1
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Li CT, Eng R, Zuniga C, Huang KW, Chen Y, Zengler K, Betenbaugh MJ. Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control. NPJ Syst Biol Appl 2023; 9:7. [PMID: 36922521 PMCID: PMC10017758 DOI: 10.1038/s41540-022-00260-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/08/2022] [Indexed: 03/18/2023] Open
Abstract
Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris. This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.
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Affiliation(s)
- Chien-Ting Li
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Richard Eng
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.,Department of Biology, San Diego State University, San Diego, USA
| | - Kai-Wen Huang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Yiqun Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.,Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0412, USA.,Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0436, USA
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.
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2
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Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, Anastasiou D. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience 2023; 26:106040. [PMID: 36844450 PMCID: PMC9947310 DOI: 10.1016/j.isci.2023.106040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/08/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.
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Affiliation(s)
- Frederick Clasen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Patrícia M. Nunes
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Nourdine Bah
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Dimitrios Anastasiou
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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3
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Sangtani R, Nogueira R, Yadav AK, Kiran B. Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment. JOURNAL OF POLYMERS AND THE ENVIRONMENT 2023; 31:2741-2760. [PMID: 36811096 PMCID: PMC9933833 DOI: 10.1007/s10924-023-02787-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/30/2023] [Indexed: 06/12/2023]
Abstract
The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.
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Affiliation(s)
- Rimjhim Sangtani
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, 453552, Indore, India
| | - Regina Nogueira
- Institute for Sanitary Engineering and Waste Management, Leibniz Universität Hannover, Hannover, Germany
| | - Asheesh Kumar Yadav
- CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, Odisha 751013 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002 India
| | - Bala Kiran
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, 453552, Indore, India
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4
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Inwongwan S, Pekkoh J, Pumas C, Sattayawat P. Metabolic network reconstruction of Euglena gracilis: Current state, challenges, and applications. Front Microbiol 2023; 14:1143770. [PMID: 36937274 PMCID: PMC10018167 DOI: 10.3389/fmicb.2023.1143770] [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: 01/13/2023] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.
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Affiliation(s)
- Sahutchai Inwongwan
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Jeeraporn Pekkoh
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Chayakorn Pumas
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Pachara Sattayawat
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai, Thailand
- *Correspondence: Pachara Sattayawat,
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5
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Ruffing AM, Davis RW, Lane TW. Advances in engineering algae for biofuel production. Curr Opin Biotechnol 2022; 78:102830. [PMID: 36332347 DOI: 10.1016/j.copbio.2022.102830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 12/14/2022]
Abstract
While algae demonstrate potential as a sustainable fuel source, low productivities limit the economic realization of algal biofuels. High-throughput strain engineering, omics-informed genome-scale modeling, and microbiome engineering are key technologies for enabling algal biofuels. High-throughput strain engineering efforts generate improved traits, including high biomass productivity and lipid content, in diverse algal species. Genome-scale models, constructed with the aid of omics data, provide insight into metabolic limitations and guide rational algal strain engineering efforts. As outdoor cultivation systems introduce exogenous organisms, microbiome engineering seeks to eliminate harmful organisms and introduce beneficial species. Optimizing algal biomass production and lipid content using these technologies may overcome the productivity barrier for the commercialization of algal biofuels.
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Affiliation(s)
- Anne M Ruffing
- Sandia National Laboratories, Molecular and Microbiology, P.O. Box 5800, MS 1413, Albuquerque, NM 87185, USA.
| | - Ryan W Davis
- Sandia National Laboratories, Bioresource and Environmental Security, P.O. Box 969, MS 9292, Livermore, CA 94551, USA
| | - Todd W Lane
- Sandia National Laboratories, Bioresource and Environmental Security, P.O. Box 969, MS 9292, Livermore, CA 94551, USA
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6
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Broddrick JT, Ware MA, Jallet D, Palsson BO, Peers G. Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:603-621. [PMID: 36053127 PMCID: PMC9826171 DOI: 10.1111/tpj.15965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 08/25/2022] [Accepted: 08/31/2022] [Indexed: 06/01/2023]
Abstract
Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome-scale metabolic models capture species-specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non-photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non-radiative energy dissipation processes, cross-compartment electron shuttling, and non-photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico. Overall, we present a methodology for incorporating a common, informative data type into computational models of light-driven metabolism and show its utilization within the design-build-test-learn cycle for engineering of photosynthetic organisms.
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Affiliation(s)
- Jared T. Broddrick
- Division of Biological SciencesUniversity of California, San DiegoLa JollaCA92093USA
- Department of BioengineeringUniversity of California, San DiegoLa JollaCA92093USA
- Space Biosciences Research BranchNASA Ames Research CenterMoffett FieldCA94035USA
| | - Maxwell A. Ware
- Department of BiologyColorado State UniversityFort CollinsCO80524USA
| | - Denis Jallet
- Department of BiologyColorado State UniversityFort CollinsCO80524USA
| | - Bernhard O. Palsson
- Department of BioengineeringUniversity of California, San DiegoLa JollaCA92093USA
| | - Graham Peers
- Department of BiologyColorado State UniversityFort CollinsCO80524USA
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7
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Briones-Baez MF, Aguilera-Vazquez L, Rangel-Valdez N, Martinez-Salazar AL, Zuñiga C. Multi-Objective Optimization of Microalgae Metabolism: An Evolutive Algorithm Based on FBA. Metabolites 2022; 12:metabo12070603. [PMID: 35888727 PMCID: PMC9325016 DOI: 10.3390/metabo12070603] [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: 05/21/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 12/07/2022] Open
Abstract
Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.
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Affiliation(s)
- Monica Fabiola Briones-Baez
- TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico; (M.F.B.-B.); (L.A.-V.)
| | - Luciano Aguilera-Vazquez
- TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico; (M.F.B.-B.); (L.A.-V.)
| | - Nelson Rangel-Valdez
- CONACyT—TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico;
| | - Ana Lidia Martinez-Salazar
- TECNM/Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Los Mangos 89440, Mexico; (M.F.B.-B.); (L.A.-V.)
- Correspondence:
| | - Cristal Zuñiga
- Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA;
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8
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Joshi S, Mishra S. Recent advances in biofuel production through metabolic engineering. BIORESOURCE TECHNOLOGY 2022; 352:127037. [PMID: 35318143 DOI: 10.1016/j.biortech.2022.127037] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/15/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
Rising global energy demands and climate crisis has created an unprecedented need for the bio-based circular economy to ensure sustainable development with the minimized carbon footprint. Along with conventional biofuels such as ethanol, microbes can be used to produce advanced biofuels which are equivalent to traditional fuels in their energy efficiencies and are compatible with already established infrastructure and hence can be directly blended in higher proportions without overhauling of the pre-existing setup. Metabolic engineering is at the frontiers to develop microbial chassis for biofuel bio-foundries to meet the industrial needs for clean energy. This review does a thorough inquiry of recent developments in metabolic engineering for increasing titers, rates, and yields (TRY) of biofuel production by engineered microorganisms.
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Affiliation(s)
- Swati Joshi
- ICMR-National Institute of Occupational Health (NIOH), Ahmedabad, Gujarat, India; Central University of Gujarat, Gandhinagar, Gujarat, India.
| | - SukhDev Mishra
- ICMR-National Institute of Occupational Health (NIOH), Ahmedabad, Gujarat, India
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9
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Farooq W. Maximizing Energy Content and CO 2 Bio-fixation Efficiency of an Indigenous Isolated Microalga Parachlorella kessleri HY-6 Through Nutrient Optimization and Water Recycling During Cultivation. Front Bioeng Biotechnol 2022; 9:804608. [PMID: 35223814 PMCID: PMC8867024 DOI: 10.3389/fbioe.2021.804608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/29/2021] [Indexed: 11/25/2022] Open
Abstract
An alternative source of energy and materials with low negative environmental impacts is essential for a sustainable future. Microalgae is a promising candidate in this aspect. The focus of this study is to optimize the supply of nitrogen and carbon dioxide during the cultivation of locally isolated strain Parachlorella kessleri HY-6. This study focuses on optimizing nitrogen and CO2 supply based on total biomass and biomass per unit amount of nitrogen and CO2. Total biomass increased from 1.23 to 2.30 g/L with an increase in nitrogen concentration from 15.8 to 47.4 mg/L. However, biomass per unit amount of nitrogen supplied was higher at low nitrogen content. Biomass and CO2 fixation rate increased at higher CO2 concentrations in bubbling air, but CO2 fixation efficiency decreased drastically. Finally, the energy content of biomass increased with increases in both nitrogen and CO2 supply. This work thoroughly analyzed the biomass composition via ultimate, proximate, and biochemical analysis. Water is recycled three times for cultivation at three different nitrogen levels. Microalgae biomass increased during the second recycling and then decreased drastically during the third. Activated carbon helped remove the organics after the third recycling to improve the water recyclability. This study highlights the importance of selecting appropriate variables for optimization by considering net energy investment in terms of nutrients (as nitrogen) and CO2 fixation efficiency and effective water recycling.
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Affiliation(s)
- Wasif Farooq
- Chemical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.,Integrated Research Centre for Membranes and Water Security (IRC-MWS ), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
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10
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Passi A, Tibocha-Bonilla JD, Kumar M, Tec-Campos D, Zengler K, Zuniga C. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites 2021; 12:14. [PMID: 35050136 PMCID: PMC8778254 DOI: 10.3390/metabo12010014] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022] Open
Abstract
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.
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Affiliation(s)
- Anurag Passi
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA;
| | - Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Diego Tec-Campos
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Facultad de Ingeniería Química, Campus de Ciencias Exactas e Ingenierías, Universidad Autónoma de Yucatán, Merida 97203, Yucatan, Mexico
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0403, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
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11
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Chen Y, Liu X, Anderson JYL, Naik HM, Dhara VG, Chen X, Harris GA, Betenbaugh MJ. A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures. Biotechnol Bioeng 2021; 119:435-451. [PMID: 34811743 DOI: 10.1002/bit.27994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/26/2021] [Accepted: 11/13/2021] [Indexed: 11/09/2022]
Abstract
Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.
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Affiliation(s)
- Yiqun Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xiao Liu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Harnish Mukesh Naik
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Venkata Gayatri Dhara
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xiaolu Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Glenn A Harris
- Research and Development, 908 Devices Inc., Boston, Massachusetts, USA
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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12
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Kinetic, metabolic, and statistical analytics: addressing metabolic transport limitations among organelles and microbial communities. Curr Opin Biotechnol 2021; 71:91-97. [PMID: 34293631 DOI: 10.1016/j.copbio.2021.06.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 05/24/2021] [Accepted: 06/28/2021] [Indexed: 11/23/2022]
Abstract
Microbial organisms engage in a variety of metabolic interactions. A crucial part of these interactions is the exchange of molecules between different organelles, cells, and the environment. The main forces mediating this metabolic exchange are transporters. This transport can be difficult to measure experimentally because several transport mechanisms remain opaque. However, theoretical calculations about the inputs and outputs of cells via metabolic exchanges have enabled the successful inference of the workings of intra-organismal and inter-organismal systems. Kinetic, metabolic, and statistical modeling approaches in combination with omics data are enhancing our knowledge and understanding about metabolic exchange and mass resource allocation. This model-driven analytics approach can guide effective experimental design and yield new insights into biological function and control.
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Zuñiga C, Peacock B, Liang B, McCollum G, Irigoyen SC, Tec-Campos D, Marotz C, Weng NC, Zepeda A, Vidalakis G, Mandadi KK, Borneman J, Zengler K. Linking metabolic phenotypes to pathogenic traits among "Candidatus Liberibacter asiaticus" and its hosts. NPJ Syst Biol Appl 2020; 6:24. [PMID: 32753656 PMCID: PMC7403731 DOI: 10.1038/s41540-020-00142-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 06/18/2020] [Indexed: 12/21/2022] Open
Abstract
Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.
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Affiliation(s)
- Cristal Zuñiga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA
| | - Beth Peacock
- Department of Microbiology and Plant Pathology, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA
| | - Bo Liang
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA
- State Key Laboratory of Bioreactor Engineering and Institute of Applied Chemistry, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Greg McCollum
- USDA, ARS, US Horticultural Research Laboratory, 2001 S. Rock Road, Fort Pierce, FL, 34945, USA
| | - Sonia C Irigoyen
- Texas A&M AgriLife Research and Extension Center, Texas A&M University System, Weslaco, TX, USA
| | - Diego Tec-Campos
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Campus de Ciencias Exactas e Ingenierías, Mérida, 97203, Yucatán, México
| | - Clarisse Marotz
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA
| | - Nien-Chen Weng
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA
| | - Alejandro Zepeda
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Campus de Ciencias Exactas e Ingenierías, Mérida, 97203, Yucatán, México
| | - Georgios Vidalakis
- Department of Microbiology and Plant Pathology, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA
| | - Kranthi K Mandadi
- Texas A&M AgriLife Research and Extension Center, Texas A&M University System, Weslaco, TX, USA
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA
| | - James Borneman
- Department of Microbiology and Plant Pathology, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA.
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA.
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0403, USA.
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