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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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2
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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Angarita-Rodríguez A, González-Giraldo Y, Rubio-Mesa JJ, Aristizábal AF, Pinzón A, González J. Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets. Int J Mol Sci 2023; 25:365. [PMID: 38203536 PMCID: PMC10778851 DOI: 10.3390/ijms25010365] [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: 10/21/2023] [Revised: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.
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Affiliation(s)
- Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Yeimy González-Giraldo
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Juan J. Rubio-Mesa
- Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Andrés Felipe Aristizábal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
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Mirveis Z, Howe O, Cahill P, Patil N, Byrne HJ. Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives. Metabolomics 2023; 19:67. [PMID: 37482587 PMCID: PMC10363518 DOI: 10.1007/s11306-023-02031-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge. AIM This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway. KEY SCIENTIFIC CONCEPTS Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.
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Affiliation(s)
- Zohreh Mirveis
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland.
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological, Health and Sport Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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5
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Janoska A, Buijs J, van Gulik WM. Predicting the influence of combined oxygen and glucose gradients based on scale-down and modelling approaches for the scale-up of penicillin fermentations. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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6
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Chung WY, Zhu Y, Mahamad Maifiah MH, Hawala Shivashekaregowda NK, Wong EH, Abdul Rahim N. Exogenous metabolite feeding on altering antibiotic susceptibility in Gram-negative bacteria through metabolic modulation: a review. Metabolomics 2022; 18:47. [PMID: 35781167 DOI: 10.1007/s11306-022-01903-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/06/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND The rise of antimicrobial resistance at an alarming rate is outpacing the development of new antibiotics. The worrisome trends of multidrug-resistant Gram-negative bacteria have enormously diminished existing antibiotic activity. Antibiotic treatments may inhibit bacterial growth or lead to induce bacterial cell death through disruption of bacterial metabolism directly or indirectly. In light of this, it is imperative to have a thorough understanding of the relationship of bacterial metabolism with antimicrobial activity and leverage the underlying principle towards development of novel and effective antimicrobial therapies. OBJECTIVE Herein, we explore studies on metabolic analyses of Gram-negative pathogens upon antibiotic treatment. Metabolomic studies revealed that antibiotic therapy caused changes of metabolites abundance and perturbed the bacterial metabolism. Following this line of thought, addition of exogenous metabolite has been employed in in vitro, in vivo and in silico studies to activate the bacterial metabolism and thus potentiate the antibiotic activity. KEY SCIENTIFIC CONCEPTS OF REVIEW Exogenous metabolites were discovered to cause metabolic modulation through activation of central carbon metabolism and cellular respiration, stimulation of proton motive force, increase of membrane potential, improvement of host immune protection, alteration of gut microbiome, and eventually facilitating antibiotic killing. The use of metabolites as antimicrobial adjuvants may be a promising approach in the fight against multidrug-resistant pathogens.
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Affiliation(s)
- Wan Yean Chung
- School of Pharmacy, Taylor's University, 47500, Subang Jaya, Selangor, Malaysia
| | - Yan Zhu
- Biomedicine Discovery Institute, Infection and Immunity Program, Department of Microbiology, Monash University, 3800, Victoria, Australia
| | - Mohd Hafidz Mahamad Maifiah
- International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), 53100, Jalan Gombak, Selangor, Malaysia
| | - Naveen Kumar Hawala Shivashekaregowda
- Center for Drug Discovery and Molecular Pharmacology (CDDMP), Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Selangor, Malaysia
| | - Eng Hwa Wong
- School of Medicine, Taylor's University, 47500, Subang Jaya, Selangor, Malaysia.
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Lao-Martil D, Verhagen KJA, Schmitz JPJ, Teusink B, Wahl SA, van Riel NAW. Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities. Metabolites 2022; 12:74. [PMID: 35050196 PMCID: PMC8779790 DOI: 10.3390/metabo12010074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 11/23/2022] Open
Abstract
Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.
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Affiliation(s)
- David Lao-Martil
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands;
| | - Koen J. A. Verhagen
- Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany; (K.J.A.V.); (S.A.W.)
| | - Joep P. J. Schmitz
- DSM Biotechnology Center, Alexander Fleminglaan 1, 2613 AX Delft, The Netherlands;
| | - Bas Teusink
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands;
| | - S. Aljoscha Wahl
- Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany; (K.J.A.V.); (S.A.W.)
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands;
- Amsterdam University Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
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8
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Schink SJ, Christodoulou D, Mukherjee A, Athaide E, Brunner V, Fuhrer T, Bradshaw GA, Sauer U, Basan M. Glycolysis/gluconeogenesis specialization in microbes is driven by biochemical constraints of flux sensing. Mol Syst Biol 2022; 18:e10704. [PMID: 34994048 PMCID: PMC8738977 DOI: 10.15252/msb.202110704] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/10/2021] [Accepted: 12/14/2021] [Indexed: 11/29/2022] Open
Abstract
Central carbon metabolism is highly conserved across microbial species, but can catalyze very different pathways depending on the organism and their ecological niche. Here, we study the dynamic reorganization of central metabolism after switches between the two major opposing pathway configurations of central carbon metabolism, glycolysis, and gluconeogenesis in Escherichia coli, Pseudomonas aeruginosa, and Pseudomonas putida. We combined growth dynamics and dynamic changes in intracellular metabolite levels with a coarse-grained model that integrates fluxes, regulation, protein synthesis, and growth and uncovered fundamental limitations of the regulatory network: After nutrient shifts, metabolite concentrations collapse to their equilibrium, rendering the cell unable to sense which direction the flux is supposed to flow through the metabolic network. The cell can partially alleviate this by picking a preferred direction of regulation at the expense of increasing lag times in the opposite direction. Moreover, decreasing both lag times simultaneously comes at the cost of reduced growth rate or higher futile cycling between metabolic enzymes. These three trade-offs can explain why microorganisms specialize for either glycolytic or gluconeogenic substrates and can help elucidate the complex growth patterns exhibited by different microbial species.
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Affiliation(s)
| | - Dimitris Christodoulou
- Systems Biology DepartmentHarvard Medical SchoolBostonMAUSA
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Avik Mukherjee
- Systems Biology DepartmentHarvard Medical SchoolBostonMAUSA
- Applied Mathematics DepartmentHarvard CollegeCambridgeMAUSA
| | - Edward Athaide
- Applied Mathematics DepartmentHarvard CollegeCambridgeMAUSA
| | - Viktoria Brunner
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Tobias Fuhrer
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Gary Andrew Bradshaw
- Laboratory of Systems PharmacologyHarvard Program in Therapeutic ScienceHarvard Medical SchoolBostonMAUSA
| | - Uwe Sauer
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Markus Basan
- Systems Biology DepartmentHarvard Medical SchoolBostonMAUSA
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9
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Evers B, Gerding A, Boer T, Heiner-Fokkema MR, Jalving M, Wahl SA, Reijngoud DJ, Bakker BM. Simultaneous Quantification of the Concentration and Carbon Isotopologue Distribution of Polar Metabolites in a Single Analysis by Gas Chromatography and Mass Spectrometry. Anal Chem 2021; 93:8248-8256. [PMID: 34060804 PMCID: PMC8253487 DOI: 10.1021/acs.analchem.1c01040] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
13C-isotope tracing is a frequently employed approach to study metabolic pathway activity. When combined with the subsequent quantification of absolute metabolite concentrations, this enables detailed characterization of the metabolome in biological specimens and facilitates computational time-resolved flux quantification. Classically, a 13C-isotopically labeled sample is required to quantify 13C-isotope enrichments and a second unlabeled sample for the quantification of metabolite concentrations. The rationale for a second unlabeled sample is that the current methods for metabolite quantification rely mostly on isotope dilution mass spectrometry (IDMS) and thus isotopically labeled internal standards are added to the unlabeled sample. This excludes the absolute quantification of metabolite concentrations in 13C-isotopically labeled samples. To address this issue, we have developed and validated a new strategy using an unlabeled internal standard to simultaneously quantify metabolite concentrations and 13C-isotope enrichments in a single 13C-labeled sample based on gas chromatography-mass spectrometry (GC/MS). The method was optimized for amino acids and citric acid cycle intermediates and was shown to have high analytical precision and accuracy. Metabolite concentrations could be quantified in small tissue samples (≥20 mg). Also, we applied the method on 13C-isotopically labeled mammalian cells treated with and without a metabolic inhibitor. We proved that we can quantify absolute metabolite concentrations and 13C-isotope enrichments in a single 13C-isotopically labeled sample.
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Affiliation(s)
- Bernard Evers
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands
| | - Albert Gerding
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.,Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
| | - Theo Boer
- Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
| | - M Rebecca Heiner-Fokkema
- Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
| | - Mathilde Jalving
- Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - S Aljoscha Wahl
- Department of Biotechnology, Applied Science Faculty, Delft University of Technology, van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Dirk-Jan Reijngoud
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands
| | - Barbara M Bakker
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands
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Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, Chimusa ER. Computational/in silico methods in drug target and lead prediction. Brief Bioinform 2020; 21:1663-1675. [PMID: 31711157 PMCID: PMC7673338 DOI: 10.1093/bib/bbz103] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/17/2019] [Accepted: 07/18/2019] [Indexed: 01/10/2023] Open
Abstract
Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.
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Affiliation(s)
- Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- African Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa
| | - Radia Hassan
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Christian D Bope
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Nicholas E Thomford
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana
| | - Anita Ghansah
- Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, PO Box LG 581, Legon, Ghana
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
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11
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Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review. Processes (Basel) 2020. [DOI: 10.3390/pr8080951] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.
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12
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Verhagen KJA, van Gulik WM, Wahl SA. Dynamics in redox metabolism, from stoichiometry towards kinetics. Curr Opin Biotechnol 2020; 64:116-123. [DOI: 10.1016/j.copbio.2020.01.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/05/2020] [Accepted: 01/06/2020] [Indexed: 12/12/2022]
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13
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Volkova S, Matos MRA, Mattanovich M, Marín de Mas I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites 2020; 10:E303. [PMID: 32722118 PMCID: PMC7465778 DOI: 10.3390/metabo10080303] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/08/2020] [Accepted: 07/22/2020] [Indexed: 01/05/2023] Open
Abstract
Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.
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Affiliation(s)
| | | | | | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; (S.V.); (M.R.A.M.); (M.M.)
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14
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Lopatkin AJ, Collins JJ. Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol 2020; 18:507-520. [DOI: 10.1038/s41579-020-0372-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/15/2020] [Indexed: 12/11/2022]
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15
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Zhang C, Too HP. Strategies for the Biosynthesis of Pharmaceuticals and Nutraceuticals in Microbes from Renewable Feedstock. Curr Med Chem 2020; 27:4613-4621. [PMID: 32048953 DOI: 10.2174/0929867327666200212121047] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 09/06/2018] [Accepted: 10/18/2018] [Indexed: 01/03/2023]
Abstract
BACKGROUNDS Abundant and renewable biomaterials serve as ideal substrates for the sustainable production of various chemicals, including natural products (e.g., pharmaceuticals and nutraceuticals). For decades, researchers have been focusing on how to engineer microorganisms and developing effective fermentation processes to overproduce these molecules from biomaterials. Despite many laboratory achievements, it remains a challenge to transform some of these into successful industrial applications. RESULTS Here, we review recent progress in strategies and applications in metabolic engineering for the production of natural products. Modular engineering methods, such as a multidimensional heuristic process markedly improve efficiencies in the optimization of long and complex biosynthetic pathways. Dynamic pathway regulation realizes autonomous adjustment and can redirect metabolic carbon fluxes to avoid the accumulation of toxic intermediate metabolites. Microbial co-cultivation bolsters the identification and overproduction of natural products by introducing competition or cooperation of different species. Efflux engineering is applied to reduce product toxicity or to overcome storage limitation and thus improves product titers and productivities. CONCLUSION Without dispute, many of the innovative methods and strategies developed are gradually catalyzing this transformation from the laboratory into the industry in the biosynthesis of natural products. Sometimes, it is necessary to combine two or more strategies to acquire additive or synergistic benefits. As such, we foresee a bright future of the biosynthesis of pharmaceuticals and nutraceuticals in microbes from renewable biomaterials.
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Affiliation(s)
- Congqiang Zhang
- Biotransformation Innovation Platform (BioTrans), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Heng-Phon Too
- Biotransformation Innovation Platform (BioTrans), Agency for Science, Technology and Research (A*STAR), Singapore
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16
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Harris T, Uppala S, Lev-Cohain N, Adler-Levy Y, Shaul D, Nardi-Schreiber A, Sapir G, Azar A, Gamliel A, Sosna J, Gomori JM, Katz-Brull R. Hyperpolarized product selective saturating-excitations for determination of changes in metabolic reaction rates in real-time. NMR IN BIOMEDICINE 2020; 33:e4189. [PMID: 31793111 DOI: 10.1002/nbm.4189] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 08/04/2019] [Accepted: 08/16/2019] [Indexed: 06/10/2023]
Abstract
Investigation of hyperpolarized substrate metabolism has been showing utility in real-time determination of in-cell and in vivo enzymatic activities. Intracellular reaction rates may vary during the course of a measurement, even on the very short time scales of visibility on hyperpolarized MR, due to many factors such as the availability of the substrate and co-factors in the intracellular space. Despite this potential variation, the kinetic analysis of hyperpolarized signals typically assumes that the same rate constant (and in many cases, the same rate) applies throughout the course of the reaction as observed via the build-up and decay of the hyperpolarized signals. We demonstrate here an acquisition approach that can null the need for such an assumption and enable the detection of instantaneous changes in the rate of the reaction during an ex vivo hyperpolarized investigation, (i.e. in the course of the decay of one hyperpolarized substrate dose administered to a viable tissue sample ex vivo). This approach utilizes hyperpolarized product selective saturating-excitation pulses. Similar pulses have been previously utilized in vivo for spectroscopic imaging. However, we show here favorable consequences to kinetic rate determinations in the preparations used. We implement this acquisition strategy for studies on perfused tissue slices and develop a theory that explains why this particular approach enables the determination of changes in enzymatic rates that are monitored via the chemical conversions of hyperpolarized substrates. Real-time changes in intracellular reaction rates are demonstrated in perfused brain, liver, and xenograft breast cancer tissue slices and provide another potential differentiation parameter for tissue characterization.
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Affiliation(s)
- Talia Harris
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Sivaranjan Uppala
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Naama Lev-Cohain
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Yael Adler-Levy
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - David Shaul
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Atara Nardi-Schreiber
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Gal Sapir
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Assad Azar
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Ayelet Gamliel
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Jacob Sosna
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - J Moshe Gomori
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
| | - Rachel Katz-Brull
- Department of Radiology, Hadassah Medical Center, Hebrew University of Jerusalem, The Faculty of Medicine, Jerusalem, Israel
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17
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Ajjolli Nagaraja A, Fontaine N, Delsaut M, Charton P, Damour C, Offmann B, Grondin-Perez B, Cadet F. Flux prediction using artificial neural network (ANN) for the upper part of glycolysis. PLoS One 2019; 14:e0216178. [PMID: 31067238 PMCID: PMC6505829 DOI: 10.1371/journal.pone.0216178] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 04/15/2019] [Indexed: 01/08/2023] Open
Abstract
The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments. The artificial neural network (ANN) method has been successively applied in different fields of science to perform complex functions. In this study, ANN models were trained to predict the flux for the upper part of glycolysis as inferred by NADH consumption, using four enzyme concentrations i.e., phosphoglucoisomerase, phosphofructokinase, fructose-bisphosphate-aldolase, triose-phosphate-isomerase. Out of three ANN algorithms, the neuralnet package with two activation functions, “logistic” and “tanh” were implemented. The prediction of the flux was very efficient: RMSE and R2 were 0.847, 0.93 and 0.804, 0.94 respectively for logistic and tanh functions using a cross validation procedure. This study showed that a systemic approach such as ANN could be used for accurate prediction of the flux through the metabolic pathway. This could help to save a lot of time and costs, particularly from an industrial perspective. The R-code is available at: https://github.com/DSIMB/ANN-Glycolysis-Flux-Prediction.
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Affiliation(s)
- Anamya Ajjolli Nagaraja
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | | | - Mathieu Delsaut
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | - Philippe Charton
- DSIMB, INSERM, UMR S-1134, Laboratory of ExcellenceLABEX GR, Faculty of Sciences and Technology, University of La Reunion & University Paris Diderot, Paris, France
| | - Cedric Damour
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | - Bernard Offmann
- Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UMR 6286 CNRS, UFR Sciences et Techniques, chemin de la Houssinière, France
| | - Brigitte Grondin-Perez
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, France
| | - Frederic Cadet
- DSIMB, INSERM, UMR S-1134, Laboratory of ExcellenceLABEX GR, Faculty of Sciences and Technology, University of La Reunion & University Paris Diderot, Paris, France
- * E-mail:
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18
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St. John PC, Bomble YJ. Approaches to Computational Strain Design in the Multiomics Era. Front Microbiol 2019; 10:597. [PMID: 31024467 PMCID: PMC6461008 DOI: 10.3389/fmicb.2019.00597] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/08/2019] [Indexed: 01/29/2023] Open
Abstract
Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.
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19
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Jensen PR, Matos MRA, Sonnenschein N, Meier S. Combined In-Cell NMR and Simulation Approach to Probe Redox-Dependent Pathway Control. Anal Chem 2019; 91:5395-5402. [PMID: 30896922 DOI: 10.1021/acs.analchem.9b00660] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Dynamic response of intracellular reaction cascades to changing environments is a hallmark of living systems. As metabolism is complex, mechanistic models have gained popularity for describing the dynamic response of cellular metabolism and for identifying target genes for engineering. At the same time, the detailed tracking of transient metabolism in living cells on the subminute time scale has become amenable using dynamic nuclear polarization-enhanced 13C NMR. Here, we suggest an approach combining in-cell NMR spectroscopy with perturbation experiments and modeling to obtain evidence that the bottlenecks of yeast glycolysis depend on intracellular redox state. In pre-steady-state glycolysis, pathway bottlenecks shift from downstream to upstream reactions within a few seconds, consistent with a rapid decline in the NAD+/NADH ratio. Simulations using mechanistic models reproduce the experimentally observed response and help identify unforeseen biochemical events. Remaining inaccuracies in the computational models can be identified experimentally. The combined use of rapid injection NMR spectroscopy and in silico simulations provides a promising method for characterizing cellular reactions with increasing mechanistic detail.
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Affiliation(s)
- Pernille R Jensen
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
| | - Marta R A Matos
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
| | - Nikolaus Sonnenschein
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
| | - Sebastian Meier
- Department of Chemistry, Department of Health Technology and The Novo Nordisk Foundation Center of Biosustainability , Technical University of Denmark , 2800 Kgs Lyngby , Denmark
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20
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Crooks DR, Fan TWM, Linehan WM. Metabolic Labeling of Cultured Mammalian Cells for Stable Isotope-Resolved Metabolomics: Practical Aspects of Tissue Culture and Sample Extraction. Methods Mol Biol 2019; 1928:1-27. [PMID: 30725447 PMCID: PMC8195444 DOI: 10.1007/978-1-4939-9027-6_1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Stable isotope-resolved metabolomics (SIRM) methods are used increasingly by cancer researchers to probe metabolic pathways and identify vulnerabilities in cancer cells. Analytical and computational advances are being made constantly, but tissue culture and sample extraction procedures are often variable and not elaborated in the literature. This chapter discusses basic aspects of tissue culture practices as they relate to the use of stable isotope tracers and provides a detailed metabolic labeling and metabolite extraction procedure designed to maximize the amount of information that can be obtained from a single tracer experiment.
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Affiliation(s)
- Daniel R Crooks
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Teresa W-M Fan
- Department of Toxicology and Cancer Biology, Center for Environmental and Systems Biochemistry, Markey Cancer Center, and University of Kentucky, Lexington, KY, USA.
| | - W Marston Linehan
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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21
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Kasbawati, Agnes, Kalondeng A, Sulfahri. Mathematical study of feedback inhibition effects on the dynamics of metabolites on the central metabolism of a yeast cell: a combination of kinetic model and metabolic control analysis. BIOTECHNOL BIOTEC EQ 2019. [DOI: 10.1080/13102818.2019.1641148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Affiliation(s)
- Kasbawati
- Applied Mathematics Laboratory, Department of Mathematics, Hasanuddin University, Makassar, Indonesia
| | - Agnes
- Applied Mathematics Laboratory, Department of Mathematics, Hasanuddin University, Makassar, Indonesia
| | - Anisa Kalondeng
- Applied Mathematics Laboratory, Department of Mathematics, Hasanuddin University, Makassar, Indonesia
| | - Sulfahri
- Microbiology Laboratory, Department of Biology, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia
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22
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Integrating -omics data into genome-scale metabolic network models: principles and challenges. Essays Biochem 2018; 62:563-574. [PMID: 30315095 DOI: 10.1042/ebc20180011] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/30/2018] [Accepted: 08/31/2018] [Indexed: 12/13/2022]
Abstract
At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available -omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of -omics data into CBMs focussing on the methods' assumptions and limitations. We argue that key assumptions - often derived from single-enzyme kinetics - do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for -omics data integration in a common framework to provide more accurate predictions.
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23
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Multidimensional heuristic process for high-yield production of astaxanthin and fragrance molecules in Escherichia coli. Nat Commun 2018; 9:1858. [PMID: 29752432 PMCID: PMC5948211 DOI: 10.1038/s41467-018-04211-x] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 04/06/2018] [Indexed: 01/09/2023] Open
Abstract
Optimization of metabolic pathways consisting of large number of genes is challenging. Multivariate modular methods (MMMs) are currently available solutions, in which reduced regulatory complexities are achieved by grouping multiple genes into modules. However, these methods work well for balancing the inter-modules but not intra-modules. In addition, application of MMMs to the 15-step heterologous route of astaxanthin biosynthesis has met with limited success. Here, we expand the solution space of MMMs and develop a multidimensional heuristic process (MHP). MHP can simultaneously balance different modules by varying promoter strength and coordinating intra-module activities by using ribosome binding sites (RBSs) and enzyme variants. Consequently, MHP increases enantiopure 3S,3′S-astaxanthin production to 184 mg l−1 day−1 or 320 mg l−1. Similarly, MHP improves the yields of nerolidol and linalool. MHP may be useful for optimizing other complex biochemical pathways. Achieving high titer yield and productivity of target chemicals in industrial organism depends on multidimensional pathway optimization. Here, the authors use a refined modular method called multidimensional heuristic process to improve production of astaxanthin, nerolidol and linalool in E. coli.
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24
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Reijngoud DJ. Flux analysis of inborn errors of metabolism. J Inherit Metab Dis 2018; 41:309-328. [PMID: 29318410 PMCID: PMC5959979 DOI: 10.1007/s10545-017-0124-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 12/04/2017] [Accepted: 12/05/2017] [Indexed: 02/07/2023]
Abstract
Patients with an inborn error of metabolism (IEM) are deficient of an enzyme involved in metabolism, and as a consequence metabolism reprograms itself to reach a new steady state. This new steady state underlies the clinical phenotype associated with the deficiency. Hence, we need to know the flux of metabolites through the different metabolic pathways in this new steady state of the reprogrammed metabolism. Stable isotope technology is best suited to study this. In this review the progress made in characterizing the altered metabolism will be presented. Studies done in patients to estimate the residual flux through the metabolic pathway affected by enzyme deficiencies will be discussed. After this, studies done in model systems will be reviewed. The focus will be on glycogen storage disease type I, medium-chain acyl-CoA dehydrogenase deficiency, propionic and methylmalonic aciduria, urea cycle defects, phenylketonuria, and combined D,L-2-hydroxyglutaric aciduria. Finally, new developments are discussed, which allow the tracing of metabolic reprogramming in IEM on a genome-wide scale. In conclusion, the outlook for flux analysis of metabolic derangement in IEMs looks promising.
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Affiliation(s)
- D-J Reijngoud
- Section of Systems Medicine and Metabolic Signaling, Laboratory of Pediatrics, Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Center of Liver, Digestive and Metabolic Diseases, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- European Research Institute of the Biology of Ageing, Internal ZIP code EA12, A. Deusinglaan 1, 9713, AV, Groningen, The Netherlands.
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25
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Metabolic engineering of Pichia pastoris. Metab Eng 2018; 50:2-15. [PMID: 29704654 DOI: 10.1016/j.ymben.2018.04.017] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 04/16/2018] [Accepted: 04/23/2018] [Indexed: 12/11/2022]
Abstract
Besides its use for efficient production of recombinant proteins the methylotrophic yeast Pichia pastoris (syn. Komagataella spp.) has been increasingly employed as a platform to produce metabolites of varying origin. We summarize here the impressive methodological developments of the last years to model and analyze the metabolism of P. pastoris, and to engineer its genome and metabolic pathways. Efficient methods to insert, modify or delete genes via homologous recombination and CRISPR/Cas9, supported by modular cloning techniques, have been reported. An outstanding early example of metabolic engineering in P. pastoris was the humanization of protein glycosylation. More recently the cell metabolism was engineered also to enhance the productivity of heterologous proteins. The last few years have seen an increased number of metabolic pathway design and engineering in P. pastoris, mainly towards the production of complex (secondary) metabolites. In this review, we discuss the potential role of P. pastoris as a platform for metabolic engineering, its strengths, and major requirements for future developments of chassis strains based on synthetic biology principles.
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26
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Baumgart M, Unthan S, Kloß R, Radek A, Polen T, Tenhaef N, Müller MF, Küberl A, Siebert D, Brühl N, Marin K, Hans S, Krämer R, Bott M, Kalinowski J, Wiechert W, Seibold G, Frunzke J, Rückert C, Wendisch VF, Noack S. Corynebacterium glutamicum Chassis C1*: Building and Testing a Novel Platform Host for Synthetic Biology and Industrial Biotechnology. ACS Synth Biol 2018; 7:132-144. [PMID: 28803482 DOI: 10.1021/acssynbio.7b00261] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Targeted top-down strategies for genome reduction are considered to have a high potential for providing robust basic strains for synthetic biology and industrial biotechnology. Recently, we created a library of 26 genome-reduced strains of Corynebacterium glutamicum carrying broad deletions in single gene clusters and showing wild-type-like biological fitness. Here, we proceeded with combinatorial deletions of these irrelevant gene clusters in two parallel orders, and the resulting library of 28 strains was characterized under various environmental conditions. The final chassis strain C1* carries a genome reduction of 13.4% (412 deleted genes) and shows wild-type-like growth behavior in defined medium with d-glucose as carbon and energy source. Moreover, C1* proves to be robust against several stresses (including oxygen limitation) and shows long-term growth stability under defined and complex medium conditions. In addition to providing a novel prokaryotic chassis strain, our results comprise a large strain library and a revised genome annotation list, which will be valuable sources for future systemic studies of C. glutamicum.
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Affiliation(s)
- Meike Baumgart
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systemic
Microbiology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Simon Unthan
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systems Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Ramona Kloß
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systems Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Andreas Radek
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systems Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Tino Polen
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systemic
Microbiology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Niklas Tenhaef
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systems Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Moritz Fabian Müller
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systems Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Andreas Küberl
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systemic
Microbiology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Daniel Siebert
- Institute
for Microbiology and Biotechnology, Ulm University, 89081 Ulm, Germany
| | - Natalie Brühl
- Institute
of Biochemistry, University of Cologne, 50923 Cologne, Germany
| | - Kay Marin
- Evonik Nutrition & Care GmbH, 45128 Essen, Germany
| | - Stephan Hans
- Evonik Nutrition & Care GmbH, 45128 Essen, Germany
| | - Reinhard Krämer
- Institute
of Biochemistry, University of Cologne, 50923 Cologne, Germany
| | - Michael Bott
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systemic
Microbiology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Jörn Kalinowski
- Microbial
Genomics and Biotechnology, Center for Biotechnology (CeBiTec), Bielefeld University, 33615 Bielefeld, Germany
| | - Wolfgang Wiechert
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systems Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
- Computational
Systems Biotechnology, RWTH Aachen University, 52062 Aachen, Germany
| | - Gerd Seibold
- Institute
for Microbiology and Biotechnology, Ulm University, 89081 Ulm, Germany
| | - Julia Frunzke
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systemic
Microbiology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Christian Rückert
- Microbial
Genomics and Biotechnology, Center for Biotechnology (CeBiTec), Bielefeld University, 33615 Bielefeld, Germany
| | - Volker F. Wendisch
- Chair
of Genetics of Prokaryotes, Faculty of Biology & CeBiTec, Bielefeld University, 33615 Bielefeld, Germany
| | - Stephan Noack
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Systems Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany
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27
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Delp J, Gutbier S, Cerff M, Zasada C, Niedenführ S, Zhao L, Smirnova L, Hartung T, Borlinghaus H, Schreiber F, Bergemann J, Gätgens J, Beyss M, Azzouzi S, Waldmann T, Kempa S, Nöh K, Leist M. Stage-specific metabolic features of differentiating neurons: Implications for toxicant sensitivity. Toxicol Appl Pharmacol 2017; 354:64-80. [PMID: 29278688 DOI: 10.1016/j.taap.2017.12.013] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 12/19/2017] [Accepted: 12/22/2017] [Indexed: 01/08/2023]
Abstract
Developmental neurotoxicity (DNT) may be induced when chemicals disturb a key neurodevelopmental process, and many tests focus on this type of toxicity. Alternatively, DNT may occur when chemicals are cytotoxic only during a specific neurodevelopmental stage. The toxicant sensitivity is affected by the expression of toxicant targets and by resilience factors. Although cellular metabolism plays an important role, little is known how it changes during human neurogenesis, and how potential alterations affect toxicant sensitivity of mature vs. immature neurons. We used immature (d0) and mature (d6) LUHMES cells (dopaminergic human neurons) to provide initial answers to these questions. Transcriptome profiling and characterization of energy metabolism suggested a switch from predominantly glycolytic energy generation to a more pronounced contribution of the tricarboxylic acid cycle (TCA) during neuronal maturation. Therefore, we used pulsed stable isotope-resolved metabolomics (pSIRM) to determine intracellular metabolite pool sizes (concentrations), and isotopically non-stationary 13C-metabolic flux analysis (INST 13C-MFA) to calculate metabolic fluxes. We found that d0 cells mainly use glutamine to fuel the TCA. Furthermore, they rely on extracellular pyruvate to allow continuous growth. This metabolic situation does not allow for mitochondrial or glycolytic spare capacity, i.e. the ability to adapt energy generation to altered needs. Accordingly, neuronal precursor cells displayed a higher sensitivity to several mitochondrial toxicants than mature neurons differentiated from them. In summary, this study shows that precursor cells lose their glutamine dependency during differentiation while they gain flexibility of energy generation and thereby increase their resistance to low concentrations of mitochondrial toxicants.
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Affiliation(s)
- Johannes Delp
- In Vitro Toxicology and Biomedicine, Dept Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78457 Konstanz, Germany
| | - Simon Gutbier
- In Vitro Toxicology and Biomedicine, Dept Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78457 Konstanz, Germany
| | - Martin Cerff
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Christin Zasada
- Max-Delbrück-Center of Molecular Medicine in the Helmholtz Association, Berlin 13125, Germany
| | - Sebastian Niedenführ
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Liang Zhao
- Johns Hopkins University, Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Lena Smirnova
- Johns Hopkins University, Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Thomas Hartung
- Johns Hopkins University, Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Hanna Borlinghaus
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany; Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Jörg Bergemann
- Department of Life Sciences, Albstadt-Sigmaringen University of Applied Sciences, Sigmaringen, Germany
| | - Jochem Gätgens
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Martin Beyss
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Salah Azzouzi
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Tanja Waldmann
- In Vitro Toxicology and Biomedicine, Dept Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78457 Konstanz, Germany
| | - Stefan Kempa
- Max-Delbrück-Center of Molecular Medicine in the Helmholtz Association, Berlin 13125, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, Dept Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, 78457 Konstanz, Germany; CAAT-Europe, University of Konstanz, Konstanz 78457, Germany.
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28
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Song HS, Thomas DG, Stegen JC, Li M, Liu C, Song X, Chen X, Fredrickson JK, Zachara JM, Scheibe TD. Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process. Front Microbiol 2017; 8:1866. [PMID: 29046664 PMCID: PMC5627231 DOI: 10.3389/fmicb.2017.01866] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 09/12/2017] [Indexed: 11/20/2022] Open
Abstract
In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community's traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.
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Affiliation(s)
- Hyun-Seob Song
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Dennis G Thomas
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - James C Stegen
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Minjing Li
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Chongxuan Liu
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Xuehang Song
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Xingyuan Chen
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - John M Zachara
- Pacific Northwest National Laboratory, Richland, WA, United States
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29
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Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. Biotechnol Adv 2017; 35:981-1003. [PMID: 28916392 DOI: 10.1016/j.biotechadv.2017.09.005] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 08/30/2017] [Accepted: 09/10/2017] [Indexed: 12/13/2022]
Abstract
Kinetic models are critical to predict the dynamic behaviour of metabolic networks. Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting their parameters. Recent modelling frameworks promise new ways to overcome this obstacle while retaining predictive capabilities. In this review, we present an overview of the relevant mathematical frameworks for kinetic formulation, construction and analysis. Starting with kinetic formalisms, we next review statistical methods for parameter inference, as well as recent computational frameworks applied to the construction and analysis of kinetic models. Finally, we discuss opportunities and limitations hindering the development of larger kinetic reconstructions.
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30
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Lopes H, Rocha I. Genome-scale modeling of yeast: chronology, applications and critical perspectives. FEMS Yeast Res 2017; 17:3950252. [PMID: 28899034 PMCID: PMC5812505 DOI: 10.1093/femsyr/fox050] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/07/2017] [Indexed: 01/21/2023] Open
Abstract
Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.
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Affiliation(s)
- Helder Lopes
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
| | - Isabel Rocha
- CEB - Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
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31
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Tang W, Deshmukh AT, Haringa C, Wang G, van Gulik W, van Winden W, Reuss M, Heijnen JJ, Xia J, Chu J, Noorman HJ. A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation byPenicillium chrysogenum. Biotechnol Bioeng 2017; 114:1733-1743. [DOI: 10.1002/bit.26294] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 02/26/2017] [Accepted: 03/14/2017] [Indexed: 12/30/2022]
Affiliation(s)
- Wenjun Tang
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | | | - Cees Haringa
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | - Walter van Gulik
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | | | - Matthias Reuss
- Institute of Biochemical Engineering; University of Stuttgart; Stuttgart Germany
| | - Joseph J. Heijnen
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
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32
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Gábor A, Villaverde AF, Banga JR. Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems. BMC SYSTEMS BIOLOGY 2017; 11:54. [PMID: 28476119 PMCID: PMC5420165 DOI: 10.1186/s12918-017-0428-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 04/25/2017] [Indexed: 01/13/2023]
Abstract
Background Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges. Methods We present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph. Results Our contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub (https://github.com/gabora/visid). Conclusions Our approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0428-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Attila Gábor
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.,JRC-COMBINE, RWTH Aachen University, Photonics Cluster, Level 4, Campus-Boulevard 79, Aachen, 52074, Germany
| | | | - Julio R Banga
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
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