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Sridhar S, Bhalla P, Kullu J, Veerapaneni S, Sahoo S, Bhatt N, Suraishkumar GK. A reactive species reactions module for integration into genome-scale metabolic models for improved insights: Application to cancer. Metab Eng 2023; 80:78-93. [PMID: 37689259 DOI: 10.1016/j.ymben.2023.08.006] [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: 04/03/2023] [Revised: 08/07/2023] [Accepted: 08/28/2023] [Indexed: 09/11/2023]
Abstract
Reactive species (RS) play significant roles in many disease contexts. Despite their crucial roles in diseases including cancer, the RS are not adequately modeled in the genome-scale metabolic (GSM) models, which are used to understand cell metabolism in disease contexts. We have developed a scalable RS reactions module that can be integrated with any Recon 3D-derived human metabolic model, or after fine-tuning, with any metabolic model. With RS-integration, the GSM models of three cancers (basal-like triple negative breast cancer (TNBC), high grade serous ovarian carcinoma (HGSOC) and colorectal cancer (CRC)) built from Recon 3D, precisely highlighted the increases/decreases in fluxes (dysregulation) occurring in important pathways of these cancers. These dysregulations were not prominent in the standard cancer models without the RS module. Further, the results from these RS-integrated cancer GSM models suggest the following decreasing order in the ease of ferroptosis-targeting to treat the cancers: TNBC > HGSOC > CRC.
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Affiliation(s)
- Subasree Sridhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India; Centre for Integrative Biology and Systems medicinE, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Prerna Bhalla
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Justin Kullu
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Sriya Veerapaneni
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Swagatika Sahoo
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Nirav Bhatt
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India; Centre for Integrative Biology and Systems medicinE, Indian Institute of Technology Madras, Chennai, 600 036, India; Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, 600 036, India.
| | - G K Suraishkumar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building - 1 and 2, Indian Institute of Technology Madras, Chennai, 600 036, India.
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Yasemi M, Jolicoeur M. A genome-scale dynamic constraint-based modelling (gDCBM) framework predicts growth dynamics, medium composition and intracellular flux distributions in CHO clonal variations. Metab Eng 2023; 78:209-222. [PMID: 37348809 DOI: 10.1016/j.ymben.2023.06.005] [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: 07/06/2022] [Revised: 11/16/2022] [Accepted: 06/09/2023] [Indexed: 06/24/2023]
Abstract
Optimizing mammalian cell growth and bioproduction is a tedious task. However, due to the inherent complexity of eukaryotic cells, heuristic experimental approaches such as, metabolic engineering and bioprocess design, are frequently integrated with mathematical models of cell culture to improve biological process efficiency and find paths for improvement. Constraint-based metabolic models have evolved over the last two decades to be used for dynamic modelling in addition to providing a linear description of steady-state metabolic systems. Formulation and implementation of the underlying optimization problems require special attention to the model's performance and feasibility, lack of defects in the definition of system components, and consideration of optimal alternate solutions, in addition to processing power limitations. Here, the time-resolved dynamics of a genome-scale metabolic network of Chinese hamster ovary (CHO) cell metabolism are shown using a genome-scale dynamic constraint-based modelling framework (gDCBM). The metabolic network was adapted from a reference model of CHO genome-scale metabolic model (GSMM), iCHO_DG44_v1, and dynamic restrictions were imposed to its exchange fluxes based on experimental results. We used this framework for predicting physiological changes in CHO clonal variants. Because of the methodical creation of the components for the flux balance analysis optimization problem and the integration of a switch time, this model can generate sequential predictions of intracellular fluxes during growth and non-growth phases (per hour of culture time) and transparently reveal the shortcomings in such practice. As a result of the differences exploited by various clones, we can understand the relevance of changes in intracellular flux distribution and exometabolomics. The integration of various omics data into the given gDCBM framework, as well as the reductionist analysis of the model, can further help bioprocess optimization.
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Affiliation(s)
- Mohammadreza Yasemi
- Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Centre-ville Station, Montréal, Québec, H3C 3A7, Canada.
| | - Mario Jolicoeur
- Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Centre-ville Station, Montréal, Québec, H3C 3A7, Canada.
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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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Sahu A, Blätke MA, Szymański JJ, Töpfer N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput Struct Biotechnol J 2021; 19:4626-4640. [PMID: 34471504 PMCID: PMC8382995 DOI: 10.1016/j.csbj.2021.08.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Affiliation(s)
- Ankur Sahu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Jędrzej Jakub Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Nadine Töpfer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
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