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Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart

BMC BIOINFORMATICS 2023年第1期24卷 1-25页

作者： Chu, Sz-Wei Wang, Feng-Sheng Natl Chung Cheng Univ Dept Chem Engn Chiayi 621301 Taiwan

In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco's modified eagle medium (DMEM) and Ham's medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham's medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.

关键词： Flux balance analysis Genome-scale metabolic model constraint-based modeling Drug discovery Hybrid differential evolution Multi-level optimization

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modeling Microbial Communities: Perspective and Challenges

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ACS SYNTHETIC BIOLOGY 2024年第8期13卷 2260-2270页

作者： Raajaraam, Lavanya Raman, Karthik Indian Inst Technol IIT Madras Bhupat & Jyoti Mehta Sch Biosci Dept Biotechnol Chennai 600036 India IIT Madras Ctr Integrat Biol & Syst Med Chennai 600036 India IIT Madras Robert Bosch Ctr Data Sci & Artificial Intelligenc Chennai 600036 India IIT Madras Wadhwani Sch Data Sci & Artificial Intelligence Dept Data Sci & AI Chennai 600036 India

Microbial communities are immensely important due to their widespread presence and profound impact on various facets of life. Understanding these complex systems necessitates mathematical modeling, a powerful tool for simulating and predicting microbial community behavior. This review offers a critical analysis of metabolic modeling and highlights key areas that would greatly benefit from broader discussion and collaboration. Moreover, we explore the challenges and opportunities linked to the intricate nature of these communities, spanning data generation, modeling, and validation. We are confident that ongoing advancements in modeling techniques, such as machine learning, coupled with interdisciplinary collaborations, will unlock the full potential of microbial communities across diverse applications.

关键词： metabolic modeling genome-scale models metabolicengineering constraint-based modeling microbiome microbial consortia machine learning

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Unveiling the potential of systems biology in biotechnology and biomedical research

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Systems Microbiology and Biomanufacturing 2024年第4期4卷 1217-1238页

作者： S.Saranya L.Thamanna P.Chellapandi Industrial Systems Biology Lab Department of BioinformaticsSchool of Life SciencesBharathidasan UniversityTiruchirappalliTamil Nadu 620024India

“In silico organisms”are computational genome-scale metabolic models used in systems and synthetic biology developed by constraint-based metabolic simulations using multi-omics and phenotypic *** quality of these models is hidden because of the limited availability of genomic information and genome-scale metabolic reconstruction *** this review,237 manually curated genome-scale models for various organisms with industrial and clinical significance were comprehensively reviewed,and their modelling information was tabulated based on *** review provides a comprehensive summary of potential applications of systems biology in biotechnology and biomedical *** broad applicability has been explored in the process of model improvement and design of experiments in metabolic design and drug *** review summarizes their recent advances,challenges,and practical applications in Gram-negative bacteria,Gram-positive bacteria,archaea,fungi,algae,plants,and ***-scale models of microbes have been reviewed to address their various applications in metabolic systems engineering,strain optimization,bioremediation,biomanufacturing,and personalized systems *** models have been explored to understand the molecular mechanisms underlying pathogenesis,virulence,host-microbe interactions,and metabolic *** review provides an overview of the current knowledge on human metabolic reconstructions and their important roles in human,microbiota-related,and complex metabolic ***-scale models of human and animal metals offer ethical alternatives to the traditional animal testing *** progress in systems biology research will lead to the development of indispensable databases,computational tools,and analytical *** will strengthen data-driven discovery and facilitate integration of biological information into living systems.

关键词： Systems biology Genome-scale models In silico organisms constraint-based modeling Cell factory Synthetic biology

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Optimal protein allocation controls the inhibition of GltA and AcnB in Neisseria gonorrhoeae

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PATHOGENS AND DISEASE 2024年第82期82卷 ftae023页

作者： Shahreen, Nabia Chowdhury, Niaz Bahar Saha, Rajib Univ Nebraska Dept Chem & Biomol Engn Lincoln NE 68588 USA

Neisseria gonorrhea (Ngo) is a major concern for global public health due to its severe implications for reproductive health. Understanding its metabolic phenotype is crucial for comprehending its pathogenicity. Despite Ngo's ability to encode tricarboxylic acid (TCA) cycle proteins, GltA and AcnB, their activities are notably restricted. To investigate this phenomenon, we used the iNgo_557 metabolic model and incorporated a constraint on total cellular protein content. Our results indicate that low cellular protein content severely limits GltA and AcnB activity, leading to a shift toward acetate overflow for Adenosine triphosphate (ATP) production, which is more efficient in terms of protein usage. Surprisingly, increasing cellular protein content alleviates this restriction on GltA and AcnB and delays the onset of acetate overflow, highlighting protein allocation as a critical determinant in understanding Ngo's metabolic phenotype. These findings underscore the significance of Ngo's metabolic adaptation in light of optimal protein allocation, providing a blueprint to understand Ngo's metabolic landscape. The article reveals that optimal protein allocation is key to understanding Neisseria gonorrhoeae's metabolism, impacting critical enzymes and energy production pathways.

关键词： optimal protein allocation constraint-based modeling energy production acetate overflow Neisseria gonorrhoeae

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Cellular and computational models reveal environmental and metabolic interactions in MMUT-type methylmalonic aciduria

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JOURNAL OF INHERITED METABOLIC DISEASE 2023年第3期46卷 421-435页

作者： Ramon, Charlotte Traversi, Florian Buerer, Celine Froese, D. Sean Stelling, Joerg Swiss Fed Inst Technol SIB Swiss Inst Bioinformat Dept Biosyst Sci & Engn Basel Switzerland Univ Zurich Univ Childrens Hosp Zurich Childrens Res Ctr Div Metab Zurich Switzerland Univ Zurich Univ Childrens Hosp Zurich Childrens Res Ctr Div Metab CH-8032 Zurich Switzerland Swiss Fed Inst Technol Dept Biosyst Sci & Engn CH-4058 Basel Switzerland Swiss Fed Inst Technol SIB Swiss Inst Bioinformat CH-4058 Basel Switzerland

Methylmalonyl-coenzyme A (CoA) mutase (MMUT)-type methylmalonic aciduria is a rare inherited metabolic disease caused by the loss of function of the MMUT enzyme. Patients develop symptoms resembling those of primary mitochondrial disorders, but the underlying causes of mitochondrial dysfunction remain unclear. Here, we examined environmental and genetic interactions in MMUT deficiency using a combination of computational modeling and cellular models to decipher pathways interacting with MMUT. Immortalized fibroblast (hTERT BJ5ta) MMUT-KO (MUTKO) clones displayed a mild mitochondrial impairment in standard glucose-based medium, but they did not to show increased reliance on respiratory metabolism nor reduced growth or viability. Consistently, our modeling predicted MUTKO specific growth phenotypes only for lower extracellular glutamine concentrations. Indeed, two of three MMUT-deficient BJ5ta cell lines showed a reduced viability in glutamine-free medium. Further, growth on 183 different carbon and nitrogen substrates identified increased NADH (nicotinamide adenine dinucleotide) metabolism of BJ5ta and HEK293 MUTKO cells compared with controls on purine- and glutamine-based substrates. With this knowledge, our modeling predicted 13 reactions interacting with MMUT that potentiate an effect on growth, primarily those of secondary oxidation of propionyl-CoA, oxidative phosphorylation and oxygen diffusion. Of these, we validated 3-hydroxyisobutytyl-CoA hydrolase (HIBCH) in the secondary propionyl-CoA oxidation pathway. Altogether, these results suggest compensation for the loss of MMUT function by increasing anaplerosis through glutamine or by diverting flux away from MMUT through the secondary propionyl-CoA oxidation pathway, which may have therapeutic relevance.

关键词： constraint-based modeling CRISPR-Cas9 metabolism methylmalonic aciduria rare disease

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Statistical Techniques to Explore the Quality of constraints in constraint-based modeling Environments

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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2013年第1-4期23卷 22-49页

作者： Galvez, Jaime Conejo, Ricardo Guzman, Eduardo Univ Malaga Malaga 29071 Spain

One of the most popular student modeling approaches is constraint-based modeling (CBM). It is an efficient approach that can be easily applied inside an Intelligent Tutoring System (ITS). Even with these characteristics, building new ITSs requires carefully designing the domain model to be taught because different sources of errors could affect the efficiency of the system. In this paper a novel mechanism for studying the quality of the elements in the domain model of CBM systems is presented. This mechanism combines CBM with the Item Response Theory (IRT), a data-driven technique for automatic assessment. The goal is to improve the quality of the elements that are used in problem solving environments for assessment or instruction. In this paper we propose a set of statistical techniques, i.e., the analysis of the point-biserial correlation, the Cronbach's alpha and the information function, to explore the quality of constraints. Two different tools have been used to test this approach: a problem solving environment designed to assess students in project investment analysis;and an independent component that performs assessments using CBM and IRT. Results suggest that the three methods produce consistent diagnosis and may be complementary in some cases. In the experiments we have carried out they were able to detect faulty, bad and good quality constraints.

关键词： Problem solving environments constraint-based modeling Item response theory

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Genome-scale metabolic models applied for human health and biopharmaceutical engineering

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Quantitative Biology 2023年第4期11卷 363-375页

作者： Feiran Li Yu Chen Johan Gustafsson Hao Wang YiWang Chong Zhang Xinhui Xing nstitute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenChina Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic BiologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina Department of Biology and Biological Engineering Chalmers University of TechnologyGothenburgSweden Key Laboratory for Industrial Biocatalysis Ministry of EducationInstitute of Biochemical EngineeringDepartment of Chemical EngineeringTsinghua University BeijingChina Center for Synthetic and Systems Biology Tsinghua UniversityBejingChina Institute of Biomedical Health Technology and Engineering Shenzhen Bay LaboratoryShenzhenChina

Over the last 15 years,genome-scale metabolic models(GEMs)have been reconstructed for human and model animals,such as mouse and rat,to systematically understand metabolism,simulate multicellular or multi-tissue interplay,understand human diseases,and guide cell factory design for biopharmaceutical protein ***,we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux ***,we review the history of GEM development for quantitative understanding of Homo sapiens and other relevant animals,together with their *** describe how model develops from *** to other animals and from generic purpose to precise context-specific *** progress of GEMs for animals greatly expand our systematic understanding of metabolism in human and related *** discuss the difficulties and present perspectives on the GEM development and the quest to integrate more biological processes and omics data for future research and *** truly hope that this review can inspire new models developed for other mammalian organisms and generate new algorithms for integrating big data to conduct more in-depth analysis to further make progress on human health and biopharmaceutical engineering.

关键词： constraint-based modeling disease genome-scale metabolicmodel metabolism

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Protein cost minimization promotes the emergence of coenzyme redundancy

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第14期119卷 e2110787119-e2110787119页

作者： Goldford, Joshua E. George, Ashish B. Flamholz, Avi I. Segre, Daniel MIT Phys Living Syst Cambridge MA 02139 USA Blue Marble Space Inst Sci Seattle WA 98154 USA Boston Univ Bioinformat Program Biol Design Ctr Boston MA 02215 USA Boston Univ Dept Phys Boston MA 02215 USA Univ Illinois Carl R Woese Inst Genom Biol Urbana IL 61801 USA Univ Illinois Dept Plant Biol Urbana IL 61801 USA CALTECH Div Biol & Biol Engn Pasadena CA 91125 USA Boston Univ Dept Biomed Engn Boston MA 02215 USA Boston Univ Dept Biol Boston MA 02215 USA

Coenzymes distribute a variety of chemical moieties throughout cellular metabolism, participating in group (e.g., phosphate and aryl) and electron transfer. For a variety of reactions requiring acceptors or donors of specific resources, there often exist degenerate sets of molecules [e.g., NAD(H) and NADP(H)] that carry out similar functions. Although the physiological roles of various coenzyme systems are well established, it is unclear what selective pressures may have driven the emergence of coenzyme redundancy. Here, we use genome-wide metabolic modeling approaches to decompose the selective pressures driving enzymatic specificity for either NAD(H) or NADP(H) in the metabolic network of Escherichia coli. We found that few enzymes are thermodynamically constrained to using a single coenzyme, and in principle a metabolic network relying on only NAD(H) is feasible. However, structural and sequence analyses revealed widespread conservation of residues that retain selectivity for either NAD(H) or NADP(H), suggesting that additional forces may shape specificity. Using a model accounting for the cost of oxidoreductase enzyme expression, we found that coenzyme redundancy universally reduces the minimal amount of protein required to catalyze coenzyme-coupled reactions, inducing individual reactions to strongly prefer one coenzyme over another when reactions are near thermodynamic equilibrium. We propose that protein minimization generically promotes coenzyme redundancy and that coenzymes typically thought to exist in a single pool (e.g., coenzyme A [CoA]) may exist in more than one form (e.g., dephospho-CoA).

关键词： metabolism evolution coenzymes constraint-based modeling

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Model validation and selection in metabolic flux analysis and flux balance analysis

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BIOTECHNOLOGY PROGRESS 2024年第1期40卷 e3413-e3413页

作者： Kaste, Joshua A. M. Shachar-Hill, Yair Michigan State Univ Dept Biochem & Mol Biol E Lansing MI USA Michigan State Univ Dept Plant Biol E Lansing MI USA Michigan State Univ Dept Biochem & Mol Biol 603 Wilson Rd E Lansing MI 48823 USA

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the chi 2-test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

关键词： constraint-based modeling flux balance analysis metabolic flux analysis metabolic modeling model selection model validation

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Applications of genome-scale metabolic models to the study of human diseases: A systematic review

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 256卷 108397-108397页

作者： Cortese, Nicola Procopio, Anna Merola, Alessio Zaffino, Paolo Cosentino, Carlo Magna Graecia Univ Catanzaro Dept Expt & Clin Med I-88100 Catanzaro Italy

Background and Objectives: Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high- throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases. Methods: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus (R), (R) , National Library of Medicine PubMed (R) (R) and Clarivate Web of ScienceTMdatabases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined. Results: The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models. Conclusions: The number of scientific papers, utilizing GEM-based approaches for the investigation of hum

关键词： Genome-scale metabolic networks constraint-based modeling Systems biology Simulation Systematic literature review

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