1
|
Lin DW, Khattar S, Chandrasekaran S. Metabolic Objectives and Trade-Offs: Inference and Applications. Metabolites 2025; 15:101. [PMID: 39997726 PMCID: PMC11857637 DOI: 10.3390/metabo15020101] [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: 12/19/2024] [Revised: 01/28/2025] [Accepted: 01/29/2025] [Indexed: 02/26/2025] Open
Abstract
Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology.
Collapse
Affiliation(s)
- Da-Wei Lin
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA;
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Saanjh Khattar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Sriram Chandrasekaran
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA;
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA;
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| |
Collapse
|
2
|
Valcárcel LV, San José-Enériz E, Ordoñez R, Apaolaza I, Olaverri-Mendizabal D, Barrena N, Valcárcel A, Garate L, San Miguel J, Pineda-Lucena A, Agirre X, Prósper F, Planes FJ. An automated network-based tool to search for metabolic vulnerabilities in cancer. Nat Commun 2024; 15:8685. [PMID: 39394196 PMCID: PMC11470099 DOI: 10.1038/s41467-024-52725-4] [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: 06/22/2022] [Accepted: 09/18/2024] [Indexed: 10/13/2024] Open
Abstract
The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. A detailed illustration of gmctool is presented for multiple myeloma (MM), an incurable hematological malignancy. We provide in vitro experimental evidence for the essentiality of CTPS1 (CTPS synthase) and UAP1 (UDP-N-Acetylglucosamine Pyrophosphorylase 1) in specific MM patient subgroups.
Collapse
Affiliation(s)
- Luis V Valcárcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Edurne San José-Enériz
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, 28029, Madrid, Spain
| | - Raquel Ordoñez
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, 28029, Madrid, Spain
| | - Iñigo Apaolaza
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Danel Olaverri-Mendizabal
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Naroa Barrena
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Ana Valcárcel
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
| | - Leire Garate
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, 28029, Madrid, Spain
| | - Jesús San Miguel
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, 28029, Madrid, Spain
- Departmento de Hematología, Clínica Universidad de Navarra and CCUN, Universidad de Navarra, Avenida Pío XII 36, 31008, Pamplona, Spain
| | - Antonio Pineda-Lucena
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, 28029, Madrid, Spain
| | - Xabier Agirre
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, 28029, Madrid, Spain
| | - Felipe Prósper
- Hemato-Oncology Program, Center for Applied Medical Research (CIMA), Universidad de Navarra, IDISNA, CCUN, Avenida Pío XII 55, 31008, Pamplona, Spain.
- CIBERONC Centro de Investigación Biomédica en Red de Cáncer, 28029, Madrid, Spain.
- Departmento de Hematología, Clínica Universidad de Navarra and CCUN, Universidad de Navarra, Avenida Pío XII 36, 31008, Pamplona, Spain.
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- Biomedical Engineering Center, University of Navarra, 31008, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31008, Pamplona, Spain.
| |
Collapse
|
3
|
Kishk A, Pires Pacheco M, Heurtaux T, Sauter T. Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas. Brief Bioinform 2024; 25:bbae199. [PMID: 38701414 PMCID: PMC11066901 DOI: 10.1093/bib/bbae199] [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: 01/15/2024] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.
Collapse
Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Centre of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| |
Collapse
|
4
|
Alruwaili O, Yousef A, Jumani TA, Armghan A. Response score-based protein structure analysis for cancer prediction aided by the Internet of Things. Sci Rep 2024; 14:2324. [PMID: 38282060 PMCID: PMC10822874 DOI: 10.1038/s41598-024-52634-y] [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: 11/28/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024] Open
Abstract
Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.
Collapse
Affiliation(s)
- Omar Alruwaili
- Department of Computer Engineering and Networks, College of Computer and Information Science, Jouf University, 72388, Sakaka, Saudi Arabia
| | - Amr Yousef
- Electrical Engineering Department, University of Business and Technology, 23435, Ar Rawdah, Jeddah, Saudi Arabia
- Engineering Mathematics Department, Alexandria University, Lotfy El-Sied St. Off Gamal Abd El-Naser, Alexandria, 11432, Egypt
| | - Touqeer A Jumani
- Department of Electrical Engineering, Mehran University of Engineering and Technology, SZAB Campus, Khairpur Mirs, 66020, Pakistan
| | - Ammar Armghan
- Department of Electrical Engineering. College of Engineering, Jouf University, 72388, Sakaka, Saudi Arabia.
| |
Collapse
|
5
|
Bessell B, Loecker J, Zhao Z, Aghamiri SS, Mohanty S, Amin R, Helikar T, Puniya BL. COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery. Brief Bioinform 2023; 24:bbad387. [PMID: 37930022 PMCID: PMC10627799 DOI: 10.1093/bib/bbad387] [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: 06/21/2023] [Revised: 09/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.
Collapse
Affiliation(s)
- Brandt Bessell
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Josh Loecker
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Zhongyuan Zhao
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | | | | | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | | |
Collapse
|
6
|
Vera-Siguenza E, Escribano-Gonzalez C, Serrano-Gonzalo I, Eskla KL, Spill F, Tennant D. Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. PLoS Comput Biol 2023; 19:e1011374. [PMID: 37713666 PMCID: PMC10503963 DOI: 10.1371/journal.pcbi.1011374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 07/19/2023] [Indexed: 09/17/2023] Open
Abstract
It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.
Collapse
Affiliation(s)
- Elias Vera-Siguenza
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Watson School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Cristina Escribano-Gonzalez
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Irene Serrano-Gonzalo
- Instituto de Investigación Sanitaria Aragón, Fundación Española para el Estudio y Terapéutica de la enfermedad de Gaucher y otras Lisosomales, Zaragoza, España
| | - Kattri-Liis Eskla
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Fabian Spill
- Watson School of Mathematics, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Tennant
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| |
Collapse
|
7
|
Bintener T, Pacheco MP, Philippidou D, Margue C, Kishk A, Del Mistro G, Di Leo L, Moscardó Garcia M, Halder R, Sinkkonen L, De Zio D, Kreis S, Kulms D, Sauter T. Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma. Cell Death Dis 2023; 14:468. [PMID: 37495601 PMCID: PMC10372000 DOI: 10.1038/s41419-023-05955-1] [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: 10/03/2022] [Revised: 06/12/2023] [Accepted: 07/05/2023] [Indexed: 07/28/2023]
Abstract
Despite high initial response rates to targeted kinase inhibitors, the majority of patients suffering from metastatic melanoma present with high relapse rates, demanding for alternative therapeutic options. We have previously developed a drug repurposing workflow to identify metabolic drug targets that, if depleted, inhibit the growth of cancer cells without harming healthy tissues. In the current study, we have applied a refined version of the workflow to specifically predict both, common essential genes across various cancer types, and melanoma-specific essential genes that could potentially be used as drug targets for melanoma treatment. The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available, and in-house, large-scale transcriptomic data metabolic models for melanoma were reconstructed enabling the prediction of 28 candidate drugs and estimating their respective efficacy. Twelve highly efficacious drugs with low half-maximal inhibitory concentration values for the treatment of other cancers, which are not yet approved for melanoma treatment, were used for in vitro validation using melanoma cell lines. Combination of the top 4 out of 6 promising candidate drugs with BRAF or MEK inhibitors, partially showed synergistic growth inhibition compared to individual BRAF/MEK inhibition. Hence, the repurposing of drugs may enable an increase in therapeutic options e.g., for non-responders or upon acquired resistance to conventional melanoma treatments.
Collapse
Affiliation(s)
- Tamara Bintener
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Demetra Philippidou
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Christiane Margue
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Greta Del Mistro
- Experimental Dermatology, Department of Dermatology, TU-Dresden, Dresden, Germany
- National Center for Tumour Diseases, TU-Dresden, Dresden, Germany
| | - Luca Di Leo
- Melanoma Research Team, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Maria Moscardó Garcia
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Daniela De Zio
- Melanoma Research Team, Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephanie Kreis
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Dagmar Kulms
- Experimental Dermatology, Department of Dermatology, TU-Dresden, Dresden, Germany
- National Center for Tumour Diseases, TU-Dresden, Dresden, Germany
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg.
| |
Collapse
|
8
|
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: 9] [Impact Index Per Article: 4.5] [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.
Collapse
|
9
|
Strain B, Morrissey J, Antonakoudis A, Kontoravdi C. Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems. Comput Struct Biotechnol J 2023; 21:1543-1549. [PMID: 36879884 PMCID: PMC9984296 DOI: 10.1016/j.csbj.2023.02.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/06/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.
Collapse
Affiliation(s)
- Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - James Morrissey
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| |
Collapse
|