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Sampara P, Lawson CE, Scarborough MJ, Ziels RM. Advancing environmental biotechnology with microbial community modeling rooted in functional 'omics. Curr Opin Biotechnol 2024; 88:103165. [PMID: 39033648 DOI: 10.1016/j.copbio.2024.103165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/21/2024] [Accepted: 06/04/2024] [Indexed: 07/23/2024]
Abstract
Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. 'Conventional' bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional 'omics-based approaches can provide a 'lens' through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional 'omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.
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Affiliation(s)
- Pranav Sampara
- Department of Civil Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Christopher E Lawson
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Matthew J Scarborough
- Department of Civil and Environmental Engineering, University of Vermont, Burlington, VT, United States
| | - Ryan M Ziels
- Department of Civil Engineering, The University of British Columbia, Vancouver, British Columbia, Canada.
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2
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [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: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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3
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Basile A, Zampieri G, Kovalovszki A, Karkaria B, Treu L, Patil KR, Campanaro S. Modelling of microbial interactions in anaerobic digestion: from black to glass box. Curr Opin Microbiol 2023; 75:102363. [PMID: 37542746 DOI: 10.1016/j.mib.2023.102363] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/20/2023] [Accepted: 07/10/2023] [Indexed: 08/07/2023]
Abstract
Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.
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Affiliation(s)
- Arianna Basile
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
| | - Guido Zampieri
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
| | - Adam Kovalovszki
- Department of Environmental and Resource Engineering, Technical University of Denmark, Building 115, Bygningstorvet, 2800 Kgs. Lyngby, Denmark
| | - Behzad Karkaria
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Laura Treu
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy.
| | - Kiran Raosaheb Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Stefano Campanaro
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
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Donoso-Bravo A, Sadino-Riquelme MC, Valdebenito-Rolack E, Paulet D, Gómez D, Hansen F. Comprehensive ADM1 Extensions to Tackle Some Operational and Metabolic Aspects in Anaerobic Digestion. Microorganisms 2022; 10:microorganisms10050948. [PMID: 35630393 PMCID: PMC9143495 DOI: 10.3390/microorganisms10050948] [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] [Received: 03/31/2022] [Revised: 04/25/2022] [Accepted: 04/28/2022] [Indexed: 02/04/2023] Open
Abstract
Modelling in anaerobic digestion will play a crucial role as a tool for smart monitoring and supervision of the process performance and stability. By far, the Anaerobic Digestion Model No. 1 (ADM1) has been the most recognized and exploited model to represent this process. This study aims to propose simple extensions for the ADM1 model to tackle some overlooked operational and metabolic aspects. Extensions for the discontinuous feeding process, the reduction of the active working volume, the transport of the soluble compound from the bulk to the cell interior, and biomass acclimation are presented in this study. The model extensions are included by a change in the mass balance of the process in batch and continuous operation, the incorporation of a transfer equation governed by the gradient between the extra- and intra- cellular concentration, and a saturation-type function where the time has an explicit influence on the kinetic parameters, respectively. By adding minimal complexity to the existing ADM1, the incorporation of these phenomena may help to understand some underlying process issues that remain unexplained by the current model structure, broadening the scope of the model for control and monitoring industrial applications.
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Affiliation(s)
- Andrés Donoso-Bravo
- ProCycla SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile; (M.C.S.-R.); (E.V.-R.); (D.P.); (D.G.); (F.H.)
- ProCycla SL, Carretera Pont de Vilomara 140, 2-1, 08241 Manresa, Spain
- Department of Chemical Engineering, Universidad Técnica Federico Santa Maria, Valparaíso 2390123, Chile
- Correspondence:
| | - María Constanza Sadino-Riquelme
- ProCycla SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile; (M.C.S.-R.); (E.V.-R.); (D.P.); (D.G.); (F.H.)
- ProCycla SL, Carretera Pont de Vilomara 140, 2-1, 08241 Manresa, Spain
| | - Emky Valdebenito-Rolack
- ProCycla SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile; (M.C.S.-R.); (E.V.-R.); (D.P.); (D.G.); (F.H.)
- ProCycla SL, Carretera Pont de Vilomara 140, 2-1, 08241 Manresa, Spain
- Aroma SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile
| | - David Paulet
- ProCycla SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile; (M.C.S.-R.); (E.V.-R.); (D.P.); (D.G.); (F.H.)
- ProCycla SL, Carretera Pont de Vilomara 140, 2-1, 08241 Manresa, Spain
| | - Daniel Gómez
- ProCycla SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile; (M.C.S.-R.); (E.V.-R.); (D.P.); (D.G.); (F.H.)
- ProCycla SL, Carretera Pont de Vilomara 140, 2-1, 08241 Manresa, Spain
| | - Felipe Hansen
- ProCycla SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile; (M.C.S.-R.); (E.V.-R.); (D.P.); (D.G.); (F.H.)
- ProCycla SL, Carretera Pont de Vilomara 140, 2-1, 08241 Manresa, Spain
- Aroma SpA, Camino Fundo El Junco SN, Melipilla 9580000, Chile
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Abstract
Cholesterol is an essential component of eukaryotic cellular membranes. It is also an important precursor for making other molecules needed by the body. Cholesterol homeostasis plays an essential role in human health. Having high cholesterol can increase the chances of getting heart disease. As a result of the risks associated with high cholesterol, it is imperative that studies are conducted to determine the best course of action to reduce whole body cholesterol levels. Mathematical models can provide direction on this. By examining existing models, the suitable reactions or processes for drug targeting to lower whole-body cholesterol can be determined. This paper examines existing models in the literature that, in total, cover most of the processes involving cholesterol metabolism and transport, including: the absorption of cholesterol in the intestine; the cholesterol biosynthesis in the liver; the storage and transport of cholesterol between the intestine, the liver, blood vessels, and peripheral cells. The findings presented in these models will be discussed for potential combination to form a comprehensive model of cholesterol within the entire body, which is then taken as an in-silico patient for identifying drug targets, screening drugs, and designing intervention strategies to regulate cholesterol levels in the human body.
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McDaniel EA, Wahl SA, Ishii S, Pinto A, Ziels R, Nielsen PH, McMahon KD, Williams RBH. Prospects for multi-omics in the microbial ecology of water engineering. WATER RESEARCH 2021; 205:117608. [PMID: 34555741 DOI: 10.1016/j.watres.2021.117608] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions - including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.
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Affiliation(s)
- Elizabeth A McDaniel
- Department of Bacteriology, University of Wisconsin - Madison, Madison, WI, USA.
| | | | - Shun'ichi Ishii
- Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Super-cutting-edge Grand and Advanced Research (SUGAR) Program, Institute for Extra-cutting-edge Science and Technology Avant-garde Research (X-star), Yokosuka 237-0061, Japan
| | - Ameet Pinto
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA
| | - Ryan Ziels
- Department of Civil Engineering, The University of British Columbia, Vancouver, BC, Canada
| | | | - Katherine D McMahon
- Department of Bacteriology, University of Wisconsin - Madison, Madison, WI, USA; Department of Civil and Environmental Engineering, University of Wisconsin - Madison, Madison, WI, USA
| | - Rohan B H Williams
- Singapore Centre for Environmental Life Sciences Engineering, National University of Singapore, Republic of Singapore.
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Momodu AS, Adepoju TD. System dynamics kinetic model for predicting biogas production in anaerobic condition: Preliminary assessment. Sci Prog 2021; 104:368504211042479. [PMID: 34605314 PMCID: PMC10450725 DOI: 10.1177/00368504211042479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION This preliminary assessment of a grey-box model, was predicated on system dynamics principles and developed using Vensim® DSS software. The purpose is to predict biogas production under anaerobic conditions for energy utilization at the design stage. OBJECTIVE To describe the process of a developed system dynamics model to predict biogas production under anaerobic conditions. METHODS This method involves two-stage kinetics of the biogas production process in anaerobic conditions using the first-order and Gompertz functions. The model is depicted in two parts: causal loop diagram and stock-flow diagram. The causal loop diagram describes the anaerobic digestion process a substrate undergoes for the production of biogas, while stock-flow diagram depicts basic building blocks of the dynamic behavior of an anaerobic digestion process. Primary data is from a laboratory-scale experiment of biogas production using vegetal wastes, while the secondary one is from the literature on studies using similar substrates. RESULTS Primary and secondary data are used to validate and stimulate the developed model. The kinetic model shows the substrate being reduced exponentially with increasing time; consumption of substrate and production of methane and carbon dioxide follows exponential growth and decay pattern, with carbon dioxide production starting early compared to methane, and was produced at a rate faster due to the strong and resilient characteristics of fermentative microorganisms. DISCUSSION Comparing data from empirical and model simulation shows some close relationship, though not too perfectly. Both results reflect signs of inhibitions occurring within the substrates in the digester under anaerobic conditions explaining the low methane yield or instability.
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Affiliation(s)
- Abiodun S Momodu
- Centre for Energy Research and Development, Obafemi Awolowo University, Nigeria
| | - Tofunmi D Adepoju
- Department of Chemical Engineering, Obafemi Awolowo University, Nigeria
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8
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Genome-centric investigation of anaerobic digestion using sustainable second and third generation substrates. J Biotechnol 2021; 339:53-64. [PMID: 34371053 DOI: 10.1016/j.jbiotec.2021.08.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/23/2021] [Accepted: 08/03/2021] [Indexed: 10/20/2022]
Abstract
Biogas production through co-digestion of second and third generation substrates is an environmentally sustainable approach. Green willow biomass, chicken manure waste and microalgae biomass substrates were combined in the anaerobic digestion experiments. Biochemical methane potential test showed that biogas yields of co-digestions were significantly higher compared to the yield when energy willow was the sole substrate. To scale up the experiment continuous stirred-tank reactors (CSRTs) are employed, digestion parameters are monitored. Furthermore, genome-centric metagenomics approach was employed to gain functional insight into the complex anaerobic decomposing process. This revealed the importance of Firmicutes, Actinobacteria, Proteobacteria and Bacteroidetes phyla as major bacterial participants, while Methanomicrobia and Methanobacteria represented the archaeal constituents of the communities. The bacterial phyla were shown to perform the carbohydrate hydrolysis. Among the representatives of long-chain carbohydrate hydrolysing microbes Bin_61: Clostridia is newly identified metagenome assembled genome (MAG) and Bin_13: DTU010 sp900018335 is common and abundant in all CSTRs. Methanogenesis was linked to the slow-growing members of the community, where hydrogenotrophic methanogen species Methanoculleus (Bin_10) and Methanobacterium (Bin_4) predominate. A sensitive balance between H2 producers and consumers was shown to be critical for stable biomethane production and efficient waste biodegradation.
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Dalby FR, Hafner SD, Petersen SO, VanderZaag AC, Habtewold J, Dunfield K, Chantigny MH, Sommer SG. Understanding methane emission from stored animal manure: A review to guide model development. JOURNAL OF ENVIRONMENTAL QUALITY 2021; 50:817-835. [PMID: 34021608 DOI: 10.1002/jeq2.20252] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 05/14/2021] [Indexed: 06/12/2023]
Abstract
National inventories of methane (CH4 ) emission from manure management are based on guidelines from the Intergovernmental Panel on Climate Change using country-specific emission factors. These calculations must be simple and, consequently, the effects of management practices and environmental conditions are only crudely represented in the calculations. The intention of this review is to develop a detailed understanding necessary for developing accurate models for calculating CH4 emission from liquid manure, with particular focus on the microbiological conversion of organic matter to CH4 . Themes discussed are (a) the liquid manure environment; (b) methane production processes from a modeling perspective; (c) development and adaptation of methanogenic communities; (d) mass and electron conservation; (e) steps limiting CH4 production; (f) inhibition of methanogens; (g) temperature effects on CH4 production; and (h) limits of existing estimation approaches. We conclude that a model must include calculation of microbial response to variations in manure temperature, substrate availability and age, and management system, because these variables substantially affect CH4 production. Methane production can be reduced by manipulating key variables through management procedures, and the effects may be taken into account by including a microbial component in the model. When developing new calculation procedures, it is important to include reasonably accurate algorithms of microbial adaptation. This review presents concepts for these calculations and ideas for how these may be carried out. A need for better quantification of hydrolysis kinetics is identified, and the importance of short- and long-term microbial adaptation is highlighted.
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Affiliation(s)
- Frederik R Dalby
- Dep. of Biological and Chemical Engineering, Aarhus Univ., Aarhus, 8200, Denmark
| | - Sasha D Hafner
- Dep. of Biological and Chemical Engineering, Aarhus Univ., Aarhus, 8200, Denmark
- Hafner Consulting LLC, Reston, VA, 20191, USA
| | | | - Andrew C VanderZaag
- Ottawa Research and Development Ctr., Agriculture and Agri-Food Canada, Ottawa, ON, K1A 0C6, Canada
| | - Jemaneh Habtewold
- Ottawa Research and Development Ctr., Agriculture and Agri-Food Canada, Ottawa, ON, K1A 0C6, Canada
| | - Kari Dunfield
- School of Environmental Science, Univ. of Guelph, Guelph, ON, N1G 2W1, Canada
| | - Martin H Chantigny
- Quebec Research and Development Ctr., Agriculture and Agri-Food Canada, Quebec, QC, G1V 2J3, Canada
| | - Sven G Sommer
- Dep. of Biological and Chemical Engineering, Aarhus Univ., Aarhus, 8200, Denmark
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Hashemi S, Hashemi SE, Lien KM, Lamb JJ. Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production. Microorganisms 2021; 9:microorganisms9061162. [PMID: 34071282 PMCID: PMC8226781 DOI: 10.3390/microorganisms9061162] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/17/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022] Open
Abstract
The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.
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Affiliation(s)
- Seyedbehnam Hashemi
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
| | - Sayed Ebrahim Hashemi
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
| | - Kristian M. Lien
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
| | - Jacob J. Lamb
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
- Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
- Correspondence:
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11
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Borer B, Or D. Spatiotemporal metabolic modeling of bacterial life in complex habitats. Curr Opin Biotechnol 2021; 67:65-71. [PMID: 33493977 DOI: 10.1016/j.copbio.2021.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 12/21/2020] [Accepted: 01/07/2021] [Indexed: 01/04/2023]
Abstract
The combination of genome-scale metabolic networks with spatially explicit representation of microbial habitats (spatiotemporal metabolic network modeling) paves the way to predict complex metabolic landscapes to a hitherto unparalleled detail, thus providing new insights into trophic interactions occurring at different scales. Placing detailed bacterial metabolism in realistic physical environment highlights the roles of physical barriers and diffusional bottlenecks on bacterial community interactions, structure and stability. We review recent advances in spatiotemporal metabolic network modeling using a few illustrative examples that highlight the immense potential of these novel approaches to interpret and design metabolic mediated interactions in structures (natural and engineered) environments.
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Affiliation(s)
- Benedict Borer
- Department of Environmental Systems Science, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland; The Department for Earth, Atmospheric and Planetary Science, MIT, Boston, MA, USA.
| | - Dani Or
- Department of Environmental Systems Science, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland; Div. of Hydrologic Sciences, Desert Research Institute, Reno, NV, USA
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12
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Basile A, Campanaro S, Kovalovszki A, Zampieri G, Rossi A, Angelidaki I, Valle G, Treu L. Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis. Metab Eng 2020; 62:138-149. [PMID: 32905861 DOI: 10.1016/j.ymben.2020.08.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/03/2020] [Accepted: 08/24/2020] [Indexed: 10/23/2022]
Abstract
Anaerobic digestion is a key biological process for renewable energy, yet the mechanistic knowledge on its hidden microbial dynamics is still limited. The present work charted the interaction network in the anaerobic digestion microbiome via the full characterization of pairwise interactions and the associated metabolite exchanges. To this goal, a novel collection of 836 genome-scale metabolic models was built to represent the functional capabilities of bacteria and archaea species derived from genome-centric metagenomics. Dominant microbes were shown to prefer mutualistic, parasitic and commensalistic interactions over neutralism, amensalism and competition, and are more likely to behave as metabolite importers and profiteers of the coexistence. Additionally, external hydrogen injection positively influences microbiome dynamics by promoting commensalism over amensalism. Finally, exchanges of glucogenic amino acids were shown to overcome auxotrophies caused by an incomplete tricarboxylic acid cycle. Our novel strategy predicted the most favourable growth conditions for the microbes, overall suggesting strategies to increasing the biogas production efficiency. In principle, this approach could also be applied to microbial populations of biomedical importance, such as the gut microbiome, to allow a broad inspection of the microbial interplays.
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Affiliation(s)
- Arianna Basile
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121, Padua, Italy
| | - Stefano Campanaro
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121, Padua, Italy; CRIBI Biotechnology Center, University of Padova, 35131, Padua, Italy.
| | - Adam Kovalovszki
- Department of Environmental Engineering, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Guido Zampieri
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121, Padua, Italy; Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Alessandro Rossi
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121, Padua, Italy
| | - Irini Angelidaki
- Department of Environmental Engineering, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Giorgio Valle
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121, Padua, Italy
| | - Laura Treu
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121, Padua, Italy
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13
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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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Zamorano-López N, Borrás L, Seco A, Aguado D. Unveiling microbial structures during raw microalgae digestion and co-digestion with primary sludge to produce biogas using semi-continuous AnMBR systems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 699:134365. [PMID: 31677459 DOI: 10.1016/j.scitotenv.2019.134365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 09/07/2019] [Accepted: 09/07/2019] [Indexed: 06/10/2023]
Abstract
Methane production from microalgae can be enhanced through anaerobic co-digestion with carbon-rich substrates and thus mitigate the inhibition risk associated with its low C:N ratio. Acclimated microbial communities for microalgae disruption can be used as a source of natural enzymes in bioenergy production. However, co-substrates with a certain microbial diversity such as primary sludge might shift the microbial structure. Substrates were generated in a Water Resource Recovery Facility (WRRF) and combined as follows: Scenedesmus or Chlorella digestion and microalgae co-digestion with primary sludge. The study was performed using two lab-scale Anaerobic Membrane Bioreactors (AnMBR). During three years, different feedstocks scenarios for methane production were evaluated with a special focus on the microbial diversity of the AnMBR. 57% of the population was shared between the different feedstock scenarios, revealing the importance of Anaerolineaceae members besides Smithella and Methanosaeta genera. The addition of primary sludge enhanced the microbial diversity of the system during both Chlorella and Scenedesmus co-digestion and promoted different microbial structures. Aceticlastic methanogen Methanosaeta was dominant in all the feedstock scenarios. A more remarkable role of syntrophic fatty acid degraders (Smithella, Syntrophobacteraceae) was observed during co-digestion when only microalgae were digested. However, no significant changes were observed in the microbial composition during anaerobic microalgae digestion when feeding only Chlorella or Scenedesmus. This is the first work revealing the composition of complex communities for semi-continuous bioenergy production from WRRF streams. The stability and maintenance of a microbial core over-time in semi-continuous AnMBRs is here shown supporting their future application in full-scale systems for raw microalgae digestion or co-digestion.
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Affiliation(s)
- N Zamorano-López
- CALAGUA - Unidad Mixta UV-UPV, Departament d'Enginyeria Química, Universitat de València, Avinguda de la Universitat s/n, 46100 Burjassot, Valencia, Spain.
| | - L Borrás
- CALAGUA - Unidad Mixta UV-UPV, Departament d'Enginyeria Química, Universitat de València, Avinguda de la Universitat s/n, 46100 Burjassot, Valencia, Spain.
| | - A Seco
- CALAGUA - Unidad Mixta UV-UPV, Departament d'Enginyeria Química, Universitat de València, Avinguda de la Universitat s/n, 46100 Burjassot, Valencia, Spain.
| | - D Aguado
- CALAGUA - Unidad Mixta UV-UPV, Institut Universitari d'Investigació d'Enginyeria de l'Aigua i Medi Ambient - IIAMA, Universitat Politècnica de Valencia, Camí de Vera s/n, 46022, Valencia, Spain.
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Abstract
The microbiome residing in anaerobic digesters drives the anaerobic digestion (AD) process to convert various feedstocks to biogas as a renewable source of energy. This microbiome has been investigated in numerous studies in the last century. The early studies used cultivation-based methods and analysis to identify the four guilds (or functional groups) of microorganisms. Molecular biology techniques overcame the limitations of cultivation-based methods and allowed the identification of unculturable microorganisms, revealing the high diversity of microorganisms involved in AD. In the past decade, omics technologies, including metataxonomics, metagenomics, metatranscriptomics, metaproteomics, and metametabolomics, have been or start to be used in comprehensive analysis and studies of biogas-producing microbiomes. In this chapter, we reviewed the utilities and limitations of these analysis methods, techniques, and technologies when they were used in studies of biogas-producing microbiomes, as well as the new information on diversity, composition, metabolism, and syntrophic interactions of biogas-producing microbiomes. We also discussed the current knowledge gaps and the research needed to further improve AD efficiency and stability.
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