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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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Gopalakrishnan S, Johnson W, Valderrama-Gomez MA, Icten E, Tat J, Lay F, Diep J, Gomez N, Stevens J, Schlegel F, Rolandi P, Kontoravdi C, Lewis NE. Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks. Metab Eng 2024:S1096-7176(24)00099-5. [PMID: 39047894 DOI: 10.1016/j.ymben.2024.07.009] [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: 07/06/2023] [Revised: 07/18/2024] [Accepted: 07/22/2024] [Indexed: 07/27/2024]
Abstract
Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.
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Affiliation(s)
| | | | | | | | - Jasmine Tat
- Process Development, Amgen; Department of Bioengineering, University of California San Diego
| | | | | | | | | | | | | | | | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego; Department of Bioengineering, University of California San Diego.
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3
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Park SY, Choi DH, Song J, Lakshmanan M, Richelle A, Yoon S, Kontoravdi C, Lewis NE, Lee DY. Driving towards digital biomanufacturing by CHO genome-scale models. Trends Biotechnol 2024:S0167-7799(24)00065-9. [PMID: 38548556 DOI: 10.1016/j.tibtech.2024.03.001] [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: 01/01/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 05/20/2024]
Abstract
Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.
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Affiliation(s)
- Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Jinsung Song
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Meiyappan Lakshmanan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems Medicine (IBSE), Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Anne Richelle
- Sartorius Corporate Research, Avenue Ariane 5, 1200 Brussels, Belgium
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA 01850, USA
| | - Cleo Kontoravdi
- Department of Chemical Engineering and Chemical Technology, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Nathan E Lewis
- Departments of Pediatrics and Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea.
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4
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Sun T, Xiang Y, Turner F, Bao X. Integrated Experimental and Mathematical Exploration of Modular Tissue Cultures for Developmental Engineering. Int J Mol Sci 2024; 25:2987. [PMID: 38474234 DOI: 10.3390/ijms25052987] [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: 12/21/2023] [Revised: 02/06/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Developmental engineering (DE) involves culturing various cells on modular scaffolds (MSs), yielding modular tissues (MTs) assembled into three-dimensional (3D) tissues, mimicking developmental biology. This study employs an integrated approach, merging experimental and mathematical methods to investigate the biological processes in MT cultivation and assembly. Human dermal fibroblasts (HDFs) were cultured on tissue culture plastics, poly(lactic acid) (PLA) discs with regular open structures, or spherical poly(methyl methacrylate) (PMMA) MSs, respectively. Notably, HDFs exhibited flattened spindle shapes when adhered to solid surfaces, and complex 3D structures when migrating into the structured voids of PLA discs or interstitial spaces between aggregated PMMA MSs, showcasing coordinated colonization of porous scaffolds. Empirical investigations led to power law models simulating density-dependent cell growth on solid surfaces or voids. Concurrently, a modified diffusion model was applied to simulate oxygen diffusion within tissues cultured on solid surfaces or porous structures. These mathematical models were subsequently combined to explore the influences of initial cell seeding density, culture duration, and oxygen diffusion on MT cultivation and assembly. The findings underscored the intricate interplay of factors influencing MT design for tissue assembly. The integrated approach provides insights into mechanistic aspects, informing bioprocess design for manufacturing MTs and 3D tissues in DE.
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Affiliation(s)
- Tao Sun
- Department of Chemical Engineering, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
| | - Yu Xiang
- Department of Materials, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
| | - Freya Turner
- Department of Chemical Engineering, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
| | - Xujin Bao
- Department of Materials, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
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Nausch H, Baldan M, Teichert K, Lutz J, Claussen C, Bortz M, Buyel JF. Simulation and optimization of nutrient uptake and biomass formation using a multi-parameter Monod-type model of tobacco BY-2 cell suspension cultures in a stirred-tank bioreactor. FRONTIERS IN PLANT SCIENCE 2023; 14:1183254. [PMID: 38126010 PMCID: PMC10731461 DOI: 10.3389/fpls.2023.1183254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/27/2023] [Indexed: 12/23/2023]
Abstract
Introduction Tobacco (Nicotiana tabacum) cv Bright Yellow-2 (BY-2) cell suspension cultures enable the rapid production of complex protein-based biopharmaceuticals but currently achieve low volumetric productivity due to slow biomass formation. The biomass yield can be improved with tailored media, which can be designed either by laborious trial-and-error experiments or systematic, rational design using mechanistic models, linking nutrient consumption and biomass formation. Methods Here we developed an iterative experiment-modeling-optimization workflow to gradually refine such a model and its predictions, based on collected data concerning BY-2 cell macronutrient consumption (sucrose, ammonium, nitrate and phosphate) and biomass formation. Results and discussion The biomass formation was well predicted by an unstructured segregated mechanistic Monod-type model as long as the nutrient concentrations did not approach zero (we omitted phosphate, which was completely depleted). Multi-criteria optimization for sucrose and biomass formation indicated the best tradeoff (in a Paretian sense) between maximum biomass yield and minimum process time by reducing the initial sucrose concentration, whereas the inoculation biomass could be increased to maximize the biomass yield or minimize the process time, which we confirmed in calibration experiments. The model became inaccurate at biomass densities > 8 g L-1 dry mass when sucrose was almost depleted. We compensated for this limitation by including glucose and fructose as sucrose hydrolysis products in the model. The remaining offset between the simulation and experimental data might be resolved by including intracellular pools of sucrose, ammonium, nitrate and phosphate. Overall, we demonstrated that iterative models can be used to systematically optimize conditions for bioreactor-based processes.
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Affiliation(s)
- Henrik Nausch
- Department Bioprocess Engineering, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
| | - Marco Baldan
- Division Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Katrin Teichert
- Division Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Jannik Lutz
- Department Bioprocess Engineering, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
| | - Carsten Claussen
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Hamburg, Germany
| | - Michael Bortz
- Division Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Johannes Felix Buyel
- Department Bioprocess Engineering, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
- Institute for Molecular Biotechnology, RWTH Aachen University, Aachen, Germany
- Institute of Bioprocess Science and Engineering (IBSE), University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
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6
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Reddy JV, Raudenbush K, Papoutsakis ET, Ierapetritou M. Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation. Biotechnol Adv 2023; 67:108179. [PMID: 37257729 DOI: 10.1016/j.biotechadv.2023.108179] [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: 11/27/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023]
Abstract
In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.
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Affiliation(s)
- Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Katherine Raudenbush
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Eleftherios Terry Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA; Delaware Biotechnology Institute, Department of Biological Sciences, University of Delaware, USA.
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA.
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7
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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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8
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Choi YM, Choi DH, Lee YQ, Koduru L, Lewis NE, Lakshmanan M, Lee DY. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations. Comput Struct Biotechnol J 2023; 21:3736-3745. [PMID: 37547082 PMCID: PMC10400880 DOI: 10.1016/j.csbj.2023.07.025] [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: 03/17/2023] [Revised: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.
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Affiliation(s)
- Yoon-Mi Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Yi Qing Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Lokanand Koduru
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A⁎STAR), Singapore
| | - Nathan E. Lewis
- Departments of Pediatrics and Bioengineering, University of California, La Jolla, San Diego, USA
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), Singapore
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Republic of Korea
- Bitwinners Pte. Ltd., Singapore
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Sevrin T, Strasser L, Ternet C, Junk P, Caffarini M, Prins S, D’Arcy C, Catozzi S, Oliviero G, Wynne K, Kiel C, Luthert PJ. Whole-cell energy modeling reveals quantitative changes of predicted energy flows in RAS mutant cancer cell lines. iScience 2023; 26:105931. [PMID: 36711246 PMCID: PMC9874014 DOI: 10.1016/j.isci.2023.105931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/27/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Cellular utilization of available energy flows to drive a multitude of forms of cellular "work" is a major biological constraint. Cells steer metabolism to address changing phenotypic states but little is known as to how bioenergetics couples to the richness of processes in a cell as a whole. Here, we outline a whole-cell energy framework that is informed by proteomic analysis and an energetics-based gene ontology. We separate analysis of metabolic supply and the capacity to generate high-energy phosphates from a representation of demand that is built on the relative abundance of ATPases and GTPases that deliver cellular work. We employed mouse embryonic fibroblast cell lines that express wild-type KRAS or oncogenic mutations and with distinct phenotypes. We observe shifts between energy-requiring processes. Calibrating against Seahorse analysis, we have created a whole-cell energy budget with apparent predictive power, for instance in relation to protein synthesis.
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Affiliation(s)
- Thomas Sevrin
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Lisa Strasser
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Camille Ternet
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Philipp Junk
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Miriam Caffarini
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Stella Prins
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London EC1V 9EL, UK
| | - Cian D’Arcy
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Simona Catozzi
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Giorgio Oliviero
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Christina Kiel
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
- Department of Molecular Medicine, University of Pavia, 27100 Pavia, Italy
- Corresponding author
| | - Philip J. Luthert
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London EC1V 9EL, UK
- NIHR Moorfields Biomedical Research Centre, University College London, 11-43 Bath Street, London EC1V 9EL, UK
- Corresponding author
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10
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Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, Kontoravdi C, Lewis NE. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng 2023; 75:181-191. [PMID: 36566974 PMCID: PMC10258867 DOI: 10.1016/j.ymben.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
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Affiliation(s)
| | - Chintan J Joshi
- Department of Pediatrics, University of California San Diego, United States
| | | | - Elcin Icten
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - William Johnson
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, United States; Department of Bioengineering, University of California San Diego, United States.
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11
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Magazzù G, Zampieri G, Angione C. Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods. Comput Biol Med 2022; 151:106244. [PMID: 36343407 DOI: 10.1016/j.compbiomed.2022.106244] [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: 06/15/2022] [Revised: 10/07/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far. METHODS Here, we propose and implement an interpretable multi-omics pipeline that combines mechanistic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma. RESULTS AND CONCLUSIONS While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics.
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Affiliation(s)
- Giuseppe Magazzù
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom
| | - Guido Zampieri
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Department of Biology, University of Padova, Padova, Italy
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Centre for Digital Innovation, Teesside University, Middlesbrough, England, United Kingdom; National Horizons Centre, Teesside University, Darlington, England, United Kingdom.
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12
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Narayanan H, Luna M, Sokolov M, Butté A, Morbidelli M. Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Martin Luna
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | | | | | - Massimo Morbidelli
- DataHow AG, Zürichstrasse 137, 8600 Dübendorf, Switzerland
- Dipartimento di Chimica, Materiali e Ingegneria Chimica, Giulio Natta, Politecnico di Milano, 20131 Milano, Italy
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13
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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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14
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Samoudi M, Masson HO, Kuo CC, Robinson CM, Lewis NE. From omics to Cellular mechanisms in mammalian cell factory development. Curr Opin Chem Eng 2021; 32:100688. [PMID: 37475722 PMCID: PMC10357924 DOI: 10.1016/j.coche.2021.100688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mammalian cells have been used widely as biopharmaceutical cell factories due to their ability to make complex biotherapeutic proteins with human-compatible modifications. However, their application for some products has been hampered by low protein yields. Numerous studies have aimed to characterize cellular bottlenecks in the hope of boosting protein productivity, but the complexity of the underlying pathways and the diversity of the modifications have complicated cell engineering when relying solely on traditional methodologies. Incorporating omics-based and systems approaches into cell engineering can provide valuable insights into desirable phenotypes of cell factories. Here, we discuss cell engineering strategies for enhancing protein productivity in mammalian cell factories, particularly CHO and HEK293, and the opportunities and limitations of the genome-wide screening and multi-omics approaches for guiding cell engineering. Systems biology strategies will also be discussed to show how they refine our understanding of the cellular mechanisms which will aid in effective engineering strategies.
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Affiliation(s)
- Mojtaba Samoudi
- Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
| | - Helen O. Masson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Chih-Chung Kuo
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
| | - Caressa M Robinson
- Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, California, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
- National Biologics Facility, Technical University of Denmark, Kgs. Lyngby, Denmark
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