1
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Dodia H, Mishra V, Nakrani P, Muddana C, Kedia A, Rana S, Sahasrabuddhe D, Wangikar PP. Dynamic flux balance analysis of high cell density fed-batch culture of Escherichia coli BL21 (DE3) with mass spectrometry-based spent media analysis. Biotechnol Bioeng 2024; 121:1394-1406. [PMID: 38214104 DOI: 10.1002/bit.28654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/08/2023] [Accepted: 12/29/2023] [Indexed: 01/13/2024]
Abstract
Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome-scale metabolic models (GSMM) and by assuming instantaneous pseudo-steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed-batch, high-cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri-carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.
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Affiliation(s)
- Hardik Dodia
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Vivek Mishra
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| | | | | | - Anant Kedia
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| | - Sneha Rana
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Deepti Sahasrabuddhe
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Pramod P Wangikar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
- Clarity Bio Systems India Pvt. Ltd., Pune, India
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2
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Kulakowski S, Banerjee D, Scown CD, Mukhopadhyay A. Improving microbial bioproduction under low-oxygen conditions. Curr Opin Biotechnol 2023; 84:103016. [PMID: 37924688 DOI: 10.1016/j.copbio.2023.103016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/17/2023] [Accepted: 10/07/2023] [Indexed: 11/06/2023]
Abstract
Microbial bioconversion provides access to a wide range of sustainably produced chemicals and commodities. However, industrial-scale bioproduction process operations are preferred to be anaerobic due to the cost associated with oxygen transfer. Anaerobic bioconversion generally offers limited substrate utilization profiles, lower product yields, and reduced final product diversity compared with aerobic processes. Bioproduction under conditions of reduced oxygen can overcome the limitations of fully aerobic and anaerobic bioprocesses, but many microbial hosts are not developed for low-oxygen bioproduction. Here, we describe advances in microbial strain engineering involving the use of redox cofactor engineering, genome-scale metabolic modeling, and functional genomics to enable improved bioproduction processes under low oxygen and provide a viable path for scaling these bioproduction systems to industrial scales.
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Affiliation(s)
- Shawn Kulakowski
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Corinne D Scown
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Energy Analysis and Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics & Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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3
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Kurpejović E, Wibberg D, Bastem GM, Burgardt A, Busche T, Kaya FEA, Dräger A, Wendisch VF, Akbulut BS. Can Genome Sequencing Coupled to Flux Balance Analyses Offer Precision Guidance for Industrial Strain Development? The Lessons from Carbon Trafficking in Corynebacterium glutamicum ATCC 21573. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:434-443. [PMID: 37707996 DOI: 10.1089/omi.2023.0098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Systems biology tools offer new prospects for industrial strain selection. For bacteria that are significant for industrial applications, whole-genome sequencing coupled to flux balance analysis (FBA) can help unpack the complex relationships between genome mutations and carbon trafficking. This work investigates the l-tyrosine (l-Tyr) overproducing model system Corynebacterium glutamicum ATCC 21573 with an eye to more rational and precision strain development. Using genome-wide mutational analysis of C. glutamicum, we identified 27,611 single nucleotide polymorphisms and 479 insertion/deletion mutations. Mutations in the carbon uptake machinery have led to phosphotransferase system-independent routes as corroborated with FBA. Mutations within the central carbon metabolism of C. glutamicum impaired the carbon flux, as evidenced by the lower growth rate. The entry to and flow through the tricarboxylic acid cycle was affected by mutations in pyruvate and α-ketoglutarate dehydrogenase complexes, citrate synthase, and isocitrate dehydrogenase. FBA indicated that the estimated flux through the shikimate pathway became larger as the l-Tyr production rate increased. In addition, protocatechuate export was probabilistically impossible, which could have contributed to the l-Tyr accumulation. Interestingly, aroG and cg0975, which have received previous attention for aromatic amino acid overproduction, were not mutated. From the branch point molecule, prephenate, the change in the promoter region of pheA could be an influential contributor. In summary, we suggest that genome sequencing coupled with FBA is well poised to offer rational guidance for industrial strain development, as evidenced by these findings on carbon trafficking in C. glutamicum ATCC 21573.
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Affiliation(s)
- Eldin Kurpejović
- Department of Bioengineering, Marmara University, Istanbul, Turkey
| | - Daniel Wibberg
- Genome Research of Industrial Microorganisms, Center for Biotechnology, Bielefeld University, Bielefeld, Germany
| | | | - Arthur Burgardt
- Genetics of Prokaryotes, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany
| | - Tobias Busche
- Technology Platform Genomics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany
- Medical School East Westphalia-Lippe, Bielefeld University, Bielefeld, Germany
| | - Fatma Ece Altinisik Kaya
- Department of Bioengineering, Marmara University, Istanbul, Turkey
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Andreas Dräger
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Volker F Wendisch
- Genetics of Prokaryotes, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany
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Gomez Romero S, Boyle N. Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction. Compr Rev Food Sci Food Saf 2023; 22:3422-3443. [PMID: 37306528 DOI: 10.1111/1541-4337.13193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/07/2023] [Accepted: 05/22/2023] [Indexed: 06/13/2023]
Abstract
The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.
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Affiliation(s)
- Sandra Gomez Romero
- Quantitative Biosciences and Engineering, Colorado School of Mines, Golden, Colorado, USA
| | - Nanette Boyle
- Chemical and Biological Engineering, Colorado School of Mines, Golden, Colorado, USA
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5
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Vikromvarasiri N, Noda S, Shirai T, Kondo A. Investigation of two metabolic engineering approaches for (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis. J Biol Eng 2023; 17:3. [PMID: 36627686 PMCID: PMC9830791 DOI: 10.1186/s13036-022-00320-w] [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: 09/01/2022] [Accepted: 12/24/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Flux Balance Analysis (FBA) is a well-known bioinformatics tool for metabolic engineering design. Previously, we have successfully used single-level FBA to design metabolic fluxes in Bacillus subtilis to enhance (R,R)-2,3-butanediol (2,3-BD) production from glycerol. OptKnock is another powerful technique for devising gene deletion strategies to maximize microbial growth coupling with improved biochemical production. It has never been used in B. subtilis. In this study, we aimed to compare the use of single-level FBA and OptKnock for designing enhanced 2,3-BD production from glycerol in B. subtilis. RESULTS Single-level FBA and OptKnock were used to design metabolic engineering approaches for B. subtilis to enhance 2,3-BD production from glycerol. Single-level FBA indicated that deletion of ackA, pta, lctE, and mmgA would improve the production of 2,3-BD from glycerol, while OptKnock simulation suggested the deletion of ackA, pta, mmgA, and zwf. Consequently, strains LM01 (single-level FBA-based) and MZ02 (OptKnock-based) were constructed, and their capacity to produce 2,3-BD from glycerol was investigated. The deletion of multiple genes did not negatively affect strain growth and glycerol utilization. The highest 2,3-BD production was detected in strain LM01. Strain MZ02 produced 2,3-BD at a similar level as the wild type, indicating that the OptKnock prediction was erroneous. Two-step FBA was performed to examine the reason for the erroneous OptKnock prediction. Interestingly, we newly found that zwf gene deletion in strain MZ02 improved lactate production, which has never been reported to date. The predictions of single-level FBA for strain MZ02 were in line with experimental findings. CONCLUSIONS We showed that single-level FBA is an effective approach for metabolic design and manipulation to enhance 2,3-BD production from glycerol in B. subtilis. Further, while this approach predicted the phenotypes of generated strains with high precision, OptKnock prediction was not accurate. We suggest that OptKnock modelling predictions be evaluated by using single-level FBA to ensure the accuracy of metabolic pathway design. Furthermore, the zwf gene knockout resulted in the change of metabolic fluxes to enhance the lactate productivity.
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Affiliation(s)
- Nunthaphan Vikromvarasiri
- grid.509461.f0000 0004 1757 8255RIKEN Center for Sustainable Resource Science, 1‑7‑22 Suehiro‑cho, Tsurumi‑ku, Yokohama, Kanagawa 230‑0045 Japan
| | - Shuhei Noda
- grid.509461.f0000 0004 1757 8255RIKEN Center for Sustainable Resource Science, 1‑7‑22 Suehiro‑cho, Tsurumi‑ku, Yokohama, Kanagawa 230‑0045 Japan
| | - Tomokazu Shirai
- grid.509461.f0000 0004 1757 8255RIKEN Center for Sustainable Resource Science, 1‑7‑22 Suehiro‑cho, Tsurumi‑ku, Yokohama, Kanagawa 230‑0045 Japan
| | - Akihiko Kondo
- grid.509461.f0000 0004 1757 8255RIKEN Center for Sustainable Resource Science, 1‑7‑22 Suehiro‑cho, Tsurumi‑ku, Yokohama, Kanagawa 230‑0045 Japan ,grid.31432.370000 0001 1092 3077Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada, Kobe, 657-8501 Japan
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6
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Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0. Processes (Basel) 2022. [DOI: 10.3390/pr10112226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.
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7
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Du YH, Wang MY, Yang LH, Tong LL, Guo DS, Ji XJ. Optimization and Scale-Up of Fermentation Processes Driven by Models. Bioengineering (Basel) 2022; 9:bioengineering9090473. [PMID: 36135019 PMCID: PMC9495923 DOI: 10.3390/bioengineering9090473] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.
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Affiliation(s)
- Yuan-Hang Du
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Min-Yu Wang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Lin-Hui Yang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Ling-Ling Tong
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Dong-Sheng Guo
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
- Correspondence: (D.-S.G.); (X.-J.J.)
| | - Xiao-Jun Ji
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
- Correspondence: (D.-S.G.); (X.-J.J.)
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8
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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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9
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Landon S, Chalkley O, Breese G, Grierson C, Marucci L. Understanding Metabolic Flux Behaviour in Whole-Cell Model Output. Front Mol Biosci 2021; 8:732079. [PMID: 34977150 PMCID: PMC8718694 DOI: 10.3389/fmolb.2021.732079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 10/28/2021] [Indexed: 11/30/2022] Open
Abstract
Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design.
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Affiliation(s)
- Sophie Landon
- BrisSynBio, University of Bristol, Bristol, United Kingdom
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Oliver Chalkley
- BrisSynBio, University of Bristol, Bristol, United Kingdom
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
- Bristol Centre for Complexity Science, Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Gus Breese
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Claire Grierson
- BrisSynBio, University of Bristol, Bristol, United Kingdom
- School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Lucia Marucci
- BrisSynBio, University of Bristol, Bristol, United Kingdom
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
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10
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Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics. Processes (Basel) 2021. [DOI: 10.3390/pr9101701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods produce useful predictions on pathway operations. In this review, we present both modelling techniques and we show how they bring complementary views of metabolism. In particular, we show on a simple example how both approaches can be used in conjunction to shed some light on the dynamics of metabolic networks.
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11
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Kuriya Y, Inoue M, Yamamoto M, Murata M, Araki M. Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering. Biotechnol J 2021; 16:e2000443. [PMID: 34516717 DOI: 10.1002/biot.202000443] [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: 11/10/2020] [Revised: 09/01/2021] [Accepted: 09/10/2021] [Indexed: 11/10/2022]
Abstract
Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.
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Affiliation(s)
- Yuki Kuriya
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
| | - Mai Inoue
- Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan
| | - Masaki Yamamoto
- Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan
| | - Masahiro Murata
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
| | - Michihiro Araki
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan.,Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan.,Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, Japan
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12
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He Y, Huang Y, Xu Z, Xie W, Luo Y, Li F, Zhu X, Shi X. Stereodivergent Syntheses of All Stereoisomers of (−)‐Shikimic Acid: Development of a Chiral Pool for the Diverse Polyhydroxy‐cyclohexenoid (or ‐cyclohexanoid) Bioactive Molecules. European J Org Chem 2021. [DOI: 10.1002/ejoc.202100653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Yun‐Gang He
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
| | - Yong‐Kang Huang
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
| | - Zhang‐Li Xu
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
| | - Wen‐Jing Xie
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
| | - Yong‐Qiang Luo
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
| | - Feng‐Lei Li
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
| | - Xing‐Liang Zhu
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
| | - Xiao‐Xin Shi
- Engineering Research Center of Pharmaceutical Process Chemistry of the Ministry of Education School of Pharmacy East China University of Science and Technology 130 Mei-Long Road Shanghai 200237 P. R. China
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Zhu XL, Luo YQ, Wang L, Huang YK, He YG, Xie WJ, Liu SL, Shi XX. Novel Stereoselective Syntheses of (+)-Streptol and (-)-1 -epi-Streptol Starting from Naturally Abundant (-)-Shikimic Acid. ACS OMEGA 2021; 6:17103-17112. [PMID: 34250367 PMCID: PMC8264934 DOI: 10.1021/acsomega.1c02502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
Novel highly stereoselective syntheses of (+)-streptol and (-)-1-epi-streptol starting from naturally abundant (-)-shikimic acid were described in this article. (-)-Shikimic acid was first converted to the common key intermediate by 11 steps in 40% yield. It was then converted to (+)-streptol by three steps in 72% yield, and it was also converted to (-)-1-epi-streptol by one step in 90% yield. In summary, (+)-streptol and (-)-1-epi-streptol were synthesized from (-)-shikimic acid by 14 and 12 steps in 29 and 36% overall yields, respectively.
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Affiliation(s)
- Xing-Liang Zhu
- Engineering
Research Center of Pharmaceutical Process Chemistry of the Ministry
of Education, School of Pharmacy, East China
University of Science and Technology, 130 Mei-Long Road, Shanghai 200237, P. R. China
| | - Yong-Qiang Luo
- Engineering
Research Center of Pharmaceutical Process Chemistry of the Ministry
of Education, School of Pharmacy, East China
University of Science and Technology, 130 Mei-Long Road, Shanghai 200237, P. R. China
| | - Lei Wang
- Engineering
Research Center of Pharmaceutical Process Chemistry of the Ministry
of Education, School of Pharmacy, East China
University of Science and Technology, 130 Mei-Long Road, Shanghai 200237, P. R. China
| | - Yong-Kang Huang
- Engineering
Research Center of Pharmaceutical Process Chemistry of the Ministry
of Education, School of Pharmacy, East China
University of Science and Technology, 130 Mei-Long Road, Shanghai 200237, P. R. China
| | - Yun-Gang He
- Engineering
Research Center of Pharmaceutical Process Chemistry of the Ministry
of Education, School of Pharmacy, East China
University of Science and Technology, 130 Mei-Long Road, Shanghai 200237, P. R. China
| | - Wen-Jing Xie
- Engineering
Research Center of Pharmaceutical Process Chemistry of the Ministry
of Education, School of Pharmacy, East China
University of Science and Technology, 130 Mei-Long Road, Shanghai 200237, P. R. China
| | - Shi-Ling Liu
- Zhejiang
Arthur Pharmaceutical Co. Ltd., 3556 Linggongtang Road, Jiake Life Science Park Building 3, Daqiao Town, Nanhu District, Jiaxing, Zhejiang 314000, P. R. China
| | - Xiao-Xin Shi
- Engineering
Research Center of Pharmaceutical Process Chemistry of the Ministry
of Education, School of Pharmacy, East China
University of Science and Technology, 130 Mei-Long Road, Shanghai 200237, P. R. China
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Antonakoudis A, Barbosa R, Kotidis P, Kontoravdi C. The era of big data: Genome-scale modelling meets machine learning. Comput Struct Biotechnol J 2020; 18:3287-3300. [PMID: 33240470 PMCID: PMC7663219 DOI: 10.1016/j.csbj.2020.10.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 12/15/2022] Open
Abstract
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
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Affiliation(s)
| | | | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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