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Ferreira MADM, Silveira WBD, Nikoloski Z. Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes. Biotechnol Bioeng 2024; 121:915-930. [PMID: 38178617 DOI: 10.1002/bit.28650] [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: 08/17/2022] [Revised: 10/24/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024]
Abstract
Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.
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Affiliation(s)
| | | | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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2
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Zhou Y, Zhou S, Lyons S, Sun H, Sweedler JV, Lu Y. Enhancing 2-Pyrone Synthase Efficiency by High-Throughput Mass-Spectrometric Quantification and In Vitro/In Vivo Catalytic Performance Correlation. Chembiochem 2024; 25:e202300849. [PMID: 38116888 DOI: 10.1002/cbic.202300849] [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: 12/19/2023] [Accepted: 12/19/2023] [Indexed: 12/21/2023]
Abstract
Engineering efficient biocatalysts is essential for metabolic engineering to produce valuable bioproducts from renewable resources. However, due to the complexity of cellular metabolic networks, it is challenging to translate success in vitro into high performance in cells. To meet such a challenge, an accurate and efficient quantification method is necessary to screen a large set of mutants from complex cell culture and a careful correlation between the catalysis parameters in vitro and performance in cells is required. In this study, we employed a mass-spectrometry based high-throughput quantitative method to screen new mutants of 2-pyrone synthase (2PS) for triacetic acid lactone (TAL) biosynthesis through directed evolution in E. coli. From the process, we discovered two mutants with the highest improvement (46 fold) in titer and the fastest kcat (44 fold) over the wild type 2PS, respectively, among those reported in the literature. A careful examination of the correlation between intracellular substrate concentration, Michaelis-Menten parameters and TAL titer for these two mutants reveals that a fast reaction rate under limiting intracellular substrate concentrations is important for in-cell biocatalysis. Such properties can be tuned by protein engineering and synthetic biology to adopt these engineered proteins for the maximum activities in different intracellular environments.
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Affiliation(s)
- Yu Zhou
- Department of Chemistry, The University of Texas at Austin, 105 E 24th St, Austin, TX 78712, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, 1206 W Gregory Dr, Urbana, IL, 61801, USA
| | - Shuaizhen Zhou
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, 1206 W Gregory Dr, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 W Gregory Dr, Urbana, IL, 61801, USA
| | - Scott Lyons
- Department of Molecular Bioscience, The University of Texas at Austin, 100 E 24th St, Austin, TX 78712, USA
| | - Haoran Sun
- Department of Molecular Bioscience, The University of Texas at Austin, 100 E 24th St, Austin, TX 78712, USA
| | - Jonathan V Sweedler
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, 1206 W Gregory Dr, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 W Gregory Dr, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, 505 S Mathews Avenue, Urbana, IL, 61801, USA
| | - Yi Lu
- Department of Chemistry, The University of Texas at Austin, 105 E 24th St, Austin, TX 78712, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, 1206 W Gregory Dr, Urbana, IL, 61801, USA
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3
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Hu M, Suthers PF, Maranas CD. KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states. Metab Eng 2024; 82:123-133. [PMID: 38336004 DOI: 10.1016/j.ymben.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/24/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.
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Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
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4
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Ferreira MADM, da Silveira WB, Nikoloski Z. PARROT: Prediction of enzyme abundances using protein-constrained metabolic models. PLoS Comput Biol 2023; 19:e1011549. [PMID: 37856550 PMCID: PMC10617714 DOI: 10.1371/journal.pcbi.1011549] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/31/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023] Open
Abstract
Protein allocation determines the activity of cellular pathways and affects growth across all organisms. Therefore, different experimental and machine learning approaches have been developed to quantify and predict protein abundance and how they are allocated to different cellular functions, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks. Here, using protein-constrained metabolic models, we propose a family of constrained-based approaches, termed PARROT, to predict how much of each enzyme is used based on the principle of minimizing the difference between a reference and an alternative growth condition. To this end, PARROT variants model the minimization of enzyme reallocation using four different (combinations of) distance functions. We demonstrate that the PARROT variant that minimizes the Manhattan distance between the enzyme allocation of a reference and an alternative condition outperforms existing approaches based on the parsimonious distribution of fluxes or enzymes for both Escherichia coli and Saccharomyces cerevisiae. Further, we show that the combined minimization of flux and enzyme allocation adjustment leads to inconsistent predictions. Together, our findings indicate that minimization of protein allocation rather than flux redistribution is a governing principle determining steady-state pathway activity for microorganism grown in alternative growth conditions.
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Affiliation(s)
| | | | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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5
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Arend M, Zimmer D, Xu R, Sommer F, Mühlhaus T, Nikoloski Z. Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale. Nat Commun 2023; 14:4781. [PMID: 37553325 PMCID: PMC10409818 DOI: 10.1038/s41467-023-40498-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/01/2023] [Indexed: 08/10/2023] Open
Abstract
Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.
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Affiliation(s)
- Marius Arend
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
- Bioinformatics and Mathematical Modeling Department, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria
| | - David Zimmer
- Computational Systems Biology, TU Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Rudan Xu
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Frederik Sommer
- Molecular Biotechnology & Systems Biology, TU Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Timo Mühlhaus
- Computational Systems Biology, TU Kaiserslautern, 67663, Kaiserslautern, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany.
- Bioinformatics and Mathematical Modeling Department, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria.
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6
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Fan X, Cao L, Yan X. Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models. Biotechnol Bioeng 2023. [PMID: 37448239 DOI: 10.1002/bit.28493] [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: 11/29/2022] [Revised: 04/04/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( k cat ${k}_{\mathrm{cat}}$ ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 k cat ${k}_{\mathrm{cat}}$ c), highly sensitive (1759 k cat ${k}_{\mathrm{cat}}$ ), and nonsensitive (2502 k cat ${k}_{\mathrm{cat}}$ ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.
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Affiliation(s)
- Xingcun Fan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Lingfeng Cao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xuefeng Yan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
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7
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Wendering P, Arend M, Razaghi-Moghadam Z, Nikoloski Z. Data integration across conditions improves turnover number estimates and metabolic predictions. Nat Commun 2023; 14:1485. [PMID: 36932067 PMCID: PMC10023748 DOI: 10.1038/s41467-023-37151-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 03/03/2023] [Indexed: 03/19/2023] Open
Abstract
Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Marius Arend
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
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8
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Li F, Chen Y, Anton M, Nielsen J. GotEnzymes: an extensive database of enzyme parameter predictions. Nucleic Acids Res 2022; 51:D583-D586. [PMID: 36169223 PMCID: PMC9825421 DOI: 10.1093/nar/gkac831] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/08/2022] [Accepted: 09/26/2022] [Indexed: 01/29/2023] Open
Abstract
Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (AI) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by AI approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.
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Affiliation(s)
- Feiran Li
- Correspondence may also be addressed to Feiran Li.
| | | | | | - Jens Nielsen
- To whom correspondence should be addressed. Tel: +46 31 772 3804;
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9
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Characterization of the Urinary Metagenome and Virome in Healthy Children. Biomedicines 2022; 10:biomedicines10102412. [PMID: 36289674 PMCID: PMC9599034 DOI: 10.3390/biomedicines10102412] [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: 06/28/2022] [Revised: 09/15/2022] [Accepted: 09/17/2022] [Indexed: 11/17/2022] Open
Abstract
Recent advances in next-generation sequencing and metagenomic studies have provided insights into the microbial profile of different body sites. However, research on the microbial composition of urine is limited, particularly in children. The goal of this study was to optimize and develop reproducible metagenome and virome protocols using a small volume of urine samples collected from healthy children. We collected midstream urine specimens from 40 healthy children. Using the metagenomics shotgun approach, we tested various protocols. Different microbial roots such as Archaea, Bacteria, Eukaryota, and Viruses were successfully identified using our optimized urine protocol. Our data reflected much variation in the microbial fingerprints of children. Girls had significantly higher levels of Firmicutes, whereas boys had significantly higher levels of Actinobacteria. The genus Anaerococcus dominated the urinary bacteriome of healthy girls, with a significant increase in Anaerococcus prevotii, Anaerococcus vaginalis, and Veillonella parvula (p-value < 0.001) when compared with that of boys. An increased relative abundance of Xylanimonas and Arthrobacter, with a significantly high abundance of Arthrobacter sp. FB24 (p-value 0.0028) and Arthrobacter aurescences (p-value 0.015), was observed in boys. The urinary mycobiome showed a significant rise in the genus Malassezia and Malassezia globose fungus (p-value 0.009) in girls, whereas genus Saccharomyces (p-value 0.009) was significantly high in boys. The beta diversity of the urinary mycobiome was found to differ between different age groups. Boys had significantly more Mastadenovirus and Human mastadenovirus-A in their urinary virome than girls. With increasing age, we noticed an increase in the relative abundance of the order Caudovirales. Our optimized protocols allowed us to identify the unique microbes for each sex by using an adequate volume of urine (3−10 mL) to screen for the bacteriome, mycobiome, and virome profiles in the urine of healthy children. To the best of our knowledge, this is the first study to characterize the metagenomics profiles of urine in a healthy pediatric population.
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Wilken SE, Besançon M, Kratochvíl M, Foko Kuate CA, Trefois C, Gu W, Ebenhöh O. Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models. Metab Eng 2022; 74:72-82. [PMID: 36152931 DOI: 10.1016/j.ymben.2022.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/08/2022] [Accepted: 09/10/2022] [Indexed: 10/31/2022]
Abstract
Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.
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Affiliation(s)
- St Elmo Wilken
- Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany.
| | - Mathieu Besançon
- Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Takustraße 7, 14195, Berlin, Germany
| | - Miroslav Kratochvíl
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg
| | - Chilperic Armel Foko Kuate
- Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Christophe Trefois
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg
| | - Oliver Ebenhöh
- Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
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11
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Yeast has evolved to minimize protein resource cost for synthesizing amino acids. Proc Natl Acad Sci U S A 2022; 119:2114622119. [PMID: 35042799 PMCID: PMC8795554 DOI: 10.1073/pnas.2114622119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2021] [Indexed: 12/02/2022] Open
Abstract
Proteins, as essential biomolecules, account for a large fraction of cell mass, and thus the synthesis of the complete set of proteins (i.e., the proteome) represents a substantial part of the cellular resource budget. Therefore, cells might be under selective pressures to optimize the resource costs for protein synthesis, particularly the biosynthesis of the 20 proteinogenic amino acids. Previous studies showed that less energetically costly amino acids are more abundant in the proteomes of bacteria that survive under energy-limited conditions, but the energy cost of synthesizing amino acids was reported to be weakly associated with the amino acid usage in Saccharomyces cerevisiae. Here we present a modeling framework to estimate the protein cost of synthesizing each amino acid (i.e., the protein mass required for supporting one unit of amino acid biosynthetic flux) and the glucose cost (i.e., the glucose consumed per amino acid synthesized). We show that the logarithms of the relative abundances of amino acids in S. cerevisiae’s proteome correlate well with the protein costs of synthesizing amino acids (Pearson’s r = −0.89), which is better than that with the glucose costs (Pearson’s r = −0.5). Therefore, we demonstrate that S. cerevisiae tends to minimize protein resource, rather than glucose or energy, for synthesizing amino acids.
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12
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Malina C, Di Bartolomeo F, Kerkhoven EJ, Nielsen J. Constraint-based modeling of yeast mitochondria reveals the dynamics of protein import and iron-sulfur cluster biogenesis. iScience 2021; 24:103294. [PMID: 34755100 PMCID: PMC8564123 DOI: 10.1016/j.isci.2021.103294] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 08/27/2021] [Accepted: 10/14/2021] [Indexed: 12/03/2022] Open
Abstract
Mitochondria are a hallmark of eukaryal cells and play an important role in cellular metabolism. There is a vast amount of knowledge available on mitochondrial metabolism and essential mitochondrial functions, such as protein import and iron-sulfur cluster biosynthesis, including multiple studies on the mitochondrial proteome. Therefore, there is a need for in silico approaches to facilitate the analysis of these data. Here, we present a detailed model of mitochondrial metabolism Saccharomyces cerevisiae, including protein import, iron-sulfur cluster biosynthesis, and a description of the coupling between charge translocation processes and ATP synthesis. Model analysis implied a dual dependence of absolute levels of proteins in protein import, iron-sulfur cluster biogenesis and cluster abundance on growth rate and respiratory activity. The model is instrumental in studying dynamics and perturbations in these processes and given the high conservation of mitochondrial metabolism in humans, it can provide insight into their role in human disease. Reconstruction of mitochondrial protein import and cofactor metabolism in yeast Quantification of the energy cost of metabolite transport Protein import activity depends on growth rate and respiratory activity Quantification iron-sulfur cluster requirements show growth rate dependence
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Affiliation(s)
- Carl Malina
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, 41296 Gothenburg, Sweden
| | | | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, 41296 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.,BioInnovation Institute, Ole Måløes Vej 3, 2200 Copenhagen N, Denmark
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