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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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2
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Andreoli L, Berca C, Katz S, Korshevniuk M, Head RM, Van Steen K. Bridging the gap in precision medicine: TranSYS training programme for next-generation scientists. Front Med (Lausanne) 2024; 11:1348148. [PMID: 38854671 PMCID: PMC11160483 DOI: 10.3389/fmed.2024.1348148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/26/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction In the evolving healthcare landscape, precision medicine's rise necessitates adaptable doctoral training. The European Union has recognized this and promotes the development of international, training-focused programmes called Innovative Training Networks (ITNs). In this article, we introduce TranSYS, an ITN focused on educating the next generation of precision medicine researchers. In an ambition to go beyond describing the consortium goals, our article explores two key aspects of ITNs: the training and collaboration. Methods Using self-report questionnaires, we evaluate the scientific, professional, and personal growth of ESRs over the duration of the ITN and investigate whether this can be linked to network activities. Results Our quantitative analysis approach reveals substantial improvements in scientific, professional, and social skills among young researchers facilitated by the engagement in this interdisciplinary network. We provide case studies underlining the advantages of collaborative environments, featuring innovative scientific exchange within TranSYS. Discussion While challenging, ITNs foster positive growth in young researchers, yet exhibit weaknesses such as balancing stakeholder interests and partner commitment. We believe this study may benefit a variety of stakeholders, from prospective ITN creators to industry partners, to design better sustainable training networks going forward.
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Affiliation(s)
- Lara Andreoli
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
| | - Catalina Berca
- Epithelial Carcinogenesis Group, Molecular Oncology Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Sonja Katz
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
- LifeGlimmer GmbH, Berlin, Germany
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Maryna Korshevniuk
- Genetics Department, University Medical Center Groningen, Groningen, Netherlands
| | | | - Kristel Van Steen
- Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
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3
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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Kulyashov MA, Kolmykov SK, Khlebodarova TM, Akberdin IR. State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. Microorganisms 2023; 11:2987. [PMID: 38138131 PMCID: PMC10745598 DOI: 10.3390/microorganisms11122987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.
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Affiliation(s)
- Mikhail A. Kulyashov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Semyon K. Kolmykov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
| | - Tamara M. Khlebodarova
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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Strain B, Morrissey J, Antonakoudis A, Kontoravdi C. How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models? Biotechnol Bioeng 2023; 120:2460-2478. [PMID: 36866411 PMCID: PMC10952175 DOI: 10.1002/bit.28366] [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: 09/02/2022] [Revised: 02/06/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023]
Abstract
Genome-scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole-cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. Here, we investigate this knowledge gap to determine the reliability of current Chinese hamster ovary (CHO) cell metabolic models. We introduce a new GEM, iCHO2441, and create CHO-S and CHO-K1 specific GEMs. These are compared against iCHO1766, iCHO2048, and iCHO2291. Model predictions are assessed via comparison with experimentally measured growth rates, gene essentialities, amino acid auxotrophies, and 13 C intracellular reaction rates. Our results highlight that all CHO cell models are able to capture extracellular phenotypes and intracellular fluxes, with the updated GEM outperforming the original CHO cell GEM. Cell line-specific models were able to better capture extracellular phenotypes but failed to improve intracellular reaction rate predictions in this case. Ultimately, this work provides an updated CHO cell GEM to the community and lays a foundation for the development and assessment of next-generation flux analysis techniques, highlighting areas for model improvements.
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Affiliation(s)
- Benjamin Strain
- Department of Chemical EngineeringImperial College LondonLondonUK
| | - James Morrissey
- Department of Chemical EngineeringImperial College LondonLondonUK
| | | | - Cleo Kontoravdi
- Department of Chemical EngineeringImperial College LondonLondonUK
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6
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Walakira A, Skubic C, Nadižar N, Rozman D, Režen T, Mraz M, Moškon M. Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma. Comput Biol Med 2023; 159:106957. [PMID: 37116239 DOI: 10.1016/j.compbiomed.2023.106957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/17/2023] [Accepted: 04/16/2023] [Indexed: 04/30/2023]
Abstract
Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.
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Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | - Cene Skubic
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Nejc Nadižar
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures. Metabolites 2023; 13:metabo13010126. [PMID: 36677051 PMCID: PMC9866716 DOI: 10.3390/metabo13010126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.
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8
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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9
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Aminian-Dehkordi J, Valiei A, Mofrad MRK. Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:987104. [PMID: 36299869 PMCID: PMC9589059 DOI: 10.3389/fcvm.2022.987104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.
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Affiliation(s)
| | | | - Mohammad R. K. Mofrad
- Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
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10
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Wang FS, Chen PR, Chen TY, Zhang HX. Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220633. [PMID: 36303939 PMCID: PMC9597175 DOI: 10.1098/rsos.220633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Computer-aided methods can be used to screen potential candidate targets and to reduce the time and cost of drug development. In most of these methods, synthetic lethality is used as a therapeutic criterion to identify drug targets. However, these methods do not consider the side effects during the identification stage. This study developed a fuzzy multi-objective optimization for identifying anti-cancer targets that not only evaluated cancer cell mortality, but also minimized side effects due to treatment. We identified potential anti-cancer enzymes and antimetabolites for the treatment of head and neck cancer (HNC). The identified one- and two-target enzymes were primarily involved in six major pathways, namely, purine and pyrimidine metabolism and the pentose phosphate pathway. Most of the identified targets can be regulated by approved drugs; thus, these drugs are potential candidates for drug repurposing as a treatment for HNC. Furthermore, we identified antimetabolites involved in pathways similar to those identified using a gene-centric approach. Moreover, HMGCR knockdown could not block the growth of HNC cells. However, the two-target combinations of (UMPS, HMGCR) and (CAD, HMGCR) could achieve cell mortality and improve metabolic deviation grades over 22% without reducing the cell viability grade.
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Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Pei-Rong Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Ting-Yu Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Hao-Xiang Zhang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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Režen T, Martins A, Mraz M, Zimic N, Rozman D, Moškon M. Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models. Comput Biol Med 2022; 145:105428. [PMID: 35339845 PMCID: PMC8940269 DOI: 10.1016/j.compbiomed.2022.105428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 12/16/2022]
Abstract
COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.
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Affiliation(s)
- Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | | | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Nikolaj Zimic
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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12
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Genome-Scale Metabolic Model Analysis of Metabolic Differences between Lauren Diffuse and Intestinal Subtypes in Gastric Cancer. Cancers (Basel) 2022; 14:cancers14092340. [PMID: 35565469 PMCID: PMC9104812 DOI: 10.3390/cancers14092340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/05/2022] [Indexed: 01/01/2023] Open
Abstract
Gastric cancer (GC) is one of the most lethal cancers worldwide; it has a high mortality rate, particularly in East Asia. Recently, genetic events (e.g., mutations and copy number alterations) and molecular signaling associated with histologically different GC subtypes (diffuse and intestinal) have been elucidated. However, metabolic differences among the histological GC subtypes have not been studied systematically. In this study, we utilized transcriptome-based genome-scale metabolic models (GEMs) to identify differential metabolic pathways between Lauren diffuse and intestinal subtypes. We found that diverse metabolic pathways, including cholesterol homeostasis, xenobiotic metabolism, fatty acid metabolism, the MTORC1 pathway, and glycolysis, were dysregulated between the diffuse and intestinal subtypes. Our study provides an overview of the metabolic differences between the two subtypes, possibly leading to an understanding of metabolism in GC heterogeneity.
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Vijayakumar S, Angione C. Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002. STAR Protoc 2021; 2:100837. [PMID: 34632416 PMCID: PMC8488602 DOI: 10.1016/j.xpro.2021.100837] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data. For complete details on the use and execution of this protocol, please refer to Vijayakumar et al. (2020).
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Affiliation(s)
- Supreeta Vijayakumar
- School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
| | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, UK
- Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK
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