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Systematically gap-filling the genome-scale metabolic model of CHO cells

BIOTECHNOLOGY LETTERS 2021年第1期43卷 73-87页

作者： Fouladiha, Hamideh Marashi, Sayed-Amir Li, Shangzhong Li, Zerong Masson, Helen O. Vaziri, Behrouz Lewis, Nathan E. Univ Tehran Coll Sci Dept Biotechnol Tehran Iran Univ Tehran Dept Biotechnol Enghelab Ave Tehran *** Iran Univ Calif San Diego Dept Bioengn San Diego CA 92103 USA Univ Calif San Diego Novo Nordisk Fdn Ctr Biosustainabil San Diego CA 92103 USA Univ Calif San Diego Dept Pediat San Diego CA 92103 USA Pasteur Inst Iran Biotechnol Res Ctr Prot Chem & Prote Lab Tehran Iran

Objective Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update toiCHO1766, the metabolic model of CHO cells. Results We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, namediCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. Conclusions The present CHO model is an important step towards more complete metabolic models of CHO cells.

关键词： CHO cells constraint-based modeling Gap-filling Metabolic network models Systems biology

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Genome-scale model of Rothia mucilaginosa predicts gene essentialities and reveals metabolic capabilities

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MICROBIOLOGY SPECTRUM 2024年第6期12卷 e0400623页

作者： Leonidou, Nantia Ostyn, Lisa Coenye, Tom Crabbe, Aurelie Draeger, Andreas Eberhard Karl Univ Tubingen Inst Bioinformat & Med Informat IBMI Computat Syst Biol Infect & Antimicrobial Resistan Tubingen Germany Eberhard Karl Univ Tubingen Dept Comp Sci Tubingen Germany Eberhard Karl Univ Tubingen Cluster Excellence Controlling Microbes Fight Infe Tubingen Germany German Ctr Infect Res DZIF Partner Site Tubingen Tubingen Germany Eberhard Karl Univ Tubingen Quantitat Biol Ctr QBiC Tubingen Germany Univ Ghent Lab Pharmaceut Microbiol LPM Ghent Belgium Martin Luther Univ Halle Wittenberg Inst Comp Sci Data Analyt & Bioinformat Halle Germany

Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model's effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa's metabolism for desired performance.

关键词： iRM23NL Rothia mucilaginosa DSM20746 ATCC 25296 constraint-based modeling flux balance analysis flux variability analysis mathematical network genome-scale metabolic model metabolic engineering pathway analysis SBML Gram-positive nasal microbiome lung infections cystic fibrosis antimicrobial strategies

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A review of advances in integrating gene regulatory networks and metabolic networks for designing strain optimization

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第6期36卷

作者： Ananda, Ridho Daud, Kauthar Mohd Zainudin, Suhaila Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia

Strain optimization aims to overproduce valuable metabolites by leveraging an understanding of biological systems, including metabolic networks and gene regulatory networks (GRNs). Accordingly, researchers proposed integrating metabolic networks and GRNs to be analyzed simultaneously. The proposed algorithms from 2002 to 2021 were rFBA, SR-FBA, iFBA, PROM, PROM2.0, TIGER, BeReTa, CoRegFlux, IDREAM, TRFBA, OptRAM, TRIMER, and PRIME. Each algorithm has different characteristics. Thus, using the appropriate algorithm for designing strain optimization is essential. Therefore, a critical review was conducted by synthesizing and analyzing the existing algorithms. Five aspects are discussed in this review: the strategic approaches, model of GRNs, source of GRNs, optimization, supplementary methods, and the programming language used. based on the review, several algorithms were better at modeling integrated regulatory-metabolic networks with high confidence, i.e., PROM, PROM2.0, and TRFBA. A simulation was applied to six strains. The results show that PROM2.0 best predicted the production rate and time complexity. However, the model is heavily influenced by the quality and quantity of the gene expression data. In addition, there are inconsistencies between GRNs and the gene expression data. Thus, this review also discussed future work based on GRNs and gene expression data.

关键词： In silico metabolic engineering Metabolic networks Gene regulatory networks constraint-based modeling Strain optimization

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Adaptations in metabolism and protein translation give rise to the Crabtree effect in yeast

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2021年第51期118卷 e2112836118-e2112836118页

作者： Malina, Carl Yu, Rosemary Bjorkeroth, Johan Kerkhoven, Eduard J. Nielsen, Jens Chalmers Univ Technol Dept Biol & Biol Engn SE-41296 Gothenburg Sweden Chalmers Univ Technol Wallenberg Ctr Prot Res SE-41296 Gothenburg Sweden Chalmers Univ Technol Novo Nordisk Fdn Ctr Biosustainabil SE-41296 Gothenburg Sweden Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark BioInnovat Inst DK-2200 Copenhagen N Denmark

Aerobic fermentation, also referred to as the Crabtree effect in yeast, is a well-studied phenomenon that allows many eukaryal cells to attain higher growth rates at high glucose availability. Not all yeasts exhibit the Crabtree effect, and it is not known why Crabtree-negative yeasts can grow at rates comparable to Crabtree-positive yeasts. Here, we quantitatively compared two Crabtree-positive yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and two Crabtree-negative yeasts, Kluyveromyces marxianus and Scheffersomyces stipitis, cultivated under glucose excess conditions. Combining physiological and proteome quantification with genome-scale metabolic modeling, we found that the two groups differ in energy metabolism and translation efficiency. In Crabtree-positive yeasts, the central carbon metabolism flux and proteome allocation favor a glucose utilization strategy minimizing proteome cost as proteins translation parameters, including ribosomal content and/or efficiency, are lower. Crabtree-negative yeasts, however, use a strategy of maximizing ATP yield, accompanied by higher protein translation parameters. Our analyses provide insight into the underlying reasons for the Crabtree effect, demonstrating a coupling to adaptations in both metabolism and protein translation.

关键词： Crabtree effect yeast systems biology proteomics constraint-based modeling

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Co-feeding enhances the yield of methyl ketones

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JOURNAL OF INDUSTRIAL MICROBIOLOGY & BIOTECHNOLOGY 2023年第1期50卷 kuad029页

作者： Ziegler, Anita L. Gruetering, Carolin Poduschnick, Leon Mitsos, Alexander Blank, Lars M. Rhein Westfal TH Aachen Proc Syst Engn AVT SVT Forckenbeckstr 51 D-52074 Aachen Germany Rhein Westfal TH Aachen Inst Appl Microbiol iAMB D-52074 Aachen Germany Forschungszentrum Julich GmbH Bioecon Sci Ctr BioSC D-52428 Julich Germany JARA ENERGY D-52056 Aachen Germany Forschungszentrum Julich GmbH Inst Energy & Climate Res Energy Syst Engn IEK 10 D-52425 Julich Germany

The biotechnological production of methyl ketones is a sustainable alternative to fossil-derived chemical production. To date, the best host for microbial production of methyl ketones is a genetically engineered Pseudomonas taiwanensis VLB120 increment 6 pProd strain, achieving yields of 101 mgg-1 on glucose in batch cultivations. For competitiveness with the petrochemical production pathway, however, higher yields are necessary. Co-feeding can improve the yield by fitting the carbon-to-energy ratio to the organism and the target product. In this work, we developed co-feeding strategies for P. taiwanensis VLB120 increment 6 pProd by combined metabolic modeling and experimental work. In a first step, we conducted flux balance analysis with an expanded genome-scale metabolic model of iJN1463 and found ethanol as the most promising among five cosubstrates. Next, we performed cultivations with ethanol and found the highest reported yield in batch production of methyl ketones with P. taiwanensis VLB120 to date, namely, 154 mg g-1 methyl ketones. However, ethanol is toxic to the cell, which reflects in a lower substrate consumption and lower product concentrations when compared to production on glucose. Hence, we propose cofeeding ethanol with glucose and find that, indeed, higher concentrations than in ethanol-fed cultivation (0.84 g Laq-1 with glucose and ethanol as opposed to 0.48 g Laq-1 with only ethanol) were achieved, with a yield of 85 mg g-1. In a last step, comparing experimental with computational results suggested the potential for improving the methyl ketone yield by fed-batch cultivation, in which cell growth and methyl ketone production are separated into two phases employing optimal ethanol to glucose ***-Sentence Summary By combining computational and experimental work, we demonstrate that feeding ethanol in addition to glucose improves the yield of biotechnologically produced methyl ketones. Graphical Abstract Workflow for in silico and experiment

关键词： Methyl ketones Co-feeding Pseudomonas constraint-based modeling

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Novel synthetic co-culture of Acetobacterium woodii and Clostridium drakei using CO2 and in situ generated H2 for the production of caproic acid via lactic acid

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ENGINEERING IN LIFE SCIENCES 2023年第1期23卷 e2100169页

作者： Herzog, Jan Mook, Alexander Guhl, Lotta Baumler, Miriam Beck, Matthias H. Weuster-Botz, Dirk Bengelsdorf, Frank R. Zeng, An-Ping Hamburg Univ Technol Inst Bioproc & Biosyst Engn Denickestr 15 D-21073 Hamburg Germany Ulm Univ Inst Microbiol & Biotechnol Ulm Germany Tech Univ Munich TUM Sch Engn & Design Dept Energy & Proc Engn Chair Biochem Engn Garching Germany

Acetobacterium woodii is known to produce mainly acetate from CO2 and H-2, but the production of higher value chemicals is desired for the bioeconomy. Using chain-elongating bacteria, synthetic co-cultures have the potential to produce longer-chained products such as caproic acid. In this study, we present first results for a successful autotrophic co-cultivation of A. woodii mutants and a Clostridium drakei wild-type strain in a stirred-tank bioreactor for the production of caproic acid from CO2 and H-2 via the intermediate lactic acid. For autotrophic lactate production, a recombinant A. woodii strain with a deleted Lct-dehydrogenase complex, which is encoded by the lctBCD genes, and an inserted D-lactate dehydrogenase (LdhD) originating from Leuconostoc mesenteroides, was used. Hydrogen for the process was supplied using an All-in-One electrode for in situ water electrolysis. Lactate concentrations as high as 0.5 g L-1 were achieved with the AiO-electrode, whereas 8.1 g L-1 lactate were produced with direct H-2 sparging in a stirred-tank bioreactor. Hydrogen limitation was identified in the AiO process. However, with cathode surface area enlargement or numbering-up of the electrode and on-demand hydrogen generation, this process has great potential for a true carbon-negative production of value chemicals from CO2.

关键词： bioelectrochemical system carbon fixation cell-cell interaction constraint-based modeling in situ electrolysis

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Global Sensitivity Analysis of constraint-based Metabolic Models 15th

Global Sensitivity Analysis of Constraint-Based Metabolic Mo...

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15th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)

作者： Damiani, Chiara Pescini, Dario Nobile, Marco S. Univ Milano Bicocca Dept Biotechnol & Biosci I-20126 Milan Italy Univ Milano Bicocca Dept Informat Syst & Commun I-20126 Milan Italy SYSBIOIT Ctr Syst Biol I-20126 Milan Italy Univ Milano Bicocca Dept Stat & Quantitat Methods I-20126 Milan Italy

ISBN: (纸本)9783030345853;9783030345846

In the latter years, detailed genome-wide metabolic models have been proposed, paving the way to thorough investigations of the connection between genotype and phenotype in human cells. Nevertheless, classic modeling and dynamic simulation approaches-based either on differential equations integration, Markov chains or hybrid methods-are still unfeasible on genome-wide models due to the lack of detailed information about kinetic parameters and initial molecular amounts. By relying on a steady-state assumption and constraints on extracellular fluxes, constraint-based modeling provides an alternative means-computationally less expensive than dynamic simulation-for the investigation of genome-wide biochemical models. Still, the predictions provided by constraint-based analysis methods (e.g., flux balance analysis) are strongly dependent on the choice of flux boundaries. To contain possible errors induced by erroneous boundary choices, a rational approach suggests to focus on the pivotal ones. In this work we propose a novel methodology for the automatic identification of the key fluxes in large-scale constraint-based models, exploiting variance-based sensitivity analysis and distributing the computation on massively multi-core architectures. We show a proof-of-concept of our approach on core models of relatively small size (up to 314 reactions and 256 chemical species), highlighting the computational challenges.

关键词： Flux Balance Analysis constraint-based modeling Global sensitivity analysis MPI Linear Programming

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Integration of proteomic data with genome-scale metabolic models: A methodological overview

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PROTEIN SCIENCE 2024年第10期33卷 e5150页

作者： Zare, Farid Fleming, Ronan M. T. Univ Galway Sch Med Galway Ireland

The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.

关键词： constraint-based modeling genome-scale model proteomics systems biology

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Engineering Escherichia coli for the utilization of ethylene glycol

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MICROBIAL CELL FACTORIES 2021年第1期20卷 1-17页

作者： Pandit, Aditya Vikram Harrison, Emma Mahadevan, Radhakrishnan Univ Toronto Dept Chem Engn & Appl Chem 200 Coll St Toronto ON M5S 3E5 Canada Univ Toronto Inst Biomed Engn 164 Coll St Toronto ON M5S 3G9 Canada

BackgroundA considerable challenge in the development of bioprocesses for producing chemicals and fuels has been the high cost of feedstocks relative to oil prices, making it difficult for these processes to compete with their conventional petrochemical counterparts. Hence, in the absence of high oil prices in the near future, there has been a shift in the industry to produce higher value compounds such as fragrances for cosmetics. Yet, there is still a need to address climate change and develop biotechnological approaches for producing large market, lower value chemicals and *** this work, we study ethylene glycol (EG), a novel feedstock that we believe has promise to address this challenge. We engineer Escherichia coli (E. coli) to consume EG and examine glycolate production as a case study for chemical production. Using a combination of modeling and experimental studies, we identify oxygen concentration as an important metabolic valve in the assimilation and use of EG as a substrate. Two oxygen-based strategies are thus developed and tested in fed-batch bioreactors. Ultimately, the best glycolate production strategy employed a target respiratory quotient leading to the highest observed fermentation performance. With this strategy, a glycolate titer of 10.4 g/L was reached after 112 h of production time in a fed-batch bioreactor. Correspondingly, a yield of 0.8 g/g from EG and productivity of 0.1 g/L h were measured during the production stage. Our modeling and experimental results clearly suggest that oxygen concentration is an important factor in the assimilation and use of EG as a substrate. Finally, our use of metabolic modeling also sheds light on the intracellular distribution through central metabolism, implicating flux to 2-phosphoglycerate as the primary route for EG ***, our work suggests that EG could provide a renewable starting material for commercial biosynthesis of fuels and chemicals that may achieve economic

关键词： Ethylene glycol Carbon fixation Glycolate Metabolic engineering Metabolic modeling Bioprocess optimization constraint-based modeling

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Advanced modeling of Cellular Proliferation: Toward a Multi-scale Framework Coupling Cell Cycle to Metabolism by Integrating Logical and constraint-based Models

Advanced Modeling of Cellular Proliferation: Toward a Multi-...

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作者： Lucas Zee Matteo Barberis

Biological functions require a coherent cross talk among multiple layers of regulation within the cell. Computational efforts that aim to understand how these layers are integrated across spatial, temporal, and functional scales represent a challenge in Systems Biology. We have developed a computational, multi-scale framework that couples cell cycle and metabolism networks in the budding yeast cell. Here we describe the methodology at the basis of this framework, which integrates on off-the-shelf logical (Boolean) models of a minimal yeast cell cycle with a constraint-based model of metabolism (i.e., the Yeast 7 metabolic network reconstruction). Models are implemented in Python code using the BooleanNet and COBRApy packages, respectively, and are connected through the Boolean logic. The methodology allows for incorporation of interaction data, and validation through –omics data. Furthermore, evolutionary strategies may be incorporated to explore regulatory structures underlying coherent cross talks among regulatory layers. less

关键词： modeling And Simulation Cell Cycle Synchronization Python Data analysis Saccharomyces cerevisiae Metabolism Systems biology Cell cycle constraint-based modeling Logical modeling

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