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Biomolecules
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Computational Biology for Metabolic Modelling and Pathway Design
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Computational Biology for Metabolic Modelling and Pathway Design

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 30179

Special Issue Editors

Prof. Dr. Hongwu Ma

E-Mail Website
Guest Editor
Biodesign Center, Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
Interests: metabolic network analysis; metabolic engineering; synthetic biology; computational systems biology
Prof. Dr. Igor Groyanin

E-Mail Website1 Website2
Guest Editor
1. School of Informatics, University of Edinburgh, Edinburgh EH8 9AZ, UK
2. Biological Systems Unit, Okinawa Institute Science and Technology, Kunigami District, Okinawa 904-0495, Japan
Interests: systems biology and systems medicine including human biochemical network reconstruction; modelling of complex biological systems; microbial fuel cells and other biotechnology and bioinformatics applications

Special Issue Information

Dear Colleagues,

Modelling is at the centre of the “DBTL” (Design–Build–Test–Learn) cycle of Synthetic Biology, developed based on test data and used to design new biosystems. Computational reconstruction and analysis of genome-scale metabolic network models (GEMs) are crucial for understanding cellular physiology from a systems level and subsequently providing guidance in the design of new metabolic engineering strategies. The purpose of this Special Issue on “Computational Biology for Metabolic Modelling and Pathway Design” is to demonstrate the latest research on metabolic modelling and its application in metabolic pathway design and engineering strategy design. We invite scientists to submit their original research (as full papers or short communications) and review papers for publication in this Special Issue.

Topics of interest for this Special Issue include (but are not limited to) the following:

  • Reconstruction and analysis of genome-scale metabolic networks;
  • New metabolic models with additional constraints (e.g., an enzymatic constraint);
  • Integration of metabolic models with omics data;
  • Metabolic models of a microbial community;
  • New databases/methods/algorithms/tools for the design of new pathways and metabolic engineering strategies;
  • AI application in metabolic modelling and metabolic engineering design;
  • Non-natural metabolic pathway design.

Prof. Dr. Hongwu Ma
Prof. Dr. Igor Groyanin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolic model
  • metabolic pathway design
  • metabolic engineering
  • biodesign

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Published Papers (9 papers)

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Research

Jump to: Review

10 pages, 1543 KiB  
Article
FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models
by Anatoly Sorokin and Igor Goryanin
Biomolecules 2023, 13(3), 500; https://doi.org/10.3390/biom13030500 - 9 Mar 2023
Cited by 2 | Viewed by 2242
Abstract
Background: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the [...] Read more.
Background: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. Methods: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. Results: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. Conclusions: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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15 pages, 7034 KiB  
Article
A Pan-Draft Metabolic Model Reflects Evolutionary Diversity across 332 Yeast Species
by Hongzhong Lu, Eduard J. Kerkhoven and Jens Nielsen
Biomolecules 2022, 12(11), 1632; https://doi.org/10.3390/biom12111632 - 3 Nov 2022
Cited by 4 | Viewed by 2224
Abstract
Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using [...] Read more.
Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using two alternative procedures from the toolbox RAVEN v 2.0. We found that draft GEMs could reflect the difference in yeast metabolic potentials, and therefore, could be utilized to probe the evolutionary trend of metabolism among 332 yeast species. We created a pan-draft metabolic model to account for the metabolic capacity of every sequenced yeast species by merging all draft GEMs. Further analysis showed that the pan-reactome of yeast has a “closed” property, which confirmed the great conservatism that exists in yeast metabolic evolution. Lastly, the quantitative correlations among trait similarity, evolutionary distances, genotype, and model similarity were thoroughly investigated. The results suggest that the evolutionary distance and genotype, to some extent, determine model similarity, but not trait similarity, indicating that multiple mechanisms shape yeast trait evolution. A large-scale reconstruction and integrative analysis of yeast draft GEMs would be a valuable resource to probe the evolutionary mechanism behind yeast trait variety and to further refine the existing yeast species-specific GEMs for the community. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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15 pages, 2246 KiB  
Article
Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum
by Jinhui Niu, Zhitao Mao, Yufeng Mao, Ke Wu, Zhenkun Shi, Qianqian Yuan, Jingyi Cai and Hongwu Ma
Biomolecules 2022, 12(10), 1499; https://doi.org/10.3390/biom12101499 - 17 Oct 2022
Cited by 15 | Viewed by 3519
Abstract
The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing [...] Read more.
The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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15 pages, 1874 KiB  
Article
Re-Programing Glucose Catabolism in the Microalga Chlorella sorokiniana under Light Condition
by Tingting Li, Na Pang, Lian He, Yuan Xu, Xinyu Fu, Yinjie Tang, Yair Shachar-Hill and Shulin Chen
Biomolecules 2022, 12(7), 939; https://doi.org/10.3390/biom12070939 - 4 Jul 2022
Cited by 10 | Viewed by 2351
Abstract
The microalga Chlorella sorokiniana has attracted much attention for lipid production and wastewater treatment. It can perform photosynthesis and organic carbon utilization concurrently. To understand its phototrophic metabolism, a biomass compositional analysis, a 13C metabolic flux analysis, and metabolite pool size analyses [...] Read more.
The microalga Chlorella sorokiniana has attracted much attention for lipid production and wastewater treatment. It can perform photosynthesis and organic carbon utilization concurrently. To understand its phototrophic metabolism, a biomass compositional analysis, a 13C metabolic flux analysis, and metabolite pool size analyses were performed. Under dark condition, the oxidative pentose phosphate pathway (OPP) was the major route for glucose catabolism (88% carbon flux) and a cyclic OPP–glycolytic route for glucose catabolism was formed. Under light condition, fluxes in the glucose catabolism, tricarboxylic acid (TCA) cycle, and anaplerotic reaction (CO2 fixation via phosphoenolpyruvate carboxylase) were all suppressed. Meanwhile, the RuBisCO reaction became active and the ratio of its carbon fixation to glucose carbon utilization was determined as 7:100. Moreover, light condition significantly reduced the pool sizes of sugar phosphate metabolites (such as E4P, F6P, and S7P) and promoted biomass synthesis (which reached 0.155 h−1). In addition, light condition increased glucose consumption rates, leading to higher ATP and NADPH production and a higher protein content (43% vs. 30%) in the biomass during the exponential growth phase. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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11 pages, 1246 KiB  
Article
In Silico Design Strategies for the Production of Target Chemical Compounds Using Iterative Single-Level Linear Programming Problems
by Tomokazu Shirai and Akihiko Kondo
Biomolecules 2022, 12(5), 620; https://doi.org/10.3390/biom12050620 - 21 Apr 2022
Cited by 2 | Viewed by 2138
Abstract
The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and [...] Read more.
The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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14 pages, 3086 KiB  
Article
Integrative Gene Expression and Metabolic Analysis Tool IgemRNA
by Kristina Grausa, Ivars Mozga, Karlis Pleiko and Agris Pentjuss
Biomolecules 2022, 12(4), 586; https://doi.org/10.3390/biom12040586 - 16 Apr 2022
Cited by 2 | Viewed by 3138
Abstract
Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has [...] Read more.
Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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13 pages, 3288 KiB  
Article
ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model
by Zhitao Mao, Xin Zhao, Xue Yang, Peiji Zhang, Jiawei Du, Qianqian Yuan and Hongwu Ma
Biomolecules 2022, 12(1), 65; https://doi.org/10.3390/biom12010065 - 2 Jan 2022
Cited by 25 | Viewed by 4896
Abstract
Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of [...] Read more.
Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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Review

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21 pages, 1652 KiB  
Review
Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges
by Xinyu Bi, Yanfeng Liu, Jianghua Li, Guocheng Du, Xueqin Lv and Long Liu
Biomolecules 2022, 12(5), 721; https://doi.org/10.3390/biom12050721 - 19 May 2022
Cited by 13 | Viewed by 4897
Abstract
Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or [...] Read more.
Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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19 pages, 9530 KiB  
Review
Metabolic Engineering and Regulation of Diol Biosynthesis from Renewable Biomass in Escherichia coli
by Tong Wu, Yumei Liu, Jinsheng Liu, Zhenya Chen and Yi-Xin Huo
Biomolecules 2022, 12(5), 715; https://doi.org/10.3390/biom12050715 - 18 May 2022
Cited by 4 | Viewed by 3689
Abstract
As bulk chemicals, diols have wide applications in many fields, such as clothing, biofuels, food, surfactant and cosmetics. The traditional chemical synthesis of diols consumes numerous non-renewable energy resources and leads to environmental pollution. Green biosynthesis has emerged as an alternative method to [...] Read more.
As bulk chemicals, diols have wide applications in many fields, such as clothing, biofuels, food, surfactant and cosmetics. The traditional chemical synthesis of diols consumes numerous non-renewable energy resources and leads to environmental pollution. Green biosynthesis has emerged as an alternative method to produce diols. Escherichia coli as an ideal microbial factory has been engineered to biosynthesize diols from carbon sources. Here, we comprehensively summarized the biosynthetic pathways of diols from renewable biomass in E. coli and discussed the metabolic-engineering strategies that could enhance the production of diols, including the optimization of biosynthetic pathways, improvement of cofactor supplementation, and reprogramming of the metabolic network. We then investigated the dynamic regulation by multiple control modules to balance the growth and production, so as to direct carbon sources for diol production. Finally, we proposed the challenges in the diol-biosynthesis process and suggested some potential methods to improve the diol-producing ability of the host. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling and Pathway Design)
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