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Bioengineering
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Metabolic Modeling and Engineering
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Metabolic Modeling and Engineering

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biochemical Engineering".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 6231

Special Issue Editors

Dr. Dipali Singh

E-Mail Website
Guest Editor
Quadram Institute Bioscience, Norwich Research Park, Norwich NR4 7UQ, UK
Interests: systems biology; computational and mathematical biology; genome-scale metabolic models; flux balance analysis; constraint-based modeling
Dr. Jianwen Ye

E-Mail Website
Guest Editor
School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
Interests: synthetic biology; cell factory design and transformation; metabolic engineering; bioprocess optimization and scale-up; biomaterials; microorganism

Special Issue Information

Dear Colleagues,

The genome-scale metabolic model describes the network of biochemical reactions in a given organism, typically derived from an annotated genome. It has been instrumental in characterizing the system-level features and properties that arise from the biochemical interactions.

Genome-scale metabolic models are now widely used in both elemental and applied studies, ranging from the evaluation of the genotype–phenotype relationship to guiding experimental strategies, the identification of novel pathways and potential drug targets. In addition, these models have wide scope to guide strategies for optimization and metabolic engineering for the production of specific metabolites or chemical compounds of industrial and pharmaceutical interest.

This Special Issue will focus on the recent advances, challenges, and breakthroughs in the field of metabolic modeling and metabolic engineering. The topics of interest may cover, but are not limited to:

  • Reconstruction of genome-scale metabolic models and their applications;
  • Applications of metabolic models for the optimizing of media composition for improved growth and/or metabolite yield;
  • Development of metabolic modeling tools/analysis pipeline for potential genetic and environmental modifications for metabolic engineering;
  • Development of computational algorithms and tools for the integration of high-throughput data into metabolic models;
  • Applications of metabolic models for evaluation of the genotype–phenotype relationship. 

Dr. Dipali Singh
Dr. Jianwen Ye
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. Bioengineering 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.

Published Papers (4 papers)

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Research

16 pages, 2619 KiB  
Article
Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective
by Carolin Bokelmann, Alireza Ehsani, Jochen Schaub and Fabian Stiefel
Bioengineering 2024, 11(4), 331; https://doi.org/10.3390/bioengineering11040331 - 28 Mar 2024
Viewed by 675
Abstract
Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in [...] Read more.
Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism. Full article
(This article belongs to the Special Issue Metabolic Modeling and Engineering)
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30 pages, 833 KiB  
Article
Parameter Estimation for Kinetic Models of Chemical Reaction Networks from Partial Experimental Data of Species’ Concentrations
by Manvel Gasparyan and Shodhan Rao
Bioengineering 2023, 10(9), 1056; https://doi.org/10.3390/bioengineering10091056 - 07 Sep 2023
Viewed by 1426
Abstract
The current manuscript addresses the problem of parameter estimation for kinetic models of chemical reaction networks from observed time series partial experimental data of species concentrations. It is demonstrated how the Kron reduction method of kinetic models, in conjunction with the (weighted) least [...] Read more.
The current manuscript addresses the problem of parameter estimation for kinetic models of chemical reaction networks from observed time series partial experimental data of species concentrations. It is demonstrated how the Kron reduction method of kinetic models, in conjunction with the (weighted) least squares optimization technique, can be used as a tool to solve the above-mentioned ill-posed parameter estimation problem. First, a new trajectory-independent measure is introduced to quantify the dynamical difference between the original mathematical model and the corresponding Kron-reduced model. This measure is then crucially used to estimate the parameters contained in the kinetic model so that the corresponding values of the species’ concentrations predicted by the model fit the available experimental data. The new parameter estimation method is tested on two real-life examples of chemical reaction networks: nicotinic acetylcholine receptors and Trypanosoma brucei trypanothione synthetase. Both weighted and unweighted least squares techniques, combined with Kron reduction, are used to find the best-fitting parameter values. The method of leave-one-out cross-validation is utilized to determine the preferred technique. For nicotinic receptors, the training errors due to the application of unweighted and weighted least squares are 3.22 and 3.61 respectively, while for Trypanosoma synthetase, the application of unweighted and weighted least squares result in training errors of 0.82 and 0.70 respectively. Furthermore, the problem of identifiability of dynamical systems, i.e., the possibility of uniquely determining the parameters from certain types of output, has also been addressed. Full article
(This article belongs to the Special Issue Metabolic Modeling and Engineering)
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11 pages, 1845 KiB  
Article
Prediction of Metabolic Flux Distribution by Flux Sampling: As a Case Study, Acetate Production from Glucose in Escherichia coli
by Yuki Kuriya, Masahiro Murata, Masaki Yamamoto, Naoki Watanabe and Michihiro Araki
Bioengineering 2023, 10(6), 636; https://doi.org/10.3390/bioengineering10060636 - 24 May 2023
Viewed by 1591
Abstract
Omics data was acquired, and the development and research of metabolic simulation and analysis methods using them were also actively carried out. However, it was a laborious task to acquire such data each time the medium composition, culture conditions, and target organism changed. [...] Read more.
Omics data was acquired, and the development and research of metabolic simulation and analysis methods using them were also actively carried out. However, it was a laborious task to acquire such data each time the medium composition, culture conditions, and target organism changed. Therefore, in this study, we aimed to extract and estimate important variables and necessary numbers for predicting metabolic flux distribution as the state of cell metabolism by flux sampling using a genome-scale metabolic model (GSM) and its analysis. Acetic acid production from glucose in Escherichia coli with GSM iJO1366 was used as a case study. Flux sampling obtained by OptGP using 1000 pattern constraints on substrate, product, and growth fluxes produced a wider sample than the default case. The analysis also suggested that the fluxes of iron ions, O2, CO2, and NH4+, were important for predicting the metabolic flux distribution. Additionally, the comparison with the literature value of 13C-MFA using CO2 emission flux as an example of an important flux suggested that the important flux obtained by this method was valid for the prediction of flux distribution. In this way, the method of this research was useful for extracting variables that were important for predicting flux distribution, and as a result, the possibility of contributing to the reduction of measurement variables in experiments was suggested. Full article
(This article belongs to the Special Issue Metabolic Modeling and Engineering)
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19 pages, 2654 KiB  
Article
A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism
by Igor Marin de Mas, Helena Herand, Jorge Carrasco, Lars K. Nielsen and Pär I. Johansson
Bioengineering 2023, 10(5), 576; https://doi.org/10.3390/bioengineering10050576 - 10 May 2023
Cited by 1 | Viewed by 1948
Abstract
Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that [...] Read more.
Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant. Full article
(This article belongs to the Special Issue Metabolic Modeling and Engineering)
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