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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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2
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Morotti M, Grimm AJ, Hope HC, Arnaud M, Desbuisson M, Rayroux N, Barras D, Masid M, Murgues B, Chap BS, Ongaro M, Rota IA, Ronet C, Minasyan A, Chiffelle J, Lacher SB, Bobisse S, Murgues C, Ghisoni E, Ouchen K, Bou Mjahed R, Benedetti F, Abdellaoui N, Turrini R, Gannon PO, Zaman K, Mathevet P, Lelievre L, Crespo I, Conrad M, Verdeil G, Kandalaft LE, Dagher J, Corria-Osorio J, Doucey MA, Ho PC, Harari A, Vannini N, Böttcher JP, Dangaj Laniti D, Coukos G. PGE 2 inhibits TIL expansion by disrupting IL-2 signalling and mitochondrial function. Nature 2024; 629:426-434. [PMID: 38658764 PMCID: PMC11078736 DOI: 10.1038/s41586-024-07352-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 03/26/2024] [Indexed: 04/26/2024]
Abstract
Expansion of antigen-experienced CD8+ T cells is critical for the success of tumour-infiltrating lymphocyte (TIL)-adoptive cell therapy (ACT) in patients with cancer1. Interleukin-2 (IL-2) acts as a key regulator of CD8+ cytotoxic T lymphocyte functions by promoting expansion and cytotoxic capability2,3. Therefore, it is essential to comprehend mechanistic barriers to IL-2 sensing in the tumour microenvironment to implement strategies to reinvigorate IL-2 responsiveness and T cell antitumour responses. Here we report that prostaglandin E2 (PGE2), a known negative regulator of immune response in the tumour microenvironment4,5, is present at high concentrations in tumour tissue from patients and leads to impaired IL-2 sensing in human CD8+ TILs via the PGE2 receptors EP2 and EP4. Mechanistically, PGE2 inhibits IL-2 sensing in TILs by downregulating the IL-2Rγc chain, resulting in defective assembly of IL-2Rβ-IL2Rγc membrane dimers. This results in impaired IL-2-mTOR adaptation and PGC1α transcriptional repression, causing oxidative stress and ferroptotic cell death in tumour-reactive TILs. Inhibition of PGE2 signalling to EP2 and EP4 during TIL expansion for ACT resulted in increased IL-2 sensing, leading to enhanced proliferation of tumour-reactive TILs and enhanced tumour control once the cells were transferred in vivo. Our study reveals fundamental features that underlie impairment of human TILs mediated by PGE2 in the tumour microenvironment. These findings have therapeutic implications for cancer immunotherapy and cell therapy, and enable the development of targeted strategies to enhance IL-2 sensing and amplify the IL-2 response in TILs, thereby promoting the expansion of effector T cells with enhanced therapeutic potential.
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MESH Headings
- Animals
- Humans
- Mice
- CD8-Positive T-Lymphocytes/cytology
- CD8-Positive T-Lymphocytes/immunology
- CD8-Positive T-Lymphocytes/metabolism
- Cell Proliferation
- Dinoprostone/metabolism
- Down-Regulation
- Ferroptosis
- Interleukin Receptor Common gamma Subunit/biosynthesis
- Interleukin Receptor Common gamma Subunit/deficiency
- Interleukin Receptor Common gamma Subunit/metabolism
- Interleukin-2/antagonists & inhibitors
- Interleukin-2/immunology
- Interleukin-2/metabolism
- Interleukin-2 Receptor beta Subunit/metabolism
- Lymphocytes, Tumor-Infiltrating/cytology
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Mitochondria/metabolism
- Oxidative Stress
- Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha/metabolism
- Receptors, Prostaglandin E, EP2 Subtype/metabolism
- Receptors, Prostaglandin E, EP2 Subtype/antagonists & inhibitors
- Receptors, Prostaglandin E, EP4 Subtype/metabolism
- Receptors, Prostaglandin E, EP4 Subtype/antagonists & inhibitors
- Signal Transduction
- TOR Serine-Threonine Kinases/metabolism
- Tumor Microenvironment/immunology
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Affiliation(s)
- Matteo Morotti
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Alizee J Grimm
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Helen Carrasco Hope
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Mathieu Desbuisson
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Nicolas Rayroux
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - David Barras
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Maria Masid
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Baptiste Murgues
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Bovannak S Chap
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Marco Ongaro
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Ioanna A Rota
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Catherine Ronet
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Aspram Minasyan
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Sebastian B Lacher
- Institute of Molecular Immunology, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
| | - Sara Bobisse
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Clément Murgues
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Eleonora Ghisoni
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Khaoula Ouchen
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Ribal Bou Mjahed
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Fabrizio Benedetti
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Naoill Abdellaoui
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Riccardo Turrini
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippe O Gannon
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Khalil Zaman
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Patrice Mathevet
- Department of Gynaecology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Loic Lelievre
- Department of Gynaecology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Isaac Crespo
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Marcus Conrad
- Institute of Metabolism and Cell Death, Molecular Target and Therapeutics Centre, Helmholtz Munich, Neuherberg, Germany
| | - Gregory Verdeil
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Lana E Kandalaft
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Julien Dagher
- Unit of Translational Oncopathology, Institute of Pathology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Jesus Corria-Osorio
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Marie-Agnes Doucey
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Ping-Chih Ho
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
| | - Nicola Vannini
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Jan P Böttcher
- Institute of Molecular Immunology, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
| | - Denarda Dangaj Laniti
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland.
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, Switzerland.
| | - George Coukos
- Ludwig Institute for Cancer Research, Lausanne Branch, University of Lausanne (UNIL), Lausanne, Switzerland.
- Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, Switzerland.
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3
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Huang G, Wu Y, Gan H, Chu L. Overexpression of CD2/CD27 could inhibit the activation of nitrogen metabolism pathways and suppress M2 polarization of macrophages, thereby preventing brain metastasis of breast cancer. Transl Oncol 2023; 37:101768. [PMID: 37666207 PMCID: PMC10480780 DOI: 10.1016/j.tranon.2023.101768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/09/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023] Open
Abstract
OBJECTIVE Our study aimed to reveal the possible molecular mechanisms of CD2 and CD27 in influencing the tumor microenvironment of breast cancer (BC) brain metastasis based on the TCGA (The Cancer Genome Atlas) and SRA (Sequence Read Archive) databases. METHODS We calculated the proportions of tumor-infiltrating immune cells and the immune and stromal cell scores in 1222 BC samples from the TCGA-BRCA dataset, followed by identification of candidate DEGs. We further screened for BC brain metastasis-related DEGs in the BC brain metastasis dataset SUB12911144 from the SRA database. Finally, we established a mouse breast cancer brain metastasis model for in vivo validation. RESULTS We further screened two immune-regulatory DEGs (CD2 and CD27). GSEA analysis showed that the downregulation of CD2 and CD27 expression was closely related to the activation of nitrogen metabolism pathways. CIBERSORT algorithm analysis showed a correlation between the expression of 16 types of tumor-infiltrating immune cells and CD2 and 19 types of tumor-infiltrating immune cells and CD27. In addition, CD2 and CD27 expression were negatively associated with the proportion of M2 macrophages. In vivo experimental results demonstrated that overexpression of CD2/CD27 could suppress the M2 polarization of macrophages and inhibit breast cancer brain metastasis. CONCLUSION In the tumor microenvironment, overexpression of CD2/CD27 inhibited the activation of nitrogen metabolism pathways and suppressed M2 polarization of macrophages, thereby preventing brain metastasis of breast cancer.
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Affiliation(s)
- Guanyou Huang
- Department of Neurosurgery, The Second People's Hospital of Guiyang (Jinyang Hospital), No.547 Jinyang South Road, Guanshanhu District, Guiyang 550081, China.
| | - Yujuan Wu
- Department of Neurology, The Second People's Hospital of Guiyang (Jinyang Hospital), No.547 Jinyang South Road, Guanshanhu District, Guiyang 550081, China
| | - Hongchuan Gan
- Department of Neurosurgery, The Second People's Hospital of Guiyang (Jinyang Hospital), No.547 Jinyang South Road, Guanshanhu District, Guiyang 550081, China
| | - Liangzhao Chu
- Department of Neurosurgery, The Affiliated Hospital of Guizhou Medical University, Guiyang 550001, China
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4
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Sciacovelli M, Dugourd A, Jimenez LV, Yang M, Nikitopoulou E, Costa ASH, Tronci L, Caraffini V, Rodrigues P, Schmidt C, Ryan DG, Young T, Zecchini VR, Rossi SH, Massie C, Lohoff C, Masid M, Hatzimanikatis V, Kuppe C, Von Kriegsheim A, Kramann R, Gnanapragasam V, Warren AY, Stewart GD, Erez A, Vanharanta S, Saez-Rodriguez J, Frezza C. Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression. Nat Commun 2022; 13:7830. [PMID: 36539415 PMCID: PMC9767928 DOI: 10.1038/s41467-022-35036-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/16/2022] [Indexed: 12/24/2022] Open
Abstract
Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.
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Affiliation(s)
- Marco Sciacovelli
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
- Department of Molecular and Clinical Cancer Medicine; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 3GE, UK
| | - Aurelien Dugourd
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Lorea Valcarcel Jimenez
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
- CECAD Research Center, Faculty of Medicine-University Hospital Cologne, 50931, Cologne, Germany
| | - Ming Yang
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
- CECAD Research Center, Faculty of Medicine-University Hospital Cologne, 50931, Cologne, Germany
| | - Efterpi Nikitopoulou
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Ana S H Costa
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
- Matterworks, Somerville, MA, 02143, USA
| | - Laura Tronci
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Veronica Caraffini
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Paulo Rodrigues
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Christina Schmidt
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
- CECAD Research Center, Faculty of Medicine-University Hospital Cologne, 50931, Cologne, Germany
| | - Dylan Gerard Ryan
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Timothy Young
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Vincent R Zecchini
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Sabrina H Rossi
- Early Detection Programme, CRUK Cambridge Centre, Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Charlie Massie
- Early Detection Programme, CRUK Cambridge Centre, Department of Oncology, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Caroline Lohoff
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
| | - Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Department of Oncology, Lausanne University Hospital (CHUV), University of Lausanne, CH-1011, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
- Division of Nephrology and Clinical Immunology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Alex Von Kriegsheim
- Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine, Crewe Road South, Edinburgh, EH4 2XR, UK
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
- Division of Nephrology and Clinical Immunology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Vincent Gnanapragasam
- Department of Surgery, University of Cambridge and Cambridge University Hospitals NHS Cambridge Biomedical Campus, Cambridge, UK
| | - Anne Y Warren
- Department of Histopathology-Cambridge University Hospitals NHS, Box 235 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge and Cambridge University Hospitals NHS Cambridge Biomedical Campus, Cambridge, UK
| | - Ayelet Erez
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sakari Vanharanta
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK
- Translational Cancer Medicine Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Physiology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Julio Saez-Rodriguez
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany.
| | - Christian Frezza
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Box 197 Biomedical Campus, Cambridge, CB2 0XZ, UK.
- CECAD Research Center, Faculty of Medicine-University Hospital Cologne, 50931, Cologne, Germany.
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5
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Küken A, Langary D, Nikoloski Z. The hidden simplicity of metabolic networks is revealed by multireaction dependencies. SCIENCE ADVANCES 2022; 8:eabl6962. [PMID: 35353565 PMCID: PMC8967225 DOI: 10.1126/sciadv.abl6962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.
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Affiliation(s)
- Anika Küken
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Damoun Langary
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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6
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Lee SM, Lee G, Kim HU. Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models. Comput Struct Biotechnol J 2022; 20:3041-3052. [PMID: 35782748 PMCID: PMC9218235 DOI: 10.1016/j.csbj.2022.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 11/30/2022] Open
Abstract
Genome-scale metabolic model (GEM) has been established as an important tool to study cellular metabolism at a systems level by predicting intracellular fluxes. With the advent of generic human GEMs, they have been increasingly applied to a range of diseases, often for the objective of predicting effective metabolic drug targets. Cancer is a representative disease where the use of GEMs has proved to be effective, partly due to the massive availability of patient-specific RNA-seq data. When using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). However, while MEMs have been thoroughly examined, MSMs have not been systematically examined, especially, when studying cancer metabolism. In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patient-specific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy observations were made from the evaluation, including the high performance of two MEMs, namely rank-based ‘task-driven Integrative Network Inference for Tissues’ (tINIT) or ‘Gene Inactivity Moderated by Metabolism and Expression’ (GIMME), paired with least absolute deviation (LAD) as a MSM, and relatively poorer performance of flux balance analysis (FBA) and parsimonious FBA (pFBA). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.
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Herrmann HA, Rusz M, Baier D, Jakupec MA, Keppler BK, Berger W, Koellensperger G, Zanghellini J. Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13164130. [PMID: 34439283 PMCID: PMC8391396 DOI: 10.3390/cancers13164130] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/23/2021] [Accepted: 08/03/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Cancer, but also its treatment, can lead to a reprogramming of cellular metabolism. These changes are observable in metabolite abundances, which can be unbiasedly measured via mass spectrometry metabolomics. However, even when the metabolome changes strongly, a (mechanistic) interpretation is difficult as metabolite levels do not necessarily directly correspond to pathway activities. Here we measure the changes of the cellular metabolome in colorectal cancer cell lines sensitive and resistant to the ruthenium-based drug BOLD-100/KP1339 and the platinum-based drug oxaliplatin. We map these changes onto a cancer-specific genome-scale metabolic model, which allows us not only to compute intracellular flux distributions, but also to disentangle drug-specific effects from growth differences from differences in metabolic adaptations due to resistance. Specifically, we find that resistance to BOLD-100/KP1339 induces more extensive reprogramming than oxaliplatin, especially with respect to fatty acid and amino acid metabolism. Abstract Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.
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Affiliation(s)
- Helena A. Herrmann
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
| | - Mate Rusz
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Dina Baier
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Michael A. Jakupec
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Bernhard K. Keppler
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Walter Berger
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
- Institute of Cancer Research and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Vienna Metabolomics Center (VIME), University of Vienna, 1090 Vienna, Austria
- Research Network Chemistry Meets Microbiology, University of Vienna, 1090 Vienna, Austria
- Correspondence: (G.K.); (J.Z.)
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Correspondence: (G.K.); (J.Z.)
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Gawthrop PJ, Pan M, Crampin EJ. Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data. J R Soc Interface 2021; 18:20210478. [PMID: 34428949 PMCID: PMC8385351 DOI: 10.1098/rsif.2021.0478] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/02/2021] [Indexed: 12/14/2022] Open
Abstract
Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.
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Affiliation(s)
- Peter J. Gawthrop
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Chemical and Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Chemical and Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
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Thermodynamics of Metabolic Pathways. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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10
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Ramirez-Malule H, López-Agudelo VA, Gómez-Ríos D, Ochoa S, Ríos-Estepa R, Junne S, Neubauer P. TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus. Bioengineering (Basel) 2021; 8:103. [PMID: 34436106 PMCID: PMC8389198 DOI: 10.3390/bioengineering8080103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/04/2021] [Accepted: 07/16/2021] [Indexed: 11/26/2022] Open
Abstract
Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce clavulanic acid (CA), a potent inhibitor of β-lactamase enzymes. In this study, S. clavuligerus cultivated in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using a bottom-up approach and validated using experimental data. The reduced model was implemented for in silico studies of the metabolic scenarios arisen during the cultivations. Constraint-based analysis confirmed the interrelations between succinate, oxaloacetate, malate, pyruvate, and acetate accumulations at high CA synthesis rates in submerged cultures of S. clavuligerus. Further analysis using shadow prices provided a first view of the metabolites positive and negatively associated with the scenarios of low and high CA production.
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Affiliation(s)
| | | | - David Gómez-Ríos
- Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín 050010, Colombia; (D.G.-R.); (S.O.)
| | - Silvia Ochoa
- Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín 050010, Colombia; (D.G.-R.); (S.O.)
| | - Rigoberto Ríos-Estepa
- Escuela de Biociencias, Universidad Nacional de Colombia sede Medellín, Medellín 050010, Colombia;
| | - Stefan Junne
- Chair of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, D-13355 Berlin, Germany; (S.J.); (P.N.)
| | - Peter Neubauer
- Chair of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, D-13355 Berlin, Germany; (S.J.); (P.N.)
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11
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Seep L, Razaghi-Moghadam Z, Nikoloski Z. Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis. Sci Rep 2021; 11:8544. [PMID: 33879809 PMCID: PMC8058346 DOI: 10.1038/s41598-021-87643-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/26/2021] [Indexed: 11/13/2022] Open
Abstract
Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation [Formula: see text], of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown [Formula: see text]. However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown [Formula: see text] whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown [Formula: see text] are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.
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Affiliation(s)
- Lea Seep
- Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany.
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12
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Johnson ME, Chen A, Faeder JR, Henning P, Moraru II, Meier-Schellersheim M, Murphy RF, Prüstel T, Theriot JA, Uhrmacher AM. Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry. Mol Biol Cell 2021; 32:186-210. [PMID: 33237849 PMCID: PMC8120688 DOI: 10.1091/mbc.e20-08-0530] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/13/2020] [Accepted: 11/17/2020] [Indexed: 12/29/2022] Open
Abstract
Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.
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Affiliation(s)
- M. E. Johnson
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - A. Chen
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - J. R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260
| | - P. Henning
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
| | - I. I. Moraru
- Department of Cell Biology, Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030
| | - M. Meier-Schellersheim
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - R. F. Murphy
- Computational Biology Department, Department of Biological Sciences, Department of Biomedical Engineering, Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15289
| | - T. Prüstel
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - J. A. Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195
| | - A. M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
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13
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Deepa Maheshvare M, Raha S, Pal D. A Graph-Based Framework for Multiscale Modeling of Physiological Transport. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:802881. [PMID: 36925576 PMCID: PMC10013063 DOI: 10.3389/fnetp.2021.802881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022]
Abstract
Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability due to the use of continuum approaches in which it is practically impossible to encapsulate the complexity of the physiological processes occurring at diverse scales. Here, we present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graph-based metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. We predict glucose drift governed by advective-dispersive transport in the vascular subdomains of an islet vasculature and cross-validate the flow and concentration fields with finite-element-based COMSOL simulations. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multi-omics data from in vitro-in vivo studies and vascular topology from imaging studies for examining the structure-function relationship of complex vasculatures. The framework can advance simulation of whole-body networks at user-defined levels and is expected to find major use in personalized medicine and drug discovery.
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Affiliation(s)
- M Deepa Maheshvare
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
| | - Soumyendu Raha
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
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