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Ploch T, Deussen J, Naumann U, Mitsos A, Hannemann-Tamás R. Direct single shooting for dynamic optimization of differential-algebraic equation systems with optimization criteria embedded. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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2
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Kinetic data analysis and mathematical modeling of intra (wild type vs. engineered) and inter species (Saccharomyces cerevisiae vs. Zymomonas mobilis) dependency for bioethanol production from glucose, xylose or their combination. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2021.108229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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3
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Kim JW, Park BJ, Oh TH, Lee JM. Model-based reinforcement learning and predictive control for two-stage optimal control of fed-batch bioreactor. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107465] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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4
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How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review. Processes (Basel) 2021. [DOI: 10.3390/pr9091577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.
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Ploch T, Lieres EV, Wiechert W, Mitsos A, Hannemann-Tamás R. Simulation of differential-algebraic equation systems with optimization criteria embedded in Modelica. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106920] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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Kuriya Y, Araki M. Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli. Metabolites 2020; 10:E198. [PMID: 32429049 PMCID: PMC7281464 DOI: 10.3390/metabo10050198] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 11/18/2022] Open
Abstract
Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.
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Affiliation(s)
- Yuki Kuriya
- Graduate School of Medicine, Kyoto University, 54 ShogoinKawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan;
| | - Michihiro Araki
- Graduate School of Medicine, Kyoto University, 54 ShogoinKawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan;
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo 162-8636, Japan
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan
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Bridging substrate intake kinetics and bacterial growth phenotypes with flux balance analysis incorporating proteome allocation. Sci Rep 2020; 10:4283. [PMID: 32152336 PMCID: PMC7062752 DOI: 10.1038/s41598-020-61174-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Accepted: 02/24/2020] [Indexed: 11/08/2022] Open
Abstract
Empirical kinetic models such as the Monod equation have been widely applied to relate the cell growth with substrate availability. The Monod equation shares a similar form with the mechanistically-based Michaelis-Menten kinetics for enzymatic processes, which has provoked long-standing and un-concluded conjectures on their relationship. In this work, we integrated proteome allocation principles into a Flux Balance Analysis (FBA) model of Escherichia coli, which quantitatively revealed potential mechanisms that underpin the phenomenological Monod parameters: the maximum specific growth rate could be dictated by the abundance of growth-controlling proteome and growth-pertinent proteome cost; more importantly, the Monod constant (Ks) was shown to relate to the Michaelis constant for substrate transport (Km,g), with the link being dependent on the cell's metabolic strategy. Besides, the proposed model was able to predict glucose uptake rate at given external glucose concentration through the size of available proteome resource for substrate transport and its enzymatic cost, while growth rate and acetate overflow were accurately simulated for two E. coli strains. Bridging the enzymatic kinetics of substrate intake and overall growth phenotypes, this work offers a mechanistic interpretation to the empirical Monod law, and demonstrates the potential of coupling local and global cellular constrains in predictive modelling.
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Paulson JA, Martin-Casas M, Mesbah A. Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions. PLoS Comput Biol 2019; 15:e1007308. [PMID: 31469832 PMCID: PMC6742419 DOI: 10.1371/journal.pcbi.1007308] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 09/12/2019] [Accepted: 07/31/2019] [Indexed: 01/05/2023] Open
Abstract
We present a novel surrogate modeling method that can be used to accelerate the solution of uncertainty quantification (UQ) problems arising in nonlinear and non-smooth models of biological systems. In particular, we focus on dynamic flux balance analysis (DFBA) models that couple intracellular fluxes, found from the solution of a constrained metabolic network model of the cellular metabolism, to the time-varying nature of the extracellular substrate and product concentrations. DFBA models are generally computationally expensive and present unique challenges to UQ, as they entail dynamic simulations with discrete events that correspond to switches in the active set of the solution of the constrained intracellular model. The proposed non-smooth polynomial chaos expansion (nsPCE) method is an extension of traditional PCE that can effectively capture singularities in the DFBA model response due to the occurrence of these discrete events. The key idea in nsPCE is to use a model of the singularity time to partition the parameter space into two elements on which the model response behaves smoothly. Separate PCE models are then fit in both elements using a basis-adaptive sparse regression approach that is known to scale well with respect to the number of uncertain parameters. We demonstrate the effectiveness of nsPCE on a DFBA model of an E. coli monoculture that consists of 1075 reactions and 761 metabolites. We first illustrate how traditional PCE is unable to handle problems of this level of complexity. We demonstrate that over 800-fold savings in computational cost of uncertainty propagation and Bayesian estimation of parameters in the substrate uptake kinetics can be achieved by using the nsPCE surrogates in place of the full DFBA model simulations. We then investigate the scalability of the nsPCE method by utilizing it for global sensitivity analysis and maximum a posteriori estimation in a synthetic metabolic network problem with a larger number of parameters related to both intracellular and extracellular quantities.
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Affiliation(s)
- Joel A. Paulson
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California, United States of America
| | - Marc Martin-Casas
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California, United States of America
| | - Ali Mesbah
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California, United States of America
- * E-mail:
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9
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Serrano-Bermúdez LM, González Barrios AF, Montoya D. Clostridium butyricum population balance model: Predicting dynamic metabolic flux distributions using an objective function related to extracellular glycerol content. PLoS One 2018; 13:e0209447. [PMID: 30571717 PMCID: PMC6301710 DOI: 10.1371/journal.pone.0209447] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 12/05/2018] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Extensive experimentation has been conducted to increment 1,3-propanediol (PDO) production using Clostridium butyricum cultures in glycerol, but computational predictions are limited. Previously, we reconstructed the genome-scale metabolic (GSM) model iCbu641, the first such model of a PDO-producing Clostridium strain, which was validated at steady state using flux balance analysis (FBA). However, the prediction ability of FBA is limited for batch and fed-batch cultures, which are the most often employed industrial processes. RESULTS We used the iCbu641 GSM model to develop a dynamic flux balance analysis (DFBA) approach to predict the PDO production of the Colombian strain Clostridium sp IBUN 158B. First, we compared the predictions of the dynamic optimization approach (DOA), static optimization approach (SOA), and direct approach (DA). We found no differences between approaches, but the DOA simulation duration was nearly 5000 times that of the SOA and DA simulations. Experimental results at glycerol limitation and glycerol excess allowed for validating dynamic predictions of growth, glycerol consumption, and PDO formation. These results indicated a 4.4% error in PDO prediction and therefore validated the previously proposed objective functions. We performed two global sensitivity analyses, finding that the kinetic input parameters of glycerol uptake flux had the most significant effect on PDO predictions. The other input parameters evaluated during global sensitivity analysis were biomass composition (precursors and macromolecules), death constants, and the kinetic parameters of acetic acid secretion flux. These last input parameters, all obtained from other Clostridium butyricum cultures, were used to develop a population balance model (PBM). Finally, we simulated fed-batch cultures, predicting a final PDO production near to 66 g/L, almost three times the PDO predicted in the best batch culture. CONCLUSIONS We developed and validated a dynamic approach to predict PDO production using the iCbu641 GSM model and the previously proposed objective functions. This validated approach was used to propose a population model and then an increment in predictions of PDO production through fed-batch cultures. Therefore, this dynamic model could predict different scenarios, including its integration into downstream processes to predict technical-economic feasibilities and reducing the time and costs associated with experimentation.
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Affiliation(s)
- Luis Miguel Serrano-Bermúdez
- Bioprocesses and Bioprospecting Group, Universidad Nacional de Colombia, Ciudad Universitaria, Carrera, Bogotá D.C., Colombia
- Grupo Cundinamarca Agroambiental, Departamento de Ingeniería Ambiental, Universidad de Cundinamarca, Facatativá, Colombia
| | - Andrés Fernando González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), Departamento de Ingeniería Química, Universidad de los Andes, Bogotá D.C., Colombia
| | - Dolly Montoya
- Bioprocesses and Bioprospecting Group, Universidad Nacional de Colombia, Ciudad Universitaria, Carrera, Bogotá D.C., Colombia
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10
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Simulation and optimization of dynamic flux balance analysis models using an interior point method reformulation. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.08.041] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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11
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Emenike VN, Schenkendorf R, Krewer U. Model-based optimization of biopharmaceutical manufacturing in Pichia pastoris based on dynamic flux balance analysis. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.07.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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12
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Jabarivelisdeh B, Waldherr S. Optimization of bioprocess productivity based on metabolic-genetic network models with bilevel dynamic programming. Biotechnol Bioeng 2018; 115:1829-1841. [PMID: 29578608 DOI: 10.1002/bit.26599] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 02/09/2018] [Accepted: 03/19/2018] [Indexed: 02/02/2023]
Abstract
One of the main goals of metabolic engineering is to obtain high levels of a microbial product through genetic modifications. To improve the productivity of such a process, the dynamic implementation of metabolic engineering strategies has been proven to be more beneficial compared to static genetic manipulations in which the gene expression is not controlled over time, by resolving the trade-off between growth and production. In this work, a bilevel optimization framework based on constraint-based models is applied to identify an optimal strategy for dynamic genetic and process level manipulations to increase productivity. The dynamic enzyme-cost flux balance analysis (deFBA) as underlying metabolic network model captures the network dynamics and enables the analysis of temporal regulation in the metabolic-genetic network. We apply our computational framework to maximize ethanol productivity in a batch process with Escherichia coli. The results highlight the importance of integrating the genetic level and enzyme production and degradation processes for obtaining optimal dynamic gene and process manipulations.
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Affiliation(s)
- Banafsheh Jabarivelisdeh
- International Max Planck Research School (IMPRS) for Advanced Methods in Process and System Engineering Magdeburg, Magdeburg, Germany.,Institute for Automation Engineering, Center for Dynamical Systems, Magdeburg, Saxony-Anhalt, Germany.,KU Leuven, Department of Chemical Engineering, Leuven, Belgium
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13
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14
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Model Predictive Control of a Fed-batch Bioreactor Based on Dynamic Metabolic-Genetic Network Models. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.ifacol.2018.09.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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15
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Joshi CJ, Peebles CA, Prasad A. Modeling and analysis of flux distribution and bioproduct formation in Synechocystis sp. PCC 6803 using a new genome-scale metabolic reconstruction. ALGAL RES 2017. [DOI: 10.1016/j.algal.2017.09.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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16
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Iman M, Sobati T, Panahi Y, Mobasheri M. Systems Biology Approach to Bioremediation of Nitroaromatics: Constraint-Based Analysis of 2,4,6-Trinitrotoluene Biotransformation by Escherichia coli. Molecules 2017; 22:E1242. [PMID: 28805729 PMCID: PMC6152126 DOI: 10.3390/molecules22081242] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 06/22/2017] [Accepted: 06/23/2017] [Indexed: 01/02/2023] Open
Abstract
Microbial remediation of nitroaromatic compounds (NACs) is a promising environmentally friendly and cost-effective approach to the removal of these life-threating agents. Escherichia coli (E. coli) has shown remarkable capability for the biotransformation of 2,4,6-trinitro-toluene (TNT). Efforts to develop E. coli as an efficient TNT degrading biocatalyst will benefit from holistic flux-level description of interactions between multiple TNT transforming pathways operating in the strain. To gain such an insight, we extended the genome-scale constraint-based model of E. coli to account for a curated version of major TNT transformation pathways known or evidently hypothesized to be active in E. coli in present of TNT. Using constraint-based analysis (CBA) methods, we then performed several series of in silico experiments to elucidate the contribution of these pathways individually or in combination to the E. coli TNT transformation capacity. Results of our analyses were validated by replicating several experimentally observed TNT degradation phenotypes in E. coli cultures. We further used the extended model to explore the influence of process parameters, including aeration regime, TNT concentration, cell density, and carbon source on TNT degradation efficiency. We also conducted an in silico metabolic engineering study to design a series of E. coli mutants capable of degrading TNT at higher yield compared with the wild-type strain. Our study, therefore, extends the application of CBA to bioremediation of nitroaromatics and demonstrates the usefulness of this approach to inform bioremediation research.
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Affiliation(s)
- Maryam Iman
- Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, 1477893855 Tehran, Iran.
- Department of Pharmaceutics, School of Pharmacy, Baqiyatallah University of Medical Sciences, 1477893855 Tehran, Iran.
| | - Tabassom Sobati
- Young Researchers and Elite Club, Islamic Azad University, 46115655 Tehran, Iran.
| | - Yunes Panahi
- Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, 1477893855 Tehran, Iran.
| | - Meysam Mobasheri
- Young Researchers and Elite Club, Islamic Azad University, 46115655 Tehran, Iran.
- Department of Biotechnology, Faculty of Advanced Sciences & Technology, Pharmaceutical Sciences Branch, Islamic Azad University (IAUPS), 194193311 Tehran, Iran.
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17
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Kumar D, Budman H. Applications of Polynomial Chaos Expansions in optimization and control of bioreactors based on dynamic metabolic flux balance models. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2017.03.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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18
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Kamminga T, Slagman SJ, Bijlsma JJE, Martins Dos Santos VAP, Suarez-Diez M, Schaap PJ. Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate. Biotechnol Bioeng 2017; 114:2339-2347. [PMID: 28600895 PMCID: PMC6084303 DOI: 10.1002/bit.26347] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 05/13/2017] [Accepted: 06/07/2017] [Indexed: 11/08/2022]
Abstract
Mycoplasma hyopneumoniae is cultured on large-scale to produce antigen for inactivated whole-cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint-based genome-scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time-series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non-growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model-driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3-4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes. Biotechnol. Bioeng. 2017;114: 2339-2347. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Tjerko Kamminga
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University and Research, Stippeneng 4, 6708, Wageningen, The Netherlands.,Bioprocess Technology and Support, MSD Animal Health, Boxmeer, The Netherlands
| | - Simen-Jan Slagman
- Bioprocess Technology and Support, MSD Animal Health, Boxmeer, The Netherlands
| | - Jetta J E Bijlsma
- Discovery and Technology, MSD Animal Health, Boxmeer, The Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University and Research, Stippeneng 4, 6708, Wageningen, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University and Research, Stippeneng 4, 6708, Wageningen, The Netherlands
| | - Peter J Schaap
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University and Research, Stippeneng 4, 6708, Wageningen, The Netherlands
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19
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Dynamic flux balance analysis with nonlinear objective function. J Math Biol 2017; 75:1487-1515. [PMID: 28401266 DOI: 10.1007/s00285-017-1127-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 01/17/2017] [Indexed: 12/20/2022]
Abstract
Dynamic flux balance analysis (DFBA) extends flux balance analysis and enables the combined simulation of both intracellular and extracellular environments of microbial cultivation processes. A DFBA model contains two coupled parts, a dynamic part at the upper level (extracellular environment) and an optimization part at the lower level (intracellular environment). Both parts are coupled through substrate uptake and product secretion rates. This work proposes a Karush-Kuhn-Tucker condition based solution approach for DFBA models, which have a nonlinear objective function in the lower-level part. To solve this class of DFBA models an extreme-ray-based reformulation is proposed to ensure certain regularity of the lower-level optimization problem. The method is introduced by utilizing two simple example networks and then applied to a realistic model of central carbon metabolism of wild-type Corynebacterium glutamicum.
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20
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St. John PC, Crowley MF, Bomble YJ. Efficient estimation of the maximum metabolic productivity of batch systems. BIOTECHNOLOGY FOR BIOFUELS 2017; 10:28. [PMID: 28163785 PMCID: PMC5282707 DOI: 10.1186/s13068-017-0709-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 01/12/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND Production of chemicals from engineered organisms in a batch culture involves an inherent trade-off between productivity, yield, and titer. Existing strategies for strain design typically focus on designing mutations that achieve the highest yield possible while maintaining growth viability. While these methods are computationally tractable, an optimum productivity could be achieved by a dynamic strategy in which the intracellular division of resources is permitted to change with time. New methods for the design and implementation of dynamic microbial processes, both computational and experimental, have therefore been explored to maximize productivity. However, solving for the optimal metabolic behavior under the assumption that all fluxes in the cell are free to vary is a challenging numerical task. Previous studies have therefore typically focused on simpler strategies that are more feasible to implement in practice, such as the time-dependent control of a single flux or control variable. RESULTS This work presents an efficient method for the calculation of a maximum theoretical productivity of a batch culture system using a dynamic optimization framework. The proposed method follows traditional assumptions of dynamic flux balance analysis: first, that internal metabolite fluxes are governed by a pseudo-steady state, and secondly that external metabolite fluxes are dynamically bounded. The optimization is achieved via collocation on finite elements, and accounts explicitly for an arbitrary number of flux changes. The method can be further extended to calculate the complete Pareto surface of productivity as a function of yield. We apply this method to succinate production in two engineered microbial hosts, Escherichia coli and Actinobacillus succinogenes, and demonstrate that maximum productivities can be more than doubled under dynamic control regimes. CONCLUSIONS The maximum theoretical yield is a measure that is well established in the metabolic engineering literature and whose use helps guide strain and pathway selection. We present a robust, efficient method to calculate the maximum theoretical productivity: a metric that will similarly help guide and evaluate the development of dynamic microbial bioconversions. Our results demonstrate that nearly optimal yields and productivities can be achieved with only two discrete flux stages, indicating that near-theoretical productivities might be achievable in practice.
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Affiliation(s)
- Peter C. St. John
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401 USA
| | - Michael F. Crowley
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401 USA
| | - Yannick J. Bomble
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401 USA
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21
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Computationally efficient dynamic simulation of cellular kinetics via explicit solution of flux balance analysis: xDFBA modelling and its biochemical process applications. Chem Eng Res Des 2016. [DOI: 10.1016/j.cherd.2016.07.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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22
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Ma KC, Perli SD, Lu TK. Foundations and Emerging Paradigms for Computing in Living Cells. J Mol Biol 2016; 428:893-915. [DOI: 10.1016/j.jmb.2016.02.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Revised: 02/13/2016] [Accepted: 02/15/2016] [Indexed: 01/11/2023]
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23
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Villaverde AF, Bongard S, Mauch K, Balsa-Canto E, Banga JR. Metabolic engineering with multi-objective optimization of kinetic models. J Biotechnol 2016; 222:1-8. [PMID: 26826510 DOI: 10.1016/j.jbiotec.2016.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 12/30/2015] [Accepted: 01/11/2016] [Indexed: 10/22/2022]
Abstract
Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.
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Affiliation(s)
- Alejandro F Villaverde
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain; Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; Department of Systems and Control Engineering, Universidade de Vigo, Rua Maxwell, 36310 Vigo, Spain
| | - Sophia Bongard
- Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany
| | - Klaus Mauch
- Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany
| | - Eva Balsa-Canto
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
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Unrean P. Bioprocess modelling for the design and optimization of lignocellulosic biomass fermentation. BIORESOUR BIOPROCESS 2016. [DOI: 10.1186/s40643-015-0079-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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25
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Abstract
Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.
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Affiliation(s)
- Michael A Henson
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, U.S.A.
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26
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Heavner BD, Price ND. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction. PLoS Comput Biol 2015; 11:e1004530. [PMID: 26566239 PMCID: PMC4643975 DOI: 10.1371/journal.pcbi.1004530] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Accepted: 08/28/2015] [Indexed: 11/18/2022] Open
Abstract
We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.
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Affiliation(s)
- Benjamin D. Heavner
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
- * E-mail:
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27
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Pornkamol U, Franzen CJ. Dynamic flux balancing elucidates NAD(P)H production as limiting response to furfural inhibition in Saccharomyces cerevisiae. Biotechnol J 2015; 10:1248-58. [PMID: 25880365 DOI: 10.1002/biot.201400833] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2014] [Revised: 02/13/2015] [Accepted: 04/13/2015] [Indexed: 12/20/2022]
Abstract
Achieving efficient and economical lignocellulose-based bioprocess requires a robust organism tolerant to furfural, a major inhibitory compound present in lignocellulosic hydrolysate. The aim of this study was to develop a model that could generate quantitative descriptions of cell metabolism for elucidating the cell's adaptive response to furfural. Such a modelling tool could provide strategies for the design of more robust cells. A dynamic flux balance (dFBA) model of Saccharomyces cerevisiae was created by coupling a kinetic fermentation model with a previously published genome-scale stoichiometric model. The dFBA model was used for studying intracellular and extracellular flux responses to furfural perturbations under steady state and dynamic conditions. The predicted effects of furfural on dynamic flux profiles agreed well with previously published experimental results. The model showed that the yeast cell adjusts its metabolism in response to furfural challenge by increasing fluxes through the pentose phosphate pathway, TCA cycle, and proline and serine biosynthesis in order to meet the high demand of NAD(P)H cofactors. The model described here can be used to aid in systematic optimization of the yeast, as well as of the fermentation process, for efficient lignocellulosic ethanol production.
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Affiliation(s)
- Unrean Pornkamol
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani, Thailand.
| | - Carl J Franzen
- Chalmers University of Technology, Department of Chemical and Biological Engineering, Division of Life Science - Industrial Biotechnology, Gothenburg, Sweden
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28
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Lisha KP, Sarkar D. In silico analysis of bioethanol production from glucose/xylose mixtures during fed-batch fermentation of co-culture and mono-culture systems. BIOTECHNOL BIOPROC E 2014. [DOI: 10.1007/s12257-014-0320-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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29
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Sánchez BJ, Pérez-Correa JR, Agosin E. Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization. Metab Eng 2014; 25:159-73. [DOI: 10.1016/j.ymben.2014.07.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 05/28/2014] [Accepted: 07/10/2014] [Indexed: 12/16/2022]
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30
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Höffner K, Barton PI. Design of Microbial Consortia for Industrial Biotechnology. PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON FOUNDATIONS OF COMPUTER-AIDED PROCESS DESIGN 2014. [DOI: 10.1016/b978-0-444-63433-7.50008-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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31
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Antoniewicz MR. Dynamic metabolic flux analysis—tools for probing transient states of metabolic networks. Curr Opin Biotechnol 2013; 24:973-8. [DOI: 10.1016/j.copbio.2013.03.018] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2012] [Revised: 03/21/2013] [Accepted: 03/22/2013] [Indexed: 12/16/2022]
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32
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Hanly TJ, Henson MA. Dynamic model-based analysis of furfural and HMF detoxification by pure and mixed batch cultures of S. cerevisiae and S. stipitis. Biotechnol Bioeng 2013; 111:272-84. [PMID: 23983023 DOI: 10.1002/bit.25101] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Revised: 08/13/2013] [Accepted: 08/15/2013] [Indexed: 01/16/2023]
Abstract
Inhibitory compounds that result from biomass hydrolysis are an obstacle to the efficient production of second-generation biofuels. Fermentative microorganisms can reduce compounds such as furfural and 5-hydroxymethyl furfural (HMF), but detoxification is accompanied by reduced growth rates and ethanol yields. In this study, we assess the effects of these furan aldehydes on pure and mixed yeast cultures consisting of a respiratory deficient mutant of Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis using dynamic flux balance analysis. Uptake kinetics and stoichiometric equations for the intracellular reduction reactions associated with each inhibitor were added to genome-scale metabolic reconstructions of the two yeasts. Further modification of the S. cerevisiae metabolic network was necessary to satisfactorily predict the amount of acetate synthesized during HMF reduction. Inhibitory terms that captured the adverse effects of the furan aldehydes and their corresponding alcohols on cell growth and ethanol production were added to attain qualitative agreement with batch experiments conducted for model development and validation. When the two yeasts were co-cultured in the presence of the furan aldehydes, inoculums that reduced the synthesis of highly toxic acetate produced by S. cerevisiae yielded the highest ethanol productivities. The model described here can be used to generate optimal fermentation strategies for the simultaneous detoxification and fermentation of lignocellulosic hydrolysates by S. cerevisiae and/or S. stipitis.
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Affiliation(s)
- Timothy J Hanly
- Department of Chemical Engineering, University of Massachusetts, Goessmann Lab 159, 686 N. Pleasant St., Amherst, Massachusetts, 01003-3110
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33
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Anesiadis N, Kobayashi H, Cluett WR, Mahadevan R. Analysis and design of a genetic circuit for dynamic metabolic engineering. ACS Synth Biol 2013; 2:442-52. [PMID: 23654263 DOI: 10.1021/sb300129j] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Recent advances in synthetic biology have equipped us with new tools for bioprocess optimization at the genetic level. Previously, we have presented an integrated in silico design for the dynamic control of gene expression based on a density-sensing unit and a genetic toggle switch. In the present paper, analysis of a serine-producing Escherichia coli mutant shows that an instantaneous ON-OFF switch leads to a maximum theoretical productivity improvement of 29.6% compared to the mutant. To further the design, global sensitivity analysis is applied here to a mathematical model of serine production in E. coli coupled with a genetic circuit. The model of the quorum sensing and the toggle switch involves 13 parameters of which 3 are identified as having a significant effect on serine concentration. Simulations conducted in this reduced parameter space further identified the optimal ranges for these 3 key parameters to achieve productivity values close to the maximum theoretical values. This analysis can now be used to guide the experimental implementation of a dynamic metabolic engineering strategy and reduce the time required to design the genetic circuit components.
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Affiliation(s)
- Nikolaos Anesiadis
- Department
of Chemical Engineering
and Applied Chemistry, University of Toronto, Canada, M5S 3E5
| | | | - William R. Cluett
- Department
of Chemical Engineering
and Applied Chemistry, University of Toronto, Canada, M5S 3E5
| | - Radhakrishnan Mahadevan
- Department
of Chemical Engineering
and Applied Chemistry, University of Toronto, Canada, M5S 3E5
- Institute of Biomaterials and
Biomedical Engineering, University of Toronto, Canada, M5S 3G9
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34
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Pasha I, Saeed F, Waqas K, Anjum FM, Arshad MU. Nutraceutical and functional scenario of wheat straw. Crit Rev Food Sci Nutr 2013; 53:287-95. [PMID: 23216000 DOI: 10.1080/10408398.2010.528080] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
In the era of nutrition, much focus has been remunerated to functional and nutraceutical foodstuffs. The health endorsing potential of such provisions is attributed to affluent phytochemistry. These dynamic constituents have functional possessions that are imperative for cereal industry. The functional and nutraceutical significance of variety of foods is often accredited to their bioactive molecules. Numerous components have been considered but wheat straw and its diverse components are of prime consideration. In this comprehensive dissertation, efforts are directed to elaborate the functional and nutraceutical importance of wheat straw. Wheat straw is lignocellulosic materials including cellulose, hemicellulose and lignin. It hold various bioactive compounds such as policosanols, phytosterols, phenolics, and triterpenoids, having enormous nutraceutical properties like anti-allergenic, anti-artherogenic, anti-inflammatory, anti-microbial, antioxidant, anti-thrombotic, cardioprotective and vasodilatory effects, antiviral, and anticancer. These compounds are protecting against various ailments like hypercholesterolemia, intermittent claudication, benign prostatic hyperplasia and cardiovascular diseases. Additionally, wheat straw has demonstrated successfully, low cost, renewable, versatile, widely distributed, easily available source for the production of biogas, bioethanol, and biohydrogen in biorefineries to enhance the overall effectiveness of biomass consumption in protected and eco-friendly environment. Furthermore, its role in enhancing the quality and extending the shelf life of bakery products through reducing the progression of staling and retrogradation is limelight of the article.
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Affiliation(s)
- Imran Pasha
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
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35
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Hanly TJ, Henson MA. Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures. BIOTECHNOLOGY FOR BIOFUELS 2013; 6:44. [PMID: 23548183 PMCID: PMC3776438 DOI: 10.1186/1754-6834-6-44] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2012] [Accepted: 03/12/2013] [Indexed: 05/11/2023]
Abstract
BACKGROUND A key step in any process that converts lignocellulose to biofuels is the efficient fermentation of both hexose and pentose sugars. The co-culture of respiratory-deficient Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis has been identified as a promising system for microaerobic ethanol production because S. cerevisiae only consumes glucose while S. stipitis efficiently converts xylose to ethanol. RESULTS To better predict how these two yeasts behave in batch co-culture and to optimize system performance, a dynamic flux balance model describing co-culture metabolism was developed from genome-scale metabolic reconstructions of the individual organisms. First a dynamic model was developed for each organism by estimating substrate uptake kinetic parameters from batch pure culture data and evaluating model extensibility to different microaerobic growth conditions. The co-culture model was constructed by combining the two individual models assuming a cellular objective of total growth rate maximization. To obtain accurate predictions of batch co-culture data collected at different microaerobic conditions, the S. cerevisiae maximum glucose uptake rate was reduced from its pure culture value to account for more efficient S. stipitis glucose uptake in co-culture. The dynamic co-culture model was used to predict the inoculum concentration and aeration level that maximized batch ethanol productivity. The model predictions were validated with batch co-culture experiments performed at the optimal conditions. Furthermore, the dynamic model was used to predict how engineered improvements to the S. stipitis xylose transport system could improve co-culture ethanol production. CONCLUSIONS These results demonstrate the utility of the dynamic co-culture metabolic model for guiding process and metabolic engineering efforts aimed at increasing microaerobic ethanol production from glucose/xylose mixtures.
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Affiliation(s)
- Timothy J Hanly
- Department of Chemical Engineering, University of Massachusetts, Goessmann Lab 159, 686 N. Pleasant St, Amherst, MA 01003-3110, USA
| | - Michael A Henson
- Department of Chemical Engineering, University of Massachusetts, Goessmann Lab 159, 686 N. Pleasant St, Amherst, MA 01003-3110, USA
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37
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Mahadevan R, Henson MA. Genome-based Modeling and Design of Metabolic Interactions in Microbial Communities. Comput Struct Biotechnol J 2012; 3:e201210008. [PMID: 24688668 PMCID: PMC3962185 DOI: 10.5936/csbj.201210008] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2012] [Revised: 10/15/2012] [Accepted: 10/18/2012] [Indexed: 11/22/2022] Open
Abstract
Biotechnology research is traditionally focused on individual microbial strains that are perceived to have the necessary metabolic functions, or the capability to have these functions introduced, to achieve a particular task. For many important applications, the development of such omnipotent microbes is an extremely challenging if not impossible task. By contrast, nature employs a radically different strategy based on synergistic combinations of different microbial species that collectively achieve the desired task. These natural communities have evolved to exploit the native metabolic capabilities of each species and are highly adaptive to changes in their environments. However, microbial communities have proven difficult to study due to a lack of suitable experimental and computational tools. With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.
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Affiliation(s)
- Radhakrishnan Mahadevan
- 200 College Street, 326 Wallberg Building, Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S3E5, Canada
| | - Michael A Henson
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, United States of America
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38
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Jouhten P. Metabolic modelling in the development of cell factories by synthetic biology. Comput Struct Biotechnol J 2012; 3:e201210009. [PMID: 24688669 PMCID: PMC3962133 DOI: 10.5936/csbj.201210009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 11/05/2012] [Accepted: 11/07/2012] [Indexed: 11/22/2022] Open
Abstract
Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.
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Affiliation(s)
- Paula Jouhten
- VTT Technical Research Centre of Finland, Tietotie 2, 02044 VTT, Espoo, Finland
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39
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Höffner K, Harwood SM, Barton PI. A reliable simulator for dynamic flux balance analysis. Biotechnol Bioeng 2012; 110:792-802. [DOI: 10.1002/bit.24748] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 09/21/2012] [Accepted: 09/25/2012] [Indexed: 12/16/2022]
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40
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Chen N, Koumpouras GC, Polizzi KM, Kontoravdi C. Genome-based kinetic modeling of cytosolic glucose metabolism in industrially relevant cell lines: Saccharomyces cerevisiae and Chinese hamster ovary cells. Bioprocess Biosyst Eng 2012; 35:1023-33. [PMID: 22286123 DOI: 10.1007/s00449-012-0687-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Accepted: 01/13/2012] [Indexed: 01/23/2023]
Abstract
Model-based analysis of cellular metabolism can facilitate our understanding of intracellular kinetics and aid the improvement of cell growth and biological product manufacturing. In this paper, a model-based kinetic study of cytosolic glucose metabolism for two industrially relevant cell lines, Saccharomyces cerevisiae and Chinese hamster ovary (CHO) cells, based on enzyme genetic presence and expression information is described. We have reconstructed the cytosolic glucose metabolism map for S. cerevisiae and CHO cells, containing 18 metabolites and 18 enzymes using information from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Based on this map, we have developed akinetic mathematical model for the pathways involved,considering regulation and/or inhibition by products orco-substrates. The values of the maximum rates of reactions(V(max)) were estimated based on kinetic parameter information and metabolic flux analysis results available in literature, and the resulting simulation results for steady-state metabolite concentrations are in good agreement with published experimental data. Finally, the model was used to analyse how the production of DHAP, an important intermediate in fine chemicals synthesis, could be increased using gene knockout.
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Affiliation(s)
- Ning Chen
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
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41
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Setoodeh P, Jahanmiri A, Eslamloueyan R. Hybrid neural modeling framework for simulation and optimization of diauxie-involved fed-batch fermentative succinate production. Chem Eng Sci 2012. [DOI: 10.1016/j.ces.2012.06.031] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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42
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Hanscho M, Ruckerbauer DE, Chauhan N, Hofbauer HF, Krahulec S, Nidetzky B, Kohlwein SD, Zanghellini J, Natter K. Nutritional requirements of the BY series of Saccharomyces cerevisiae strains for optimum growth. FEMS Yeast Res 2012; 12:796-808. [PMID: 22780918 DOI: 10.1111/j.1567-1364.2012.00830.x] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2012] [Revised: 07/04/2012] [Accepted: 07/06/2012] [Indexed: 12/19/2022] Open
Abstract
Among the vast variety of Saccharomyces cerevisiae strains, the BY family is particularly important because the widely used deletion collections are based on this background. Here we demonstrate that some standard growth media recipes require substantial modifications to provide optimum growth conditions for auxotrophic BY strains and to avoid growth arrest before glucose is depleted. In addition to the essential supplements that are required to satisfy auxotrophic requirements, we found the four amino acids phenylalanine, glutamic acid, serine, and threonine to be indispensable for optimum growth, despite the fact that BY is 'prototrophic' for these amino acids. Interestingly, other widely used S. cerevisiae strains, such as strains of the CEN.PK family, are less sensitive to lack of the described supplements. Furthermore, we found that the concentration of inositol in yeast nitrogen base is too low to support fast proliferation of yeast cultures until glucose is exhausted. Depletion of inositol during exponential growth induces characteristic changes, namely a decrease in glucose uptake and maximum specific growth rate, increased cell size, reduced viability, and accumulation of lipid storage pools. Thus, several of the existing growth media recipes need to be revised to achieve optimum growth conditions for BY-derived strains.
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Affiliation(s)
- Michael Hanscho
- Institute of Molecular Biosciences, University Graz, Graz, Austria
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43
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Copeland WB, Bartley BA, Chandran D, Galdzicki M, Kim KH, Sleight SC, Maranas CD, Sauro HM. Computational tools for metabolic engineering. Metab Eng 2012; 14:270-80. [PMID: 22629572 PMCID: PMC3361690 DOI: 10.1016/j.ymben.2012.03.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.
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Affiliation(s)
- Wilbert B Copeland
- Department of Bioengineering, University of Washington, Seattle, WA 98195-5061, USA.
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44
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Toya Y, Kono N, Arakawa K, Tomita M. Metabolic flux analysis and visualization. J Proteome Res 2012; 10:3313-23. [PMID: 21815690 DOI: 10.1021/pr2002885] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
One of the ultimate goals of systems biology research is to obtain a comprehensive understanding of the control mechanisms of complex cellular metabolisms. Metabolic Flux Analysis (MFA) is a important method for the quantitative estimation of intracellular metabolic flows through metabolic pathways and the elucidation of cellular physiology. The primary challenge in the use of MFA is that many biological networks are underdetermined systems; it is therefore difficult to narrow down the solution space from the stoichiometric constraints alone. In this tutorial, we present an overview of Flux Balance Analysis (FBA) and (13)C-Metabolic Flux Analysis ((13)C-MFA), both of which are frequently used to solve such underdetermined systems, and we demonstrate FBA and (13)C-MFA using the genome-scale model and the central carbon metabolism model, respectively. Furthermore, because such comprehensive study of intracellular fluxes is inherently complex, we subsequently introduce various pathway mapping and visualization tools to facilitate understanding of these data in the context of the pathways. Specific visualization of MFA results using the BioCyc Omics Viewer and Pathway Projector are shown as illustrative examples.
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Affiliation(s)
- Yoshihiro Toya
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0017, Japan
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45
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Abstract
Scale-up from shake flasks to bioreactors allows for the more reproducible, high-yielding production of recombinant proteins in yeast. The ability to control growth conditions through real-time monitoring facilitates further optimization of the process. The setup of a 3-L stirred-tank bioreactor for such an application is described.
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Affiliation(s)
- Sarah J Routledge
- School of Life and Health Sciences, Aston University, Birmingham, UK.
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46
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Eslamloueyan R, Setoodeh P. OPTIMIZATION OF FED-BATCH RECOMBINANT YEAST FERMENTATION FOR ETHANOL PRODUCTION USING A REDUCED DYNAMIC FLUX BALANCE MODEL BASED ON ARTIFICIAL NEURAL NETWORKS. CHEM ENG COMMUN 2011. [DOI: 10.1080/00986445.2011.560512] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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47
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Dynamic flux balance modeling of S. cerevisiae and E. coli co-cultures for efficient consumption of glucose/xylose mixtures. Appl Microbiol Biotechnol 2011; 93:2529-41. [PMID: 22005741 DOI: 10.1007/s00253-011-3628-1] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2011] [Revised: 09/19/2011] [Accepted: 09/30/2011] [Indexed: 01/30/2023]
Abstract
Current researches into the production of biochemicals from lignocellulosic feedstocks are focused on the identification and engineering of individual microbes that utilize complex sugar mixtures. Microbial consortia represent an alternative approach that has the potential to better exploit individual species capabilities for substrate uptake and biochemical production. In this work, we construct and experimentally validate a dynamic flux balance model of a Saccharomyces cerevisiae and Escherichia coli co-culture designed for efficient aerobic consumption of glucose/xylose mixtures. Each microbe is a substrate specialist, with wild-type S. cerevisiae consuming only glucose and engineered E. coli strain ZSC113 consuming only xylose, to avoid diauxic growth commonly observed in individual microbes. Following experimental identification of a common pH and temperature for optimal co-culture batch growth, we demonstrate that pure culture models developed for optimal growth conditions can be adapted to the suboptimal, common growth condition by adjustment of the non-growth associated ATP maintenance of each microbe. By comparing pure culture model predictions to co-culture experimental data, the inhibitory effect of ethanol produced by S. cerevisiae on E. coli growth was found to be the only interaction necessary to include in the co-culture model to generate accurate batch profile predictions. Co-culture model utility was demonstrated by predicting initial cell concentrations that yield simultaneous glucose and xylose exhaustion for different sugar mixtures. Successful experimental validation of the model predictions demonstrated that steady-state metabolic reconstructions developed for individual microbes can be adapted to develop dynamic flux balance models of microbial consortia for the production of renewable chemicals.
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Leppävuori JT, Domach MM, Biegler LT. Parameter Estimation in Batch Bioreactor Simulation Using Metabolic Models: Sequential Solution with Direct Sensitivities. Ind Eng Chem Res 2011. [DOI: 10.1021/ie201020g] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Juha T. Leppävuori
- VTT Technical Research Centre of Finland, P.O. Box 1000, FI-02044, Finland
| | - Michael M. Domach
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Lorenz T. Biegler
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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Purification of high strength wastewater originating from bioethanol production with simultaneous biogas production. World J Microbiol Biotechnol 2011. [DOI: 10.1007/s11274-011-0746-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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50
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Nolan RP, Lee K. Dynamic model of CHO cell metabolism. Metab Eng 2011; 13:108-24. [DOI: 10.1016/j.ymben.2010.09.003] [Citation(s) in RCA: 110] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2010] [Revised: 09/28/2010] [Accepted: 09/29/2010] [Indexed: 10/19/2022]
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