnology Group, Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran.
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11
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Aghajani Delavar M, Wang J. Modeling Combined Effects of Temperature and Structure on Competition and Growth of Multispecies Biofilms in Microbioreactors. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03102] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | - Junye Wang
- Faculty of Science and Technology, Athabasca University, Athabasca, Alberta T9S 3A3, Canada
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12
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Not Just Numbers: Mathematical Modelling and Its Contribution to Anaerobic Digestion Processes. Processes (Basel) 2020. [DOI: 10.3390/pr8080888] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Mathematical modelling of bioprocesses has a long and notable history, with eminent contributions from fields including microbiology, ecology, biophysics, chemistry, statistics, control theory and mathematical theory. This richness of ideas and breadth of concepts provide great motivation for inquisitive engineers and intrepid scientists to try their hand at modelling, and this collaboration of disciplines has also delivered significant milestones in the quality and application of models for both theoretical and practical interrogation of engineered biological systems. The focus of this review is the anaerobic digestion process, which, as a technology that has come in and out of fashion, remains a fundamental process for addressing the global climate emergency. Whether with conventional anaerobic digestion systems, biorefineries, or other anaerobic technologies, mathematical models are important tools that are used to design, monitor, control and optimise the process. Both highly structured, mechanistic models and data-driven approaches have been used extensively over half a decade, but recent advances in computational capacity, scientific understanding and diversity and quality of process data, presents an opportunity for the development of new modelling paradigms, augmentation of existing methods, or even incorporation of tools from other disciplines, to ensure that anaerobic digestion research can remain resilient and relevant in the face of emerging and future challenges.
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13
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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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14
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INDISIM-Denitrification, an individual-based model for study the denitrification process. J Ind Microbiol Biotechnol 2019; 47:1-20. [PMID: 31691030 DOI: 10.1007/s10295-019-02245-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 10/28/2019] [Indexed: 12/21/2022]
Abstract
Denitrification is one of the key processes of the global nitrogen (N) cycle driven by bacteria. It has been widely known for more than 100 years as a process by which the biogeochemical N-cycle is balanced. To study this process, we develop an individual-based model called INDISIM-Denitrification. The model embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM and is designed to simulate in aerobic and anaerobic conditions the cell growth kinetics of denitrifying bacteria. INDISIM-Denitrification simulates a bioreactor that contains a culture medium with succinate as a carbon source, ammonium as nitrogen source and various electron acceptors. To implement INDISIM-Denitrification, the individual-based model INDISIM was used to give sub-models for nutrient uptake, stirring and reproduction cycle. Using a thermodynamic approach, the denitrification pathway, cellular maintenance and individual mass degradation were modeled using microbial metabolic reactions. These equations are the basis of the sub-models for metabolic maintenance, individual mass synthesis and reducing internal cytotoxic products. The model was implemented in the open-access platform NetLogo. INDISIM-Denitrification is validated using a set of experimental data of two denitrifying bacteria in two different experimental conditions. This provides an interactive tool to study the denitrification process carried out by any denitrifying bacterium since INDISIM-Denitrification allows changes in the microbial empirical formula and in the energy-transfer-efficiency used to represent the metabolic pathways involved in the denitrification process. The simulator can be obtained from the authors on request.
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