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Hosoda S, Iwata H, Miura T, Tanabe M, Okada T, Mochizuki A, Sato M. BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks. BMC Bioinformatics 2024; 25:297. [PMID: 39256657 PMCID: PMC11389226 DOI: 10.1186/s12859-024-05921-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 09/04/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. RESULTS We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. CONCLUSIONS We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.
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Affiliation(s)
- Shion Hosoda
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan.
| | - Hisashi Iwata
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
| | - Takuya Miura
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
| | - Maiko Tanabe
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
| | - Takashi Okada
- Laboratory of Mathematical Biology, Institute for Life and Medical Sciences, Kyoto University, Kyoto-shi, Kyoto, 606-8507, Japan
| | - Atsushi Mochizuki
- Laboratory of Mathematical Biology, Institute for Life and Medical Sciences, Kyoto University, Kyoto-shi, Kyoto, 606-8507, Japan
| | - Miwa Sato
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
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2
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Li G, Liu L, Du W, Cao H. Local flux coordination and global gene expression regulation in metabolic modeling. Nat Commun 2023; 14:5700. [PMID: 37709734 PMCID: PMC10502109 DOI: 10.1038/s41467-023-41392-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.
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Affiliation(s)
- Gaoyang Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Li Liu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Huansheng Cao
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China.
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3
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Martin JP, Rasor BJ, DeBonis J, Karim AS, Jewett MC, Tyo KEJ, Broadbelt LJ. A dynamic kinetic model captures cell-free metabolism for improved butanol production. Metab Eng 2023; 76:133-145. [PMID: 36724840 DOI: 10.1016/j.ymben.2023.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/30/2022] [Accepted: 01/25/2023] [Indexed: 01/30/2023]
Abstract
Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.
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Affiliation(s)
- Jacob P Martin
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Blake J Rasor
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Jonathon DeBonis
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Ashty S Karim
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Michael C Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA.
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4
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Lee JY, Styczynski MP. Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework. Sci Rep 2022; 12:762. [PMID: 35031616 PMCID: PMC8760257 DOI: 10.1038/s41598-021-03934-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/08/2021] [Indexed: 11/29/2022] Open
Abstract
Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework's success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.
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Affiliation(s)
- Justin Y. Lee
- grid.213917.f0000 0001 2097 4943School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA USA
| | - Mark P. Styczynski
- grid.213917.f0000 0001 2097 4943School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA USA
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5
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Kloska S, Pałczyński K, Marciniak T, Talaśka T, Nitz M, Wysocki BJ, Davis P, Wysocki TA. Queueing theory model of Krebs cycle. Bioinformatics 2021; 37:2912-2919. [PMID: 33724355 DOI: 10.1093/bioinformatics/btab177] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/08/2021] [Accepted: 03/11/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Queueing theory can be effective in simulating biochemical reactions taking place in living cells, and the article paves a step toward development of a comprehensive model of cell metabolism. Such a model could help to accelerate and reduce costs for developing and testing investigational drugs reducing number of laboratory animals needed to evaluate drugs. RESULTS The article presents a Krebs cycle model based on queueing theory. The model allows for tracking of metabolites concentration changes in real time. To validate the model, a drug-induced inhibition affecting activity of enzymes involved in Krebs cycle was simulated and compared with available experimental data. AVAILABILITYAND IMPLEMENTATION The source code is freely available for download at https://github.com/UTP-WTIiE/KrebsCycleUsingQueueingTheory, implemented in C# supported in Linux or MS Windows. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sylwester Kloska
- Faculty of Medicine, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-067 Bydgoszcz, Poland
| | - Krzysztof Pałczyński
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
| | - Tomasz Marciniak
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
| | - Tomasz Talaśka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
| | - Marissa Nitz
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA
| | - Beata J Wysocki
- Department of Biology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Paul Davis
- Department of Biology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Tadeusz A Wysocki
- Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland.,Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA
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6
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Shimizu H, Toya Y. Recent advances in metabolic engineering-integration of in silico design and experimental analysis of metabolic pathways. J Biosci Bioeng 2021; 132:429-436. [PMID: 34509367 DOI: 10.1016/j.jbiosc.2021.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/07/2021] [Indexed: 11/29/2022]
Abstract
Microorganisms are widely used to produce valuable compounds. Because thousands of metabolic reactions occur simultaneously and many metabolic reactions are related to target production and cell growth, the development of a rational design method for metabolic pathway modification to optimize target production is needed. In this paper, recent advances in metabolic engineering are reviewed, specifically considering computational pathway modification design and experimental evaluation of metabolic fluxes by 13C-metabolic flux analysis. Computational tools for seeking effective gene deletion targets and recruiting heterologous genes are described in flux balance analysis approaches. A kinetic model and adaptive laboratory evolution were applied to identify and eliminate the rate-limiting step in metabolic pathways. Data science-based approaches for process monitoring and control are described to maximize the performance of engineered cells in bioreactors.
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Affiliation(s)
- Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
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7
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Foster CJ, Wang L, Dinh HV, Suthers PF, Maranas CD. Building kinetic models for metabolic engineering. Curr Opin Biotechnol 2020; 67:35-41. [PMID: 33360621 DOI: 10.1016/j.copbio.2020.11.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/09/2020] [Accepted: 11/22/2020] [Indexed: 12/16/2022]
Abstract
Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this review we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.
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Affiliation(s)
- Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, Univesity Park, PA, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, Univesity Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.
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8
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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9
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Kitamura S, Shimizu H, Toya Y. Identification of a rate-limiting step in a metabolic pathway using the kinetic model and in vitro experiment. J Biosci Bioeng 2020; 131:271-276. [PMID: 33168471 DOI: 10.1016/j.jbiosc.2020.10.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 11/16/2022]
Abstract
Identification of the rate-limiting step in a metabolic pathway is an important challenge in metabolic engineering for enhancing pathway flow. Although specific enzyme activities (Vmax) provide valuable clues for the identification, it is time-consuming and difficult to measure multiple enzymes in the pathway because different assay protocols are required for each enzyme. In the present study, we propose a method to simultaneously determine the Vmax values of multiple enzymes using a kinetic model with a time course of the intermediate concentrations through an in vitro experiment. To demonstrate this method, nine glycolysis reactions for converting glucose-6-phosphate (G6P) to pyruvate in Escherichia coli were considered. In a reaction mixture containing G6P and cofactors, glycolysis was initiated by adding a crude cell extract obtained from stationary phase cells. The Vmax values were optimized to minimize the difference between the measured and simulated time-courses using a kinetic model. Metabolic control analysis using the kinetic model with the estimated Vmax values revealed that fructose bisphosphate aldolase (FBA) was the rate-limiting step in the upper part of glycolysis. The addition of FBA in the reaction mixture successfully increased the glycolytic flux in vitro. Furthermore, in vivo, the specific glucose consumption rate of an FBA overexpression strain was 1.4 times higher than that of the control strain during the stationary phase. These results confirmed that FBA was the rate-limiting step in glycolysis under the stationary phase. This approach provides Vmax values of multiple enzymes in a pathway for metabolic control analysis with a kinetic model.
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Affiliation(s)
- Sayaka Kitamura
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
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10
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Ortega-Quintana FA, Trujillo-Roldán MA, Botero-Castro H, Alvarez H. Modeling the interaction between the central carbon metabolism of Escherichia coli and bioreactor culture media. Biochem Eng J 2020. [DOI: 10.1016/j.bej.2020.107753] [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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11
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Hwang J, Hari A, Cheng R, Gardner JG, Lobo D. Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus. Biotechnol Bioeng 2020; 117:3876-3890. [PMID: 32833226 DOI: 10.1002/bit.27544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 07/11/2020] [Accepted: 08/19/2020] [Indexed: 12/12/2022]
Abstract
Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.
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Affiliation(s)
- Jeanice Hwang
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Raymond Cheng
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Jeffrey G Gardner
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
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12
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Amera GM, Khan RJ, Jha RK, Pathak A, Muthukumaran J, Singh AK. Prioritization of Mur family drug targets against A. baumannii and identification of their homologous proteins through molecular phylogeny, primary sequence, and structural analysis. J Genet Eng Biotechnol 2020; 18:33. [PMID: 32725318 PMCID: PMC7387395 DOI: 10.1186/s43141-020-00048-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/01/2020] [Indexed: 12/22/2022]
Abstract
Background The World Health Organization (WHO) report stated that Acinetobacter baumannii had been classified as one of the most important pathogenic bacteria causing nosocomial infection in hospital patients due to multi-drug resistance (MDR). It is vital to find out new bacterial drug targets and annotated their structure and function for the exploration of new anti-bacterial agents. The present study utilized a systematic route to prioritize the potential drug targets that belong to Mur family of Acinetobacter baumannii and identify their homologous proteins using a computational approach such as sequence similarity search, multiple sequence alignment, phylogenetic analysis, protein sequence, and protein structure analysis. Results From the results of protein sequence analysis of eight Mur family proteins, they divided into three main enzymatic classes namely transferases (MurG, MurA and MraY), ligases (MurC, MurD, MurE, and MurF), and oxidoreductase (MurB). Based on the results of intra-comparative protein sequence analysis and enzymatic classification, we have chosen MurB, MurE, and MurG as the prioritized drug targets from A. baumannii and subjected them for further detailed studies of inter-species comparison. This inter-species comparison help us to explore the sequential and structural properties of homologous proteins in other species and hence, opens a gateway for new target identification and using common inhibitor for different bacterial species caused by various diseases. The pairwise sequence alignment results between A. baumannii’s MurB with A. calcoaceticus’s MurB, A. baumannii’s MurE with A. seifertii’s MurE, and A. baumannii’s MurG with A. pittii’s MurG showed that every group of the proteins are highly similar with each other and they showed sequence identity of 95.7% and sequence similarity of 97.2%. Conclusion Together with the results of secondary and three-dimensional structure predictions explained that three selected proteins (MurB, MurE, and MurG) from A. baumannii and their related proteins (AcMurB, AsMurE, and ApMurG) belong to mixed αβ class and they are very similar.
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Affiliation(s)
- Gizachew Muluneh Amera
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, UP, 201310, India
| | - Rameez Jabeer Khan
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, UP, 201310, India
| | - Rajat Kumar Jha
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, UP, 201310, India
| | - Amita Pathak
- Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Jayaraman Muthukumaran
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, UP, 201310, India
| | - Amit Kumar Singh
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida, UP, 201310, India.
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13
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Macklin DN, Ahn-Horst TA, Choi H, Ruggero NA, Carrera J, Mason JC, Sun G, Agmon E, DeFelice MM, Maayan I, Lane K, Spangler RK, Gillies TE, Paull ML, Akhter S, Bray SR, Weaver DS, Keseler IM, Karp PD, Morrison JH, Covert MW. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science 2020; 369:eaav3751. [PMID: 32703847 PMCID: PMC7990026 DOI: 10.1126/science.aav3751] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/28/2019] [Accepted: 05/26/2020] [Indexed: 12/24/2022]
Abstract
The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.
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Affiliation(s)
- Derek N Macklin
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Travis A Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Heejo Choi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Nicholas A Ruggero
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Javier Carrera
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - John C Mason
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Mialy M DeFelice
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Inbal Maayan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Keara Lane
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Ryan K Spangler
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Taryn E Gillies
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Morgan L Paull
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Sajia Akhter
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Samuel R Bray
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | | | | | - Jerry H Morrison
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
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14
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Matsuoka Y, Kurata H. Computer-Aided Rational Design of Efficient NADPH Production System by Escherichia coli pgi Mutant Using a Mixture of Glucose and Xylose. Front Bioeng Biotechnol 2020; 8:277. [PMID: 32318559 PMCID: PMC7154054 DOI: 10.3389/fbioe.2020.00277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/16/2020] [Indexed: 12/02/2022] Open
Abstract
Lignocellulosic biomass can be hydrolyzed into two major sugars of glucose and xylose, and thus the strategy for the efficient consumption of both sugars is highly desirable. NADPH is the essential molecule for the production of industrially important value-added chemicals, and thus its availability is quite important. Escherichia coli mutant lacking the pgi gene encoding phosphoglucose isomerase (Pgi) has been preferentially used to overproduce the NADPH. However, there exists a disadvantage that the cell growth rate becomes low for the mutant grown on glucose. This limits the efficient NADPH production, and therefore, it is quite important to investigate how addition of different carbon source such as xylose (other than glucose) effectively improves the NADPH production. In this study, we have developed a kinetic model to propose an efficient NADPH production system using E. coli pgi-knockout mutant with a mixture of glucose and xylose. The proposed system adds xylose to glucose medium to recover the suppressed growth of the pgi mutant, and determines the xylose content to maximize the NADPH productivity. Finally, we have designed a mevalonate (MVA) production system by implementing ArcA overexpression into the pgi-knockout mutant using a mixture of glucose and xylose. In addition to NADPH overproduction, the accumulation of acetyl-CoA (AcCoA) is necessary for the efficient MVA production. In the present study, therefore, we considered to overexpress ArcA, where ArcA overexpression suppresses the TCA cycle, causing the overflow of AcCoA, a precursor of MVA. We predicted the xylose content that maximizes the MVA production. This approach demonstrates the possibility of a great progress in the computer-aided rational design of the microbial cell factories for useful metabolite production.
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Affiliation(s)
- Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Japan.,Biomedical Informatics R&D Center, Kyushu Institute of Technology, Iizuka, Japan
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15
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Steel M, Hordijk W, Xavier JC. Autocatalytic networks in biology: structural theory and algorithms. J R Soc Interface 2020; 16:20180808. [PMID: 30958202 DOI: 10.1098/rsif.2018.0808] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Self-sustaining autocatalytic networks play a central role in living systems, from metabolism at the origin of life, simple RNA networks and the modern cell, to ecology and cognition. A collectively autocatalytic network that can be sustained from an ambient food set is also referred to more formally as a 'reflexively autocatalytic food-generated' (RAF) set. In this paper, we first investigate a simplified setting for studying RAFs, which is nevertheless relevant to real biochemistry and which allows an exact mathematical analysis based on graph-theoretic concepts. This, in turn, allows for the development of efficient (polynomial-time) algorithms for questions that are computationally intractable (NP-hard) in the general RAF setting. We then show how this simplified setting for RAF systems leads naturally to a more general notion of RAFs that are 'generative' (they can be built up from simpler RAFs) and for which efficient algorithms carry over to this more general setting. Finally, we show how classical RAF theory can be extended to deal with ensembles of catalysts as well as the assignment of rates to reactions according to which catalysts (or combinations of catalysts) are available.
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Affiliation(s)
- Mike Steel
- 1 Biomathematics Research Centre, University of Canterbury , Christchurch , New Zealand
| | - Wim Hordijk
- 2 Institute for Advanced Study, University of Amsterdam , Amsterdam , The Netherlands
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16
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Kopp J, Slouka C, Spadiut O, Herwig C. The Rocky Road From Fed-Batch to Continuous Processing With E. coli. Front Bioeng Biotechnol 2019; 7:328. [PMID: 31824931 PMCID: PMC6880763 DOI: 10.3389/fbioe.2019.00328] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 10/28/2019] [Indexed: 12/21/2022] Open
Abstract
Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space-time yield, and especially targeting constant quality attributes. In the 1950s, the term "chemostat" was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.
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Affiliation(s)
- Julian Kopp
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna, Austria
| | - Christoph Slouka
- Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna, Austria
| | - Oliver Spadiut
- Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna, Austria
| | - Christoph Herwig
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna, Austria
- Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna, Austria
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17
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Kurata H. Self-replenishment cycles generate a threshold response. Sci Rep 2019; 9:17139. [PMID: 31748624 PMCID: PMC6868230 DOI: 10.1038/s41598-019-53589-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 11/02/2019] [Indexed: 11/10/2022] Open
Abstract
Many metabolic cycles, including the tricarboxylic acid cycle, glyoxylate cycle, Calvin cycle, urea cycle, coenzyme recycling, and substrate cycles, are well known to catabolize and anabolize different metabolites for efficient energy and mass conversion. In terms of stoichiometric structure, this study explicitly identifies two types of metabolic cycles. One is the well-known, elementary cycle that converts multiple substrates into different products and recycles one of the products as a substrate, where the recycled substrate is supplied from the outside to run the cycle. The other is the self-replenishment cycle that merges multiple substrates into two or multiple identical products and reuses one of the products as a substrate. The substrates are autonomously supplied within the cycle. This study first defines the self-replenishment cycles that many scientists have overlooked despite its functional importance. Theoretical analysis has revealed the design principle of the self-replenishment cycle that presents a threshold response without any bistability nor cooperativity. To verify the principle, three detailed kinetic models of self-replenishment cycles embedded in an E. coli metabolic system were simulated. They presented the threshold response or digital switch-like function that steeply shift metabolic status.
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Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka, Japan. .,Biomedical Informatics R&D Center, Kyushu Institute of Technology, Fukuoka, Japan.
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18
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Foster CJ, Gopalakrishnan S, Antoniewicz MR, Maranas CD. From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline. PLoS Comput Biol 2019; 15:e1007319. [PMID: 31504032 PMCID: PMC6759195 DOI: 10.1371/journal.pcbi.1007319] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 09/24/2019] [Accepted: 08/02/2019] [Indexed: 12/02/2022] Open
Abstract
Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.
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Affiliation(s)
- Charles J. Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Saratram Gopalakrishnan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Maciek R. Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware. Newark, Delaware, United States of America
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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19
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An energetic reformulation of kinetic rate laws enables scalable parameter estimation for biochemical networks. J Theor Biol 2019; 461:145-156. [DOI: 10.1016/j.jtbi.2018.10.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 09/20/2018] [Accepted: 10/19/2018] [Indexed: 11/18/2022]
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20
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Liu D, Mannan AA, Han Y, Oyarzún DA, Zhang F. Dynamic metabolic control: towards precision engineering of metabolism. ACTA ACUST UNITED AC 2018; 45:535-543. [DOI: 10.1007/s10295-018-2013-9] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 01/13/2018] [Indexed: 12/20/2022]
Abstract
Abstract
Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.
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Affiliation(s)
- Di Liu
- 0000 0001 2355 7002 grid.4367.6 Department of Energy, Environmental and Chemical Engineering Washington University in St. Louis 63130 St. Louis MO USA
| | - Ahmad A Mannan
- 0000 0001 2113 8111 grid.7445.2 Department of Mathematics Imperial College London SW7 2AZ London UK
| | - Yichao Han
- 0000 0001 2355 7002 grid.4367.6 Department of Energy, Environmental and Chemical Engineering Washington University in St. Louis 63130 St. Louis MO USA
| | - Diego A Oyarzún
- 0000 0001 2113 8111 grid.7445.2 Department of Mathematics Imperial College London SW7 2AZ London UK
| | - Fuzhong Zhang
- 0000 0001 2355 7002 grid.4367.6 Department of Energy, Environmental and Chemical Engineering Washington University in St. Louis 63130 St. Louis MO USA
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