1
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Tummler K, Klipp E. Data integration strategies for whole-cell modeling. FEMS Yeast Res 2024; 24:foae011. [PMID: 38544322 PMCID: PMC11042497 DOI: 10.1093/femsyr/foae011] [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: 12/02/2023] [Revised: 03/15/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.
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Affiliation(s)
- Katja Tummler
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
| | - Edda Klipp
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
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2
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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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3
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Lapin A, Perfahl H, Jain HV, Reuss M. Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth. Sci Rep 2022; 12:12373. [PMID: 35858953 PMCID: PMC9300625 DOI: 10.1038/s41598-022-15767-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.
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Affiliation(s)
- Alexey Lapin
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
- Institute of Chemical Process Engineering, University Stuttgart, Stuttgart, Germany
| | - Holger Perfahl
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
| | - Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, USA
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.
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4
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Minden S, Aniolek M, Sarkizi Shams Hajian C, Teleki A, Zerrer T, Delvigne F, van Gulik W, Deshmukh A, Noorman H, Takors R. Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli. Metabolites 2022; 12:metabo12030263. [PMID: 35323706 PMCID: PMC8953226 DOI: 10.3390/metabo12030263] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022] Open
Abstract
Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L−1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.
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Affiliation(s)
- Steven Minden
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Maria Aniolek
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Christopher Sarkizi Shams Hajian
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Attila Teleki
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Tobias Zerrer
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Frank Delvigne
- Microbial Processes and Interactions (MiPI), TERRA Research and Teaching Centre, Gembloux Agro Bio Tech, University of Liege, 5030 Gembloux, Belgium;
| | - Walter van Gulik
- Department of Biotechnology, Delft University of Technology, van der Maasweg 6, 2629 HZ Delft, The Netherlands;
| | - Amit Deshmukh
- Royal DSM, 2613 AX Delft, The Netherlands; (A.D.); (H.N.)
| | - Henk Noorman
- Royal DSM, 2613 AX Delft, The Netherlands; (A.D.); (H.N.)
- Department of Biotechnology, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
- Correspondence:
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5
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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: 6] [Impact Index Per Article: 3.0] [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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6
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McDonald AG, Tipton KF. Parameter Reliability and Understanding Enzyme Function. Molecules 2022; 27:263. [PMID: 35011495 PMCID: PMC8746786 DOI: 10.3390/molecules27010263] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/21/2021] [Accepted: 12/24/2021] [Indexed: 11/16/2022] Open
Abstract
Knowledge of the Michaelis-Menten parameters and their meaning in different circumstances is an essential prerequisite to understanding enzyme function and behaviour. The published literature contains an abundance of values reported for many enzymes. The problem concerns assessing the appropriateness and validity of such material for the purpose to which it is to be applied. This review considers the evaluation of such data with particular emphasis on the assessment of its fitness for purpose.
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Affiliation(s)
- Andrew G. McDonald
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, D02 PN40 Dublin, Ireland;
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7
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Bizon K, Tabiś B. Problems in volumetric flow rate and liquid level control of a continuous stirred tank bioreactor with structured and unstructured kinetics. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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8
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Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 32978650 DOI: 10.1007/10_2020_133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.
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9
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van Riel NAW, Tiemann CA, Hilbers PAJ, Groen AK. Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis. Front Bioeng Biotechnol 2021; 8:536957. [PMID: 33665185 PMCID: PMC7921164 DOI: 10.3389/fbioe.2020.536957] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 12/16/2020] [Indexed: 11/23/2022] Open
Abstract
Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.
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Affiliation(s)
- Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Department of Vascular Medicine, Amsterdam UMC, Amsterdam, Netherlands.,Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
| | - Christian A Tiemann
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Peter A J Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Albert K Groen
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, Netherlands.,Department of Laboratory Medicine, University Medical Center Groningen, Groningen, Netherlands
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10
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Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach. Processes (Basel) 2020. [DOI: 10.3390/pr8121554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.
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11
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Fang Y, Kaszuba T, Imoukhuede PI. Systems Biology Will Direct Vascular-Targeted Therapy for Obesity. Front Physiol 2020; 11:831. [PMID: 32760294 PMCID: PMC7373796 DOI: 10.3389/fphys.2020.00831] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 06/22/2020] [Indexed: 12/12/2022] Open
Abstract
Healthy adipose tissue expansion and metabolism during weight gain require coordinated angiogenesis and lymphangiogenesis. These vascular growth processes rely on the vascular endothelial growth factor (VEGF) family of ligands and receptors (VEGFRs). Several studies have shown that controlling vascular growth by regulating VEGF:VEGFR signaling can be beneficial for treating obesity; however, dysregulated angiogenesis and lymphangiogenesis are associated with several chronic tissue inflammation symptoms, including hypoxia, immune cell accumulation, and fibrosis, leading to obesity-related metabolic disorders. An ideal obesity treatment should minimize adipose tissue expansion and the advent of adverse metabolic consequences, which could be achieved by normalizing VEGF:VEGFR signaling. Toward this goal, a systematic investigation of the interdependency of vascular and metabolic systems in obesity and tools to predict personalized treatment ranges are necessary to improve patient outcomes through vascular-targeted therapies. Systems biology can identify the critical VEGF:VEGFR signaling mechanisms that can be targeted to regress adipose tissue expansion and can predict the metabolic consequences of different vascular-targeted approaches. Establishing a predictive, biologically faithful platform requires appropriate computational models and quantitative tissue-specific data. Here, we discuss the involvement of VEGF:VEGFR signaling in angiogenesis, lymphangiogenesis, adipogenesis, and macrophage specification – key mechanisms that regulate adipose tissue expansion and metabolism. We then provide useful computational approaches for simulating these mechanisms, and detail quantitative techniques for acquiring tissue-specific parameters. Systems biology, through computational models and quantitative data, will enable an accurate representation of obese adipose tissue that can be used to direct the development of vascular-targeted therapies for obesity and associated metabolic disorders.
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Affiliation(s)
- Yingye Fang
- Imoukhuede Systems Biology Laboratory, Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - Tomasz Kaszuba
- Imoukhuede Systems Biology Laboratory, Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - P I Imoukhuede
- Imoukhuede Systems Biology Laboratory, Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO, United States
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12
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Mili M, Panthu B, Madec AM, Berger MA, Rautureau GJP, Elena-Herrmann B. Fast and ergonomic extraction of adherent mammalian cells for NMR-based metabolomics studies. Anal Bioanal Chem 2020; 412:5453-5463. [PMID: 32556564 DOI: 10.1007/s00216-020-02764-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/08/2020] [Accepted: 06/08/2020] [Indexed: 11/29/2022]
Abstract
Cellular metabolomics has become key to elucidate mechanistic aspects in various fields such as cancerology or pharmacology, and is rapidly becoming a standard phenotyping tool accessible to the broad biological community. Acquisition of reliable spectroscopic datasets, such as nuclear magnetic resonance (NMR) spectra, to characterize biological systems depends on the elaboration of robust methods for cellular metabolites extraction. Previous studies have addressed many issues raised by these protocols, however with little pondering on ergonomic and practical aspects of the methods that impact their scalability, reproducibility and hence their suitability to high-throughput studies or their use by non-metabolomics experts. Here, we optimize a fast and ergonomic protocol for extraction of metabolites from adherent mammalian cells for NMR metabolomics studies. The proposed extraction protocol, including cell washing, metabolism quenching and actual extraction of intracellular metabolites, was first optimized on HeLa cells. Efficiency of the protocol, in its globality and for the different individual steps, was assessed by NMR quantification of 27 metabolites from cellular extracts. We show that a single PBS wash provides a seemly compromise between contamination from growth medium and leakage of intracellular metabolites. In HeLa cells, extraction using pure methanol, without cell scraping, recovered a higher amount of intracellular metabolites than the reference methanol/water/chloroform method with cell scraping, with yields varying across metabolite classes. Optimized and reference protocols were further tested on eight cell lines of miscellaneous nature, and inter-operator reproducibility was demonstrated. Our results stress the need for tailored extraction protocols and show that fast protocols minimizing time-consuming steps, without compromising extraction yields, are suitable for high-throughput metabolomics studies. Graphical abstract.
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Affiliation(s)
- Manhal Mili
- Institut des Sciences Analytiques UMR 5280, CRMN FRE 2034, Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, 5 rue de la Doua, 69100, Villeurbanne, France
| | - Baptiste Panthu
- CarMeN laboratory, Univ Lyon, INSERM, INRA, INSA, Université Claude Bernard Lyon1, 69121, Oullins, France
| | - Anne-Marie Madec
- CarMeN laboratory, Univ Lyon, INSERM, INRA, INSA, Université Claude Bernard Lyon1, 69121, Oullins, France
| | - Marie-Agnès Berger
- CarMeN laboratory, Univ Lyon, INSERM, INRA, INSA, Université Claude Bernard Lyon1, 69121, Oullins, France
| | - Gilles J P Rautureau
- Institut des Sciences Analytiques UMR 5280, CRMN FRE 2034, Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, 5 rue de la Doua, 69100, Villeurbanne, France
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13
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La A, Du H, Taidi B, Perré P. A predictive dynamic yeast model based on component, energy, and electron carrier balances. Biotechnol Bioeng 2020; 117:2728-2740. [PMID: 32458414 DOI: 10.1002/bit.27442] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/18/2020] [Accepted: 05/26/2020] [Indexed: 11/10/2022]
Abstract
The present study describes a novel yeast model for the prediction of yeast fermentation. The proposed model considers the possible metabolic pathways of yeast. For each pathway, the time evolution of components, energy (ATP/ADP), and electron carriers (NAD+ /NADH) are expressed with limitation factors for all quantities consumed by each respective pathway. In this manner, the model can predict the partition of these pathways based on the growth conditions and their evolution over time. Several biological pathways and their stoichiometric coefficients are well known from literature. It is important to note that most of the kinetic parameters have no effect as the actual kinetics are controlled by the balance of limiting factors. The few remaining parameters were adjusted and compared with the literature when the data set was available. The model fits our experimental data from yeast fermentation on glucose in a nonaerated batch system. The predictive ability of the model and its capacity to represent the intensity of each pathway over time facilitate an improved understanding of the interactions between the pathways. The key role of energy (ATP) and electron carrier (NAD+ ) to trigger the different metabolic pathways during yeast growth is highlighted, whereas the involvement of mitochondrial respiration not being associated with the TCA cycle is also shown.
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Affiliation(s)
- Angéla La
- LGPM, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France.,LGPM, CentraleSupélec, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Pomacle, France
| | - Huan Du
- LGPM, CentraleSupélec, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Pomacle, France
| | - Behnam Taidi
- LGPM, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France.,LGPM, CentraleSupélec, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Pomacle, France
| | - Patrick Perré
- LGPM, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France.,LGPM, CentraleSupélec, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Pomacle, France
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14
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Ramos JRC, Rath AG, Genzel Y, Sandig V, Reichl U. A dynamic model linking cell growth to intracellular metabolism and extracellular by-product accumulation. Biotechnol Bioeng 2020; 117:1533-1553. [PMID: 32022250 DOI: 10.1002/bit.27288] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 12/12/2019] [Accepted: 01/26/2020] [Indexed: 12/16/2022]
Abstract
Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1-antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by-products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α-ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.
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Affiliation(s)
- João R C Ramos
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Alexander G Rath
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- Bioprocess Engineering, AMINO GmbH, Frellstedt, Germany
| | - Yvonne Genzel
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Volker Sandig
- Bioprocess Engineering, ProBioGen AG, Berlin, Germany
| | - Udo Reichl
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- Bioprocess Engineering, Otto von Guericke University Magdeburg, Chair of Bioprocess Engineering, Magdeburg, Germany
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15
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O'Brien C, Allman A, Daoutidis P, Hu WS. Kinetic model optimization and its application to mitigating the Warburg effect through multiple enzyme alterations. Metab Eng 2019; 56:154-164. [PMID: 31400493 DOI: 10.1016/j.ymben.2019.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 07/05/2019] [Accepted: 08/06/2019] [Indexed: 12/17/2022]
Abstract
Pathway engineering is a powerful tool in biotechnological and clinical applications. However, many phenomena cannot be rewired with a single enzyme change, and in a complex network like energy metabolism, the selection of combinations of targets to engineer is a daunting task. To facilitate this process, we have developed an optimization framework and applied it to a mechanistic kinetic model of energy metabolism. We then identified combinations of enzyme alternations that led to the elimination of the Warburg effect seen in the metabolism of cancer cells and cell lines, a phenomenon coupling rapid proliferation to lactate production. Typically, optimization approaches use integer variables to achieve the desired flux redistribution with a minimum number of altered genes. This framework uses convex penalty terms to replace these integer variables and improve computational tractability. Optimal solutions are identified which substantially reduce or eliminate lactate production while maintaining the requirements for cellular proliferation using three or more enzymes.
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Affiliation(s)
- Conor O'Brien
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Andrew Allman
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA
| | - Wei-Shou Hu
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455-0132, USA.
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16
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Hard Limits and Performance Tradeoffs in a Class of Antithetic Integral Feedback Networks. Cell Syst 2019; 9:49-63.e16. [PMID: 31279505 DOI: 10.1016/j.cels.2019.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 02/28/2019] [Accepted: 05/30/2019] [Indexed: 12/18/2022]
Abstract
Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism called antithetic integral feedback, which can be implemented using the binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control. Using tools from control theory, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and leakiness. These findings yield a holistic understanding of the behavior of antithetic integral feedback and contribute to a more general theory of biological control systems.
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17
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Ren X, Deschênes JS, Tremblay R, Peres S, Jolicoeur M. A kinetic metabolic study of lipid production in Chlorella protothecoides under heterotrophic condition. Microb Cell Fact 2019; 18:113. [PMID: 31253148 PMCID: PMC6598345 DOI: 10.1186/s12934-019-1163-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 06/19/2019] [Indexed: 11/13/2022] Open
Abstract
Background Microalgae have been proposed as potential platform to produce lipid-derived products, such as biofuels. Knowledge on the intracellular carbon flow distribution may identify key metabolic processes during lipid synthesis thus refining culture/genetic strategies to maximize cell lipid productivity. A kinetic metabolic model simulating cell metabolic behavior and lipid production was first applied in the microalgae platform Chlorella protothecoides under heterotrophic condition. It combines both physiology and flux information in a kinetic approach. Cell nutrition, growth, lipid production and almost 30 metabolic intermediates covering central carbon metabolism were included and simulated. Results Model simulations were shown to adequately agree with experimental data, which is suggesting that the proposed model copes with Chlorella protothecoides cells’ biology. The dynamic metabolic flux analysis using the model showed a reversible starch flux from accumulation to decomposing when glucose reached depletion, while net lipid flux shows a quasi-constant rate. The sensitive flux parameters on starch and lipid metabolism suggested that starch synthesis is the major competing pathway that affects lipid accumulation in C. protothecoides. Flux analysis also demonstrated that high lipid yield under heterotrophic condition is accompanied with high lipid flux and low TCA activity. Meanwhile, the dynamic flux distribution also suggests a relatively constant ratio of glucose distributed to biomass, lipid, starch, nucleotides as well as pentose phosphate pathway. Conclusion The model described not only experimental data, but also unraveled intracellular carbon flow distribution and identify key metabolic processes during lipid synthesis. Most of the metabolic kinetics also showed statistical significance for metabolic mechanism. Therefore, this study unravels the mechanisms of the glucose impact on the dynamic carbon flux distribution, thus improving our understanding of the links between carbon fluxes and lipid metabolism in C. protothecoides. Electronic supplementary material The online version of this article (10.1186/s12934-019-1163-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiaojie Ren
- Colin Ratledge Center for Microbial Lipids, School of Agriculture Engineering and Food Science, Shandong University of Technology, Zibo, China.,Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, École Polytechnique de Montreal, Centre-ville Station, P.O. Box 6079, Montreal, H3C 3A7, QC, Canada
| | | | - Réjean Tremblay
- Université du Québec à Rimouski, 310 allée des Ursulines, Rimouski, QC, G5L 3A1, Canada
| | - Sabine Peres
- LRI, Université Paris-Sud, CNRS, Université Paris-Saclay, 91405, Orsay, France.,MaIAGE, INRA, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Mario Jolicoeur
- Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, École Polytechnique de Montreal, Centre-ville Station, P.O. Box 6079, Montreal, H3C 3A7, QC, Canada.
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18
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Kowalska A, Boruta T, Bizukojc M. Kinetic model to describe the morphological evolution of filamentous fungi during their early stages of growth in the standard submerged and microparticle-enhanced cultivations. Eng Life Sci 2019; 19:557-574. [PMID: 32625032 DOI: 10.1002/elsc.201900013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 04/25/2019] [Accepted: 05/23/2019] [Indexed: 01/30/2023] Open
Abstract
Biosynthesis of metabolites and enzymes by filamentous fungi depends on their morphological form in submerged cultures. However, their early stages of growth lasting approximately 24 h, from the introduction of spores to the medium until the formation of stable morphological forms, such as clumps or pellets, have rarely been the objects of experimental and modeling studies. Microparticle-enhanced cultivation (MPEC) has been applied only to a few fungal species, mainly Aspergilli. Therefore, the objective of this work was to formulate the kinetic model to describe the early stages of the fungal evolution in the standard cultivation and MPEC for Aspergillus terreus, Chaetomium globosum, Penicillium rubens, and Mucor racemosus. These fungi exhibit various mechanisms of agglomerates formation in submerged cultures. The experiments were performed in batch shake flasks (parameters identification) and a stirred tank bioreactor (model verification). In the balance equation for fungal cells, the mean projected area of hyphal objects measured by the digital analysis of microscopic images was used as the dependent variable. The analysis of the experimental data and model solution revealed that the effect of the microparticles (aluminum oxide at 6 g L-1) in MPEC toward the studied filamentous fungi was to the high extent species dependent. This effect was most evident in the case of spore coagulative A. terreus and noncoagulative M. racemosus.
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Affiliation(s)
- Anna Kowalska
- Faculty of Process and Environmental Engineering Department of Bioprocess Engineering Lodz University of Technology Lodz Poland
| | - Tomasz Boruta
- Faculty of Process and Environmental Engineering Department of Bioprocess Engineering Lodz University of Technology Lodz Poland
| | - Marcin Bizukojc
- Faculty of Process and Environmental Engineering Department of Bioprocess Engineering Lodz University of Technology Lodz Poland
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19
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Nielsen J. Yeast Systems Biology: Model Organism and Cell Factory. Biotechnol J 2019; 14:e1800421. [PMID: 30925027 DOI: 10.1002/biot.201800421] [Citation(s) in RCA: 137] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 02/23/2019] [Indexed: 01/02/2023]
Abstract
For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.
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Affiliation(s)
- Jens Nielsen
- BioInnovation Institute, Ole Måløes Vej 3, DK2200, Copenhagen N, Denmark
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK2800, Kongens Lyngby, Denmark
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20
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Kinetic modeling and sensitivity analysis for higher ethanol production in self-cloning xylose-using Saccharomyces cerevisiae. J Biosci Bioeng 2019; 127:563-569. [DOI: 10.1016/j.jbiosc.2018.10.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 10/20/2018] [Accepted: 10/25/2018] [Indexed: 11/20/2022]
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21
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Lian J, Mishra S, Zhao H. Recent advances in metabolic engineering of Saccharomyces cerevisiae: New tools and their applications. Metab Eng 2018; 50:85-108. [DOI: 10.1016/j.ymben.2018.04.011] [Citation(s) in RCA: 140] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 04/09/2018] [Accepted: 04/13/2018] [Indexed: 10/17/2022]
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22
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Li C, Shu W, Wang S, Liu P, Zhuang Y, Zhang S, Xia J. Dynamic metabolic response of Aspergillus niger to glucose perturbation: evidence of regulatory mechanism for reduced glucoamylase production. J Biotechnol 2018; 287:28-40. [PMID: 30134150 DOI: 10.1016/j.jbiotec.2018.08.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 06/20/2018] [Accepted: 08/18/2018] [Indexed: 01/14/2023]
Abstract
Environmental gradient is an important common issue during scale-up process for protein production. To address the dynamic regulatory mechanism of Aspergillus niger being exposed to inhomogeneous glucose concentrations, glucose perturbation were experimented on the steady state of A. niger chemostat culture, and dynamic profiles of the intracellular metabolites in central carbon metabolism were tracked in a time scale of seconds. The upper glycolysis and pentose phosphate pathway showed sharp variations after glucose perturbation, while the lower glycolysis, TCA cycle and amino acid pools represented a moderate and prolonged response due to the allosteric regulation of enzymes and buffering function of metabolites with large pool sizes. Improved glucose-6-phosphate enhanced the metabolic flux to PP pathway remarkably, which provided not only more redox cofactors (NADPH) for protein synthesis but also more precursors (phosphoribosyl pyrophosphate and ribose-5-phosphate) for cell growth. Moreover, reduction of the total adenine nucleotides and major precursor amino acids indicated the upregulated RNA synthesis was required to produce stress proteins, and partially explained the drop of glucoamylase production when A. niger experienced a fluctuated glucose concentration environment. These findings would be valuable for improving bioreactor operation, design, and scale-up from engineering or genetic aspects.
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Affiliation(s)
- Chao Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Wei Shu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Shuai Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Peng Liu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yingpping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Siliang Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China.
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23
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Kim OD, Rocha M, Maia P. A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering. Front Microbiol 2018; 9:1690. [PMID: 30108559 PMCID: PMC6079213 DOI: 10.3389/fmicb.2018.01690] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 07/06/2018] [Indexed: 12/03/2022] Open
Abstract
Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.
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Affiliation(s)
- Osvaldo D Kim
- SilicoLife Lda, Braga, Portugal.,Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
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24
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Laflaquière B, Leclercq G, Choey C, Chen J, Peres S, Ito C, Jolicoeur M. Identifying Biomarkers of Wharton's Jelly Mesenchymal Stromal Cells Using a Dynamic Metabolic Model: The Cell Passage Effect. Metabolites 2018; 8:metabo8010018. [PMID: 29495309 PMCID: PMC5876007 DOI: 10.3390/metabo8010018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 02/08/2018] [Accepted: 02/22/2018] [Indexed: 01/08/2023] Open
Abstract
Because of their unique ability to modulate the immune system, mesenchymal stromal cells (MSCs) are widely studied to develop cell therapies for detrimental immune and inflammatory disorders. However, controlling the final cell phenotype and determining immunosuppressive function following cell amplification in vitro often requires prolonged cell culture assays, all of which contribute to major bottlenecks, limiting the clinical emergence of cell therapies. For instance, the multipotent Wharton's Jelly mesenchymal stem/stromal cells (WJMSC), extracted from human umbilical cord, exhibit immunosuppressive traits under pro-inflammatory conditions, in the presence of interferon-γ (IFNγ), and tumor necrosis factor-α (TNFα). However, WJMSCs require co-culture bioassays with immune cells, which can take days, to confirm their immunomodulatory function. Therefore, the establishment of robust cell therapies would benefit from fast and reliable characterization assays. To this end, we have explored the metabolic behaviour of WJMSCs in in vitro culture, to identify biomarkers that are specific to the cell passage effect and the loss of their immunosuppressive phenotype. We clearly show distinct metabolic behaviours comparing WJMSCs at the fourth (P4) and the late ninth (P9) passages, although both P4 and P9 cells do not exhibit significant differences in their low immunosuppressive capacity. Metabolomics data were analysed using an in silico modelling platform specifically adapted to WJMSCs. Of interest, P4 cells exhibit a glycolytic metabolism compared to late passage (P9) cells, which show a phosphorylation oxidative metabolism, while P4 cells show a doubling time of 29 h representing almost half of that for P9 cells (46 h). We also clearly show that fourth passage WJMSCs still express known immunosuppressive biomarkers, although, this behaviour shows overlapping with a senescence phenotype.
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Affiliation(s)
- Benoît Laflaquière
- Department of Chemical Engineering, Research Laboratory in Applied Metabolic Engineering, École Polytechnique de Montréal, C.P.6079, Centre-ville Station, Montréal, QC H3C 3A7, Canada.
| | - Gabrielle Leclercq
- Department of Chemical Engineering, Research Laboratory in Applied Metabolic Engineering, École Polytechnique de Montréal, C.P.6079, Centre-ville Station, Montréal, QC H3C 3A7, Canada.
| | - Chandarong Choey
- Sprott Centre for Stem Cell Research, Ottawa Hospital Research Institute, 501 Smyth Rd. CCW 5105a, Ottawa, ON K1H 8L6, Canada.
| | - Jingkui Chen
- Department of Chemical Engineering, Research Laboratory in Applied Metabolic Engineering, École Polytechnique de Montréal, C.P.6079, Centre-ville Station, Montréal, QC H3C 3A7, Canada.
| | - Sabine Peres
- Department of Chemical Engineering, Research Laboratory in Applied Metabolic Engineering, École Polytechnique de Montréal, C.P.6079, Centre-ville Station, Montréal, QC H3C 3A7, Canada.
- LRI, Université Paris-Sud, CNRS, Université Paris-Saclay, 91405 Orsay, France.
- MaIAGE, INRA, Université Paris-Saclay, 78350 Jouy-en-Josas, France.
| | - Caryn Ito
- Sprott Centre for Stem Cell Research, Ottawa Hospital Research Institute, 501 Smyth Rd. CCW 5105a, Ottawa, ON K1H 8L6, Canada.
| | - Mario Jolicoeur
- Department of Chemical Engineering, Research Laboratory in Applied Metabolic Engineering, École Polytechnique de Montréal, C.P.6079, Centre-ville Station, Montréal, QC H3C 3A7, Canada.
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25
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Jeon M, Kang HW, An S. A Mathematical Model for Enzyme Clustering in Glucose Metabolism. Sci Rep 2018; 8:2696. [PMID: 29426820 PMCID: PMC5807315 DOI: 10.1038/s41598-018-20348-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 01/17/2018] [Indexed: 01/01/2023] Open
Abstract
We have recently demonstrated that the rate-limiting enzymes in human glucose metabolism organize into cytoplasmic clusters to form a multienzyme complex, the glucosome, in at least three different sizes. Quantitative high-content imaging data support a hypothesis that the glucosome clusters regulate the direction of glucose flux between energy metabolism and building block biosynthesis in a cluster size-dependent manner. However, direct measurement of their functional contributions to cellular metabolism at subcellular levels has remained challenging. In this work, we develop a mathematical model using a system of ordinary differential equations, in which the association of the rate-limiting enzymes into multienzyme complexes is included as an essential element. We then demonstrate that our mathematical model provides a quantitative principle to simulate glucose flux at both subcellular and population levels in human cancer cells. Lastly, we use the model to simulate 2-deoxyglucose-mediated alteration of glucose flux in a population level based on subcellular high-content imaging data. Collectively, we introduce a new mathematical model for human glucose metabolism, which promotes our understanding of functional roles of differently sized multienzyme complexes in both single-cell and population levels.
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Affiliation(s)
- Miji Jeon
- Department of Chemistry and Biochemistry, University of Maryland Baltimore County (UMBC), 1000 Hilltop Circle, Baltimore, MD, 21250, USA
| | - Hye-Won Kang
- Department of Mathematics and Statistics, University of Maryland Baltimore County (UMBC), 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
| | - Songon An
- Department of Chemistry and Biochemistry, University of Maryland Baltimore County (UMBC), 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
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26
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Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design. Methods Mol Biol 2018; 1671:63-82. [PMID: 29170953 DOI: 10.1007/978-1-4939-7295-1_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Engineering biological systems that are capable of overproducing products of interest is the ultimate goal of any biotechnology application. To this end, stoichiometric (or steady state) and kinetic models are increasingly becoming available for a variety of organisms including prokaryotes, eukaryotes, and microbial communities. This ever-accelerating pace of such model reconstructions has also spurred the development of optimization-based strain design techniques. This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering. In particular, three major methods are covered in detail including two methods for designing strains (i.e., one stoichiometric model-based and the other by integrating kinetic information into a stoichiometric model) and one method for analyzing microbial communities.
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27
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Schmitz AC, Hartline CJ, Zhang F. Engineering Microbial Metabolite Dynamics and Heterogeneity. Biotechnol J 2017; 12. [PMID: 28901715 DOI: 10.1002/biot.201700422] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Revised: 09/06/2017] [Indexed: 11/09/2022]
Abstract
As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains.
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Affiliation(s)
- Alexander C Schmitz
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, USA
| | - Christopher J Hartline
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, USA.,Division of Biological and Biomedical Sciences, and Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, USA
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28
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Zhao X, Kasbi M, Chen J, Peres S, Jolicoeur M. A dynamic metabolic flux analysis of ABE (acetone-butanol-ethanol) fermentation byClostridium acetobutylicumATCC 824, with riboflavin as a by-product. Biotechnol Bioeng 2017; 114:2907-2919. [DOI: 10.1002/bit.26393] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 07/24/2017] [Indexed: 11/06/2022]
Affiliation(s)
- Xinhe Zhao
- Research Laboratory in Applied Metabolic Engineering; Department of Chemical Engineering; École Polytechnique de Montréal; Montréal Québec Canada
| | - Mayssa Kasbi
- Research Laboratory in Applied Metabolic Engineering; Department of Chemical Engineering; École Polytechnique de Montréal; Montréal Québec Canada
| | - Jingkui Chen
- Research Laboratory in Applied Metabolic Engineering; Department of Chemical Engineering; École Polytechnique de Montréal; Montréal Québec Canada
| | - Sabine Peres
- LRI, Université Paris-Sud; CNRS, Université Paris-Saclay; Orsay France
- MaIAGE, INRA; Université Paris-Saclay; Jouy-en-Josas France
| | - Mario Jolicoeur
- Research Laboratory in Applied Metabolic Engineering; Department of Chemical Engineering; École Polytechnique de Montréal; Montréal Québec Canada
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29
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Kostidis S, Addie RD, Morreau H, Mayboroda OA, Giera M. Quantitative NMR analysis of intra- and extracellular metabolism of mammalian cells: A tutorial. Anal Chim Acta 2017. [PMID: 28622799 DOI: 10.1016/j.aca.2017.05.011] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Metabolomics analysis of body fluids as well as cells is depended on many factors. While several well-accepted standard operating procedures for the analysis of body fluids are available, the NMR based quantitative analysis of cellular metabolites is less well standardized. Experimental designs depend on the cell type, the quenching protocol and the applied post-acquisition workflow. Here, we provide a tutorial for the quantitative description of the metabolic phenotype of mammalian cells using NMR spectroscopy. We discuss all key steps of the process, starting from the selection of the appropriate culture medium, quenching techniques to arrest metabolism in a reproducible manner, the extraction of the intracellular components and the profiling of the culture medium. NMR data acquisition and methods for both qualitative and quantitative analysis are also provided. The suggested methods cover experiments for adherent cells and cells in suspension. We ultimately describe the application of the discussed workflow to a thyroid cancer cell line. Although this tutorial focuses on mammalian cells, the given guidelines and procedures may be adjusted for the analysis of other cell types.
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Affiliation(s)
- Sarantos Kostidis
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands.
| | - Ruben D Addie
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands; Department of Pathology, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
| | - Hans Morreau
- Department of Pathology, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
| | - Oleg A Mayboroda
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2300RC, Leiden, The Netherlands
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30
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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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Abstract
Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.
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Affiliation(s)
- Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41128 Gothenburg, Sweden; .,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Lyngby, Denmark.,Science for Life Laboratory, Royal Institute of Technology, SE17121 Stockholm, Sweden
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32
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Novak M, Trontel A, Slavica A, Horvat P, Šantek B. Computational fluid dynamic (CFD) modeling of simultaneous extraction and fermentation process in a single sugar beet cossette. THE EUROBIOTECH JOURNAL 2017. [DOI: 10.24190/issn2564-615x/2017/01.04] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
For simulations of flow and microbial conversion reactions, related to modeling of simultaneous extraction and fermentation process in a single sugar beet cossette a software package OpenFOAM was used. The mass transfer of the components (sucrose, glucose, fructose and ethanol) in the studied system was controlled by the convection and diffusion processes. Microbial conversion rates and yield coefficients were experimentally determined and/or estimated by mathematical simulation. Dimensions of the model sugar beet cossette (SBC) were: average length of cosettes 40.10 mm, average thickness 3.32 mm and average width 3.5 mm, and represented in the model as a square-shape cross-section mathematical simulation. Dimensions of the model sugar beet cossette (SBC) were: average length of cosettes 40.10 mm, average thickness 3.32 mm and average width 3.5 mm, and represented in the model as a square-shape cross-section used to study the mass transfer and microbial conversion rates on the scale of single sugar beet cossette in the short time scales (up to 25 s). This model can be used for simulation of extractant flow around single sugar beet cossette as well as for description of simultaneous extraction and fermentation process in the studied system.
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Affiliation(s)
- Mario Novak
- Laboratory of Biochemical Engineering, Industrial Microbiology, Malting and Brewing Technology, Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb , Croatia
| | - Antonija Trontel
- Laboratory of Biochemical Engineering, Industrial Microbiology, Malting and Brewing Technology, Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb , Croatia
| | - Anita Slavica
- Laboratory of Biochemical Engineering, Industrial Microbiology, Malting and Brewing Technology, Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb , Croatia
| | - Predrag Horvat
- Laboratory of Biochemical Engineering, Industrial Microbiology, Malting and Brewing Technology, Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb , Croatia
| | - Božidar Šantek
- Laboratory of Biochemical Engineering, Industrial Microbiology, Malting and Brewing Technology, Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb , Croatia
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33
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Ghafari M, Mashaghi A. On the role of topology in regulating transcriptional cascades. Phys Chem Chem Phys 2017; 19:25168-25179. [DOI: 10.1039/c7cp02671d] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Topology of interactions in a transcriptional cascade determines the behavior of its signal-response profile and the activation states of genes.
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Affiliation(s)
- Mahan Ghafari
- Leiden Academic Centre for Drug Research
- Faculty of Mathematics and Natural Sciences
- Leiden University
- Leiden
- The Netherlands
| | - Alireza Mashaghi
- Leiden Academic Centre for Drug Research
- Faculty of Mathematics and Natural Sciences
- Leiden University
- Leiden
- The Netherlands
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34
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Hackett SR, Zanotelli VRT, Xu W, Goya J, Park JO, Perlman DH, Gibney PA, Botstein D, Storey JD, Rabinowitz JD. Systems-level analysis of mechanisms regulating yeast metabolic flux. Science 2016; 354:aaf2786. [PMID: 27789812 PMCID: PMC5414049 DOI: 10.1126/science.aaf2786] [Citation(s) in RCA: 185] [Impact Index Per Article: 23.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 09/23/2016] [Indexed: 07/25/2023]
Abstract
Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states and, therefore, how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three previously unrecognized instances of cross-pathway regulation, which we biochemically verified. One of these involved inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.
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Affiliation(s)
- Sean R Hackett
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | | | - Wenxin Xu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Jonathan Goya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Junyoung O Park
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - David H Perlman
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Patrick A Gibney
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - David Botstein
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - John D Storey
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08544, USA
| | - Joshua D Rabinowitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
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35
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Current state and challenges for dynamic metabolic modeling. Curr Opin Microbiol 2016; 33:97-104. [PMID: 27472025 DOI: 10.1016/j.mib.2016.07.008] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 06/28/2016] [Accepted: 07/06/2016] [Indexed: 01/06/2023]
Abstract
While the stoichiometry of metabolism is probably the best studied cellular level, the dynamics in metabolism can still not be well described, predicted and, thus, engineered. Unknowns in the metabolic flux behavior arise from kinetic interactions, especially allosteric control mechanisms. While the stoichiometry of enzymes is preserved in vitro, their activity and kinetic behavior differs from the in vivo situation. Next to this challenge, it is infeasible to test the interaction of each enzyme with each intracellular metabolite in vitro exhaustively. As a consequence, the whole interacting metabolome has to be studied in vivo to identify the relevant enzymes properties. In this review we discuss current approaches for in vivo perturbation experiments, that is, stimulus response experiments using different setups and quantitative analytical approaches, including dynamic carbon tracing. Next to reliable and informative data, advanced modeling approaches and computational tools are required to identify kinetic mechanisms and their parameters.
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36
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Sudarsan S, Blank LM, Dietrich A, Vielhauer O, Takors R, Schmid A, Reuss M. Dynamics of benzoate metabolism in Pseudomonas putida KT2440. Metab Eng Commun 2016; 3:97-110. [PMID: 29468117 PMCID: PMC5779716 DOI: 10.1016/j.meteno.2016.03.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Revised: 02/29/2016] [Accepted: 03/14/2016] [Indexed: 12/26/2022] Open
Abstract
Soil microorganisms mineralize lignin-derived aromatic carbon sources using oxidative catabolic pathways, such as the β-ketoadipate pathway. Although this aromatic pathway is one of the best-studied pathways in biochemistry, the complete pathway, including its regulation by aromatic carbon sources, has not been integrated into the metabolic network. In particular, information about the in vivo operation (e.g., kinetics and flux capacity) of the pathway is lacking. In this contribution, we use kinetic modeling and thermodynamic analysis to evaluate the in vivo operation of this key aromatic multi-step pathway. The resulting ab initio deterministic model of benzoate degradation via the β-ketoadipate (ortho-cleavage) pathway in Pseudomonas putida KT2440 is presented. The kinetic model includes mechanistic rate expressions for the enzymes and transport processes. The design and experimental validation of the model are driven by data generated from short-term perturbation experiments in a benzoate-limited continuous culture. The results of rigorous modeling of the in vivo dynamics provide strong support for flux regulation by the benzoate transporter and the enzymes forming and cleaving catechol. Revisiting the β-ketoadipate pathway might be valuable for applications in different fields, such as biochemistry and metabolic engineering, that use lignin monomers as a carbon source. We describe a kinetic model for the β-ketoadipate pathway. Short term metabolic responses were tracked on metabolite level by rapid sampling. The model captures steady state and dynamic conditions of the β-ketoadipate pathway. Thermodynamic analysis revealed regulation points of the pathway. The results are discussed in the context of metabolic network operation.
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Affiliation(s)
- Suresh Sudarsan
- Institute of Applied Microbiology, ABBt – Aachen Biology and Biotechnology Department, RWTH Aachen University, 52074 Aachen, Germany
| | - Lars M. Blank
- Institute of Applied Microbiology, ABBt – Aachen Biology and Biotechnology Department, RWTH Aachen University, 52074 Aachen, Germany
| | - Alexander Dietrich
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany
| | - Oliver Vielhauer
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany
| | - Andreas Schmid
- Department Solar Materials, Helmholtz Centre for Environmental Research GmbH – UFZ, 04318 Leipzig, Germany
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University of Stuttgart, Nobelstrasse 15, 70569 Stuttgart, Germany
- Corresponding author.
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37
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van Niekerk DD, Penkler GP, du Toit F, Snoep JL. Targeting glycolysis in the malaria parasite Plasmodium falciparum. FEBS J 2016; 283:634-46. [PMID: 26648082 DOI: 10.1111/febs.13615] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
UNLABELLED Glycolysis is the main pathway for ATP production in the malaria parasite Plasmodium falciparum and essential for its survival. Following a sensitivity analysis of a detailed kinetic model for glycolysis in the parasite, the glucose transport reaction was identified as the step whose activity needed to be inhibited to the least extent to result in a 50% reduction in glycolytic flux. In a subsequent inhibitor titration with cytochalasin B, we confirmed the model analysis experimentally and measured a flux control coefficient of 0.3 for the glucose transporter. In addition to the glucose transporter, the glucokinase and phosphofructokinase had high flux control coefficients, while for the ATPase a small negative flux control coefficient was predicted. In a broader comparative analysis of glycolytic models, we identified a weakness in the P. falciparum pathway design with respect to stability towards perturbations in the ATP demand. DATABASE The mathematical model described here has been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.bio.vu.nl/database/vanniekerk1. The SEEK-study including the experimental data set is available at DOI 10.15490/seek.1. INVESTIGATION 56 (http://dx.doi.org/10.15490/seek.1. INVESTIGATION 56).
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Affiliation(s)
- David D van Niekerk
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa
| | - Gerald P Penkler
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa.,Molecular Cell Physiology, Vrije Universiteit Amsterdam, The Netherlands
| | - Francois du Toit
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa
| | - Jacky L Snoep
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa.,Molecular Cell Physiology, Vrije Universiteit Amsterdam, The Netherlands.,MIB, University of Manchester, UK
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38
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Krull R, Peterat G. Analysis of reaction kinetics during chemostat cultivation of Saccharomyces cerevisiae using a multiphase microreactor. Biochem Eng J 2016. [DOI: 10.1016/j.bej.2015.08.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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39
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Stryjewski WS, Tabiś B, Boroń D. Dynamic behaviour of stirred tank bioreactors based on structured and unstructured kinetic models. A comparative study. Chem Eng Res Des 2015. [DOI: 10.1016/j.cherd.2015.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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40
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Pérez-Torrado R, Gamero E, Gómez-Pastor R, Garre E, Aranda A, Matallana E. Yeast biomass, an optimised product with myriad applications in the food industry. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.10.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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41
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Kesten D, Kummer U, Sahle S, Hübner K. A new model for the aerobic metabolism of yeast allows the detailed analysis of the metabolic regulation during glucose pulse. Biophys Chem 2015; 206:40-57. [DOI: 10.1016/j.bpc.2015.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Revised: 06/23/2015] [Accepted: 06/25/2015] [Indexed: 01/08/2023]
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42
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Nishino S, Okahashi N, Matsuda F, Shimizu H. Absolute quantitation of glycolytic intermediates reveals thermodynamic shifts in Saccharomyces cerevisiae strains lacking PFK1 or ZWF1 genes. J Biosci Bioeng 2015; 120:280-6. [DOI: 10.1016/j.jbiosc.2015.01.012] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Revised: 12/18/2014] [Accepted: 01/09/2015] [Indexed: 10/23/2022]
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43
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Mulukutla BC, Yongky A, Grimm S, Daoutidis P, Hu WS. Multiplicity of steady states in glycolysis and shift of metabolic state in cultured mammalian cells. PLoS One 2015; 10:e0121561. [PMID: 25806512 PMCID: PMC4373774 DOI: 10.1371/journal.pone.0121561] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Accepted: 02/11/2015] [Indexed: 01/23/2023] Open
Abstract
Cultured mammalian cells exhibit elevated glycolysis flux and high lactate production. In the industrial bioprocesses for biotherapeutic protein production, glucose is supplemented to the culture medium to sustain continued cell growth resulting in the accumulation of lactate to high levels. In such fed-batch cultures, sometimes a metabolic shift from a state of high glycolysis flux and high lactate production to a state of low glycolysis flux and low lactate production or even lactate consumption is observed. While in other cases with very similar culture conditions, the same cell line and medium, cells continue to produce lactate. A metabolic shift to lactate consumption has been correlated to the productivity of the process. Cultures that exhibited the metabolic shift to lactate consumption had higher titers than those which didn't. However, the cues that trigger the metabolic shift to lactate consumption state (or low lactate production state) are yet to be identified. Metabolic control of cells is tightly linked to growth control through signaling pathways such as the AKT pathway. We have previously shown that the glycolysis of proliferating cells can exhibit bistability with well-segregated high flux and low flux states. Low lactate production (or lactate consumption) is possible only at a low glycolysis flux state. In this study, we use mathematical modeling to demonstrate that lactate inhibition together with AKT regulation on glycolysis enzymes can profoundly influence the bistable behavior, resulting in a complex steady-state topology. The transition from the high flux state to the low flux state can only occur in certain regions of the steady state topology, and therefore the metabolic fate of the cells depends on their metabolic trajectory encountering the region that allows such a metabolic state switch. Insights from such switch behavior present us with new means to control the metabolism of mammalian cells in fed-batch cultures.
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Affiliation(s)
- Bhanu Chandra Mulukutla
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Andrew Yongky
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Simon Grimm
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei-Shou Hu
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, United States of America
- * E-mail:
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44
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Matsuoka Y, Shimizu K. Current status and future perspectives of kinetic modeling for the cell metabolism with incorporation of the metabolic regulation mechanism. BIORESOUR BIOPROCESS 2015. [DOI: 10.1186/s40643-014-0031-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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45
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Affiliation(s)
- Arvind Varma
- School of Chemical Engineering; Purdue University; West Lafayette IN 47907
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46
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Understanding bistability in yeast glycolysis using general properties of metabolic pathways. Math Biosci 2014; 255:33-42. [PMID: 24956444 DOI: 10.1016/j.mbs.2014.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Revised: 06/03/2014] [Accepted: 06/04/2014] [Indexed: 11/23/2022]
Abstract
UNLABELLED Glycolysis is the central pathway in energy metabolism in the majority of organisms. In a recent paper, van Heerden et al. showed experimentally and computationally that glycolysis can exist in two states, a global steady state and a so-called imbalanced state. In the imbalanced state, intermediary metabolites accumulate at low levels of ATP and inorganic phosphate. It was shown that Baker's yeast uses a peculiar regulatory mechanism--via trehalose metabolism--to ensure that most yeast cells reach the steady state and not the imbalanced state. RESULTS Here we explore the apparent bistable behaviour in a core model of glycolysis that is based on a well-established detailed model, and study in great detail the bifurcation behaviour of solutions, without using any numerical information on parameter values. CONCLUSION We uncover a rich suite of solutions, including so-called imbalanced states, bistability, and oscillatory behaviour. The techniques employed are generic, directly suitable for a wide class of biochemical pathways, and could lead to better analytical treatments of more detailed models.
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47
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Sowa SW, Baldea M, Contreras LM. Optimizing metabolite production using periodic oscillations. PLoS Comput Biol 2014; 10:e1003658. [PMID: 24901332 PMCID: PMC4046915 DOI: 10.1371/journal.pcbi.1003658] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 04/17/2014] [Indexed: 12/20/2022] Open
Abstract
Methods for improving microbial strains for metabolite production remain the subject of constant research. Traditionally, metabolic tuning has been mostly limited to knockouts or overexpression of pathway genes and regulators. In this paper, we establish a new method to control metabolism by inducing optimally tuned time-oscillations in the levels of selected clusters of enzymes, as an alternative strategy to increase the production of a desired metabolite. Using an established kinetic model of the central carbon metabolism of Escherichia coli, we formulate this concept as a dynamic optimization problem over an extended, but finite time horizon. Total production of a metabolite of interest (in this case, phosphoenolpyruvate, PEP) is established as the objective function and time-varying concentrations of the cellular enzymes are used as decision variables. We observe that by varying, in an optimal fashion, levels of key enzymes in time, PEP production increases significantly compared to the unoptimized system. We demonstrate that oscillations can improve metabolic output in experimentally feasible synthetic circuits.
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Affiliation(s)
- Steven W. Sowa
- Microbiology Graduate Program, University of Texas at Austin, Austin, Texas, United States of America
| | - Michael Baldea
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, United States of America
- * E-mail: (MB); (LMC)
| | - Lydia M. Contreras
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, United States of America
- * E-mail: (MB); (LMC)
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48
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Suarez-Mendez CA, Sousa A, Heijnen JJ, Wahl A. Fast "Feast/Famine" Cycles for Studying Microbial Physiology Under Dynamic Conditions: A Case Study with Saccharomyces cerevisiae. Metabolites 2014; 4:347-72. [PMID: 24957030 PMCID: PMC4101510 DOI: 10.3390/metabo4020347] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Revised: 05/01/2014] [Accepted: 05/06/2014] [Indexed: 01/24/2023] Open
Abstract
Microorganisms are constantly exposed to rapidly changing conditions, under natural as well as industrial production scale environments, especially due to large-scale substrate mixing limitations. In this work, we present an experimental approach based on a dynamic feast/famine regime (400 s) that leads to repetitive cycles with moderate changes in substrate availability in an aerobic glucose cultivation of Saccharomyces cerevisiae. After a few cycles, the feast/famine produced a stable and repetitive pattern with a reproducible metabolic response in time, thus providing a robust platform for studying the microorganism's physiology under dynamic conditions. We found that the biomass yield was slightly reduced (-5%) under the feast/famine regime, while the averaged substrate and oxygen consumption as well as the carbon dioxide production rates were comparable. The dynamic response of the intracellular metabolites showed specific differences in comparison to other dynamic experiments (especially stimulus-response experiments, SRE). Remarkably, the frequently reported ATP paradox observed in single pulse experiments was not present during the repetitive perturbations applied here. We found that intracellular dynamic accumulations led to an uncoupling of the substrate uptake rate (up to 9-fold change at 20 s.) Moreover, the dynamic profiles of the intracellular metabolites obtained with the feast/famine suggest the presence of regulatory mechanisms that resulted in a delayed response. With the feast famine setup many cellular states can be measured at high frequency given the feature of reproducible cycles. The feast/famine regime is thus a versatile platform for systems biology approaches, which can help us to identify and investigate metabolite regulations under realistic conditions (e.g., large-scale bioreactors or natural environments).
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Affiliation(s)
- Camilo A Suarez-Mendez
- Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands.
| | - Andre Sousa
- Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands.
| | - Joseph J Heijnen
- Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands.
| | - Aljoscha Wahl
- Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands.
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k-OptForce: integrating kinetics with flux balance analysis for strain design. PLoS Comput Biol 2014; 10:e1003487. [PMID: 24586136 PMCID: PMC3930495 DOI: 10.1371/journal.pcbi.1003487] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 01/10/2014] [Indexed: 11/19/2022] Open
Abstract
Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.
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