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Ziegler AL, Ullmann L, Boßmann M, Stein KL, Liebal UW, Mitsos A, Blank LM. Itaconic acid production by co-feeding of Ustilago maydis: A combined approach of experimental data, design of experiments, and metabolic modeling. Biotechnol Bioeng 2024. [PMID: 38494797 DOI: 10.1002/bit.28693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/19/2024]
Abstract
Itaconic acid is a platform chemical with a range of applications in polymer synthesis and is also discussed for biofuel production. While produced in industry from glucose or sucrose, co-feeding of glucose and acetate was recently discussed to increase itaconic acid production by the smut fungus Ustilago maydis. In this study, we investigate the optimal co-feeding conditions by interlocking experimental and computational methods. Flux balance analysis indicates that acetate improves the itaconic acid yield up to a share of 40% acetate on a carbon molar basis. A design of experiment results in the maximum yield of 0.14 itaconic acid per carbon source from 100 g L - 1 $\,\text{g L}{}^{-1}$ glucose and 12 g L - 1 $\,\text{g L}{}^{-1}$ acetate. The yield is improved by around 22% when compared to feeding of glucose as sole carbon source. To further improve the yield, gene deletion targets are discussed that were identified using the metabolic optimization tool OptKnock. The study contributes ideas to reduce land use for biotechnology by incorporating acetate as co-substrate, a C2-carbon source that is potentially derived from carbon dioxide.
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Affiliation(s)
- Anita L Ziegler
- Aachener Verfahrenstechnik - Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany
| | - Lena Ullmann
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
| | - Manuel Boßmann
- Aachener Verfahrenstechnik - Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany
| | - Karla L Stein
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
| | - Ulf W Liebal
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
| | - Alexander Mitsos
- Aachener Verfahrenstechnik - Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany
- JARA-ENERGY, Aachen, Germany
- Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10), Forschungszentrum Jü lich GmbH, Jü lich, Germany
| | - Lars M Blank
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
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Neves D, Liebal UW, Nies SC, Alter TB, Pitzler C, Blank LM, Ebert BE. Cross-Species Synthetic Promoter Library: Finding Common Ground between Pseudomonas taiwanensis VLB120 and Escherichia coli. ACS Synth Biol 2023. [PMID: 37341594 DOI: 10.1021/acssynbio.3c00084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
The potential of nonmodel organisms for industrial biotechnology is increasingly becoming evident since advances in systems and synthetic biology have made it possible to explore their unique traits. However, the lack of adequately characterized genetic elements that drive gene expression impedes benchmarking nonmodel with model organisms. Promoters are one of the genetic elements that contribute significantly to gene expression, but information about their performance in different organisms is limited. This work addresses this bottleneck by characterizing libraries of synthetic σ70-dependent promoters controlling the expression of msfGFP, a monomeric, superfolder green fluorescent protein, in both Escherichia coli TOP10 and Pseudomonas taiwanensis VLB120, a less explored microbe with industrially attractive attributes. We adopted a standardized method for comparing gene promoter strength across species and laboratories. Our approach uses fluorescein calibration and adjusts for cell growth variation, enabling accurate cross-species comparisons. The quantitative description of promoter strength is a valuable expansion of P. taiwanensis VLB120's genetic toolbox, while the comparison with the performance in E. coli facilitates the evaluation of P. taiwanensis VLB120's potential as a chassis for biotechnology applications.
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Affiliation(s)
- Dário Neves
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Ulf W Liebal
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Salome C Nies
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Tobias B Alter
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Christian Pitzler
- Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany
| | - Lars M Blank
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Birgitta E Ebert
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
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Liebal UW, Schimassek R, Broderius I, Maaßen N, Vogelgesang A, Weyers P, Blank LM. Biotechnology Data Analysis Training with Jupyter Notebooks. J Microbiol Biol Educ 2023; 24:00113-22. [PMID: 37089214 PMCID: PMC10117103 DOI: 10.1128/jmbe.00113-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/20/2022] [Indexed: 05/03/2023]
Abstract
Biotechnology has experienced innovations in analytics and data processing. As the volume of data and its complexity grow, new computational procedures for extracting information are being developed. However, the rate of change outpaces the adaptation of biotechnology curricula, necessitating new teaching methodologies to equip biotechnologists with data analysis abilities. To simulate experimental data, we created a virtual organism simulator (silvio) by combining diverse cellular and subcellular microbial models. With the silvio Python package, we constructed a computer-based instructional workflow to teach growth curve data analysis, promoter sequence design, and expression rate measurement. The instructional workflow is a Jupyter Notebook with background explanations and Python-based experiment simulations combined. The data analysis is conducted either within the Notebook in Python or externally with Excel. This instructional workflow was separately implemented in two distance courses for Master's students in biology and biotechnology with assessment of the pedagogic efficiency. The concept of using virtual organism simulations that generate coherent results across different experiments can be used to construct consistent and motivating case studies for biotechnological data literacy.
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Affiliation(s)
- Ulf W. Liebal
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Rafael Schimassek
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Iris Broderius
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Nicole Maaßen
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Alina Vogelgesang
- Center for Learning Services, RWTH Aachen University, Aachen, Germany
| | - Philipp Weyers
- Center for Learning Services, RWTH Aachen University, Aachen, Germany
| | - Lars M. Blank
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
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Becker J, Liebal UW, Phan AN, Ullmann L, Blank LM. Renewable carbon sources to biochemicals and -fuels: contributions of the smut fungi Ustilaginaceae. Curr Opin Biotechnol 2023; 79:102849. [PMID: 36446145 DOI: 10.1016/j.copbio.2022.102849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 11/27/2022]
Abstract
The global demand for food, fuels, and chemicals increases annually. Using renewable C-sources (i.e. biomass, CO2, and organic waste) is a prerequisite for a future free of fossil carbon. The smut fungi Ustilaginaceae naturally produce a versatile spectrum of valuable products, such as organic acids, polyols, and glycolipids, applicable in the food, energy, chemistry, and pharmaceutical sector. Combined with the use of alternative (co-)substrates (e.g. acetate, butanediol, formate, and glycerol), these microorganisms offer excellent potential for industrial biotechnology, thereby overcoming central challenges humankind faces, including CO2 release and land use. Here, we provide insight into fundamental production capacities, present genetic modifications that improve the biotechnical application, and review recent high-performance engineering of Ustilaginaceae toward relevant platform chemicals.
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Affiliation(s)
- Johanna Becker
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Ulf W Liebal
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - An Nt Phan
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Lena Ullmann
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Lars M Blank
- iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany.
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Liebal UW, Ullmann L, Lieven C, Kohl P, Wibberg D, Zambanini T, Blank LM. Ustilago maydis Metabolic Characterization and Growth Quantification with a Genome-Scale Metabolic Model. J Fungi (Basel) 2022; 8:jof8050524. [PMID: 35628779 PMCID: PMC9147497 DOI: 10.3390/jof8050524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 11/16/2022] Open
Abstract
Ustilago maydis is an important plant pathogen that causes corn smut disease and serves as an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of the organism’s environmental adaptation and a full use of its metabolic potential. Here, we report the first genome-scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, and the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% annotated genes (6909) in the sequenced genome. Substrate utilization was corrected by BIOLOG phenotype arrays, and exponential batch cultivations were used to test growth predictions. The growth data revealed a decrease in glucose uptake rate with rising glucose concentration. A pangenome of four different U. maydis strains highlighted missing metabolic pathways in iUma22. The new model allows for studies of metabolic adaptations to different environmental niches as well as for biotechnological applications.
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Affiliation(s)
- Ulf W. Liebal
- iAMB-Institute of Applied Microbiology, ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany; (L.U.); (P.K.); (T.Z.)
- Correspondence: (U.W.L.); (L.M.B.)
| | - Lena Ullmann
- iAMB-Institute of Applied Microbiology, ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany; (L.U.); (P.K.); (T.Z.)
| | - Christian Lieven
- Unseen Biometrics ApS, DK-2800 Kgs. Lyngby, Denmark;
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Philipp Kohl
- iAMB-Institute of Applied Microbiology, ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany; (L.U.); (P.K.); (T.Z.)
| | - Daniel Wibberg
- Genome Research of Industrial Microorganisms, CeBiTec, Bielefeld University, 33501 Bielefeld, Germany;
| | - Thiemo Zambanini
- iAMB-Institute of Applied Microbiology, ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany; (L.U.); (P.K.); (T.Z.)
| | - Lars M. Blank
- iAMB-Institute of Applied Microbiology, ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany; (L.U.); (P.K.); (T.Z.)
- Correspondence: (U.W.L.); (L.M.B.)
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Liebal UW, Köbbing S, Netze L, Schweidtmann AM, Mitsos A, Blank LM. Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb. Front Bioinform 2021; 1:747428. [PMID: 36303772 PMCID: PMC9581000 DOI: 10.3389/fbinf.2021.747428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/23/2021] [Indexed: 11/16/2022] Open
Abstract
Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called Exp2Ipynb to analyze promoter libraries maximizing information retrieval and promoter design with desired activity. We applied Exp2Ipynb to seven prokaryotic expression libraries to identify optimal experimental design principles. The workflow is open source, available as Jupyter Notebooks and covers the steps to 1) generate a statistical overview to sequence and activity, 2) train machine-learning algorithms, such as random forest, gradient boosting trees and support vector machines, for prediction and extraction of feature importance, 3) evaluate the performance of the estimator, and 4) to design new sequences with a desired activity using numerical optimization. The workflow can perform regression or classification on multiple promoter libraries, across species or reporter proteins. The most accurate predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. The prediction confidence mostly depends on sample size and sequence diversity, and we present a relationship to estimate their respective effects. The workflow can be adapted to process sequence libraries from other expression-related problems and increase insight to the growing application of high-throughput experiments, providing support for efficient strain engineering.
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Affiliation(s)
- Ulf W. Liebal
- iAMB-Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
- *Correspondence: Ulf W. Liebal,
| | - Sebastian Köbbing
- iAMB-Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Linus Netze
- AVT-Process Systems Engineering, RWTH Aachen University, Aachen, Germany
| | - Artur M. Schweidtmann
- Department of Chemical Engineering, Delft University of Technology, Delft, Netherlands
| | - Alexander Mitsos
- AVT-Process Systems Engineering, RWTH Aachen University, Aachen, Germany
| | - Lars M. Blank
- iAMB-Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
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Liebal UW, Fabry BA, Ravikrishnan A, Schedel CV, Schmitz S, Blank LM, Ebert BE. Genome-scale model reconstruction of the methylotrophic yeast Ogataea polymorpha. BMC Biotechnol 2021; 21:23. [PMID: 33722219 PMCID: PMC7962355 DOI: 10.1186/s12896-021-00675-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/04/2020] [Indexed: 11/10/2022] Open
Abstract
Background Ogataea polymorpha is a thermotolerant, methylotrophic yeast with significant industrial applications. While previously mainly used for protein synthesis, it also holds promise for producing platform chemicals. O. polymorpha has the distinct advantage of using methanol as a substrate, which could be potentially derived from carbon capture and utilization streams. Full development of the organism into a production strain and estimation of the metabolic capabilities require additional strain design, guided by metabolic modeling with a genome-scale metabolic model. However, to date, no genome-scale metabolic model is available for O. polymorpha. Results To overcome this limitation, we used a published reconstruction of the closely related yeast Komagataella phaffii as a reference and corrected reactions based on KEGG and MGOB annotation. Additionally, we conducted phenotype microarray experiments to test the suitability of 190 substrates as carbon sources. Over three-quarter of the substrate use was correctly reproduced by the model and 27 new substrates were added, that were not present in the K. phaffii reference model. Conclusion The developed genome-scale metabolic model of O. polymorpha will support the engineering of synthetic metabolic capabilities and enable the optimization of production processes, thereby supporting a sustainable future methanol economy. Supplementary Information The online version contains supplementary material available at (10.1186/s12896-021-00675-w).
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Affiliation(s)
- Ulf W Liebal
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringer Weg 1, Aachen, 52074, Germany
| | - Brigida A Fabry
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringer Weg 1, Aachen, 52074, Germany
| | - Aarthi Ravikrishnan
- Genome Institute of Singapore, 60 Biopolis Street, Genome, 03-01, Singapore, 138672, Singapore
| | - Constantin Vl Schedel
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringer Weg 1, Aachen, 52074, Germany
| | - Simone Schmitz
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringer Weg 1, Aachen, 52074, Germany
| | - Lars M Blank
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringer Weg 1, Aachen, 52074, Germany.
| | - Birgitta E Ebert
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringer Weg 1, Aachen, 52074, Germany.,Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia.,CSIRO Future Science Platform in Synthetic Biology, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain, ACT 2601, Australia
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Liebal UW, Phan ANT, Sudhakar M, Raman K, Blank LM. Machine Learning Applications for Mass Spectrometry-Based Metabolomics. Metabolites 2020; 10:E243. [PMID: 32545768 PMCID: PMC7345470 DOI: 10.3390/metabo10060243] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/09/2020] [Accepted: 06/11/2020] [Indexed: 12/20/2022] Open
Abstract
The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.
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Affiliation(s)
- Ulf W. Liebal
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
| | - An N. T. Phan
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
| | - Malvika Sudhakar
- Department of Biotechnology, Bhupat and Juoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India; (M.S.); (K.R.)
- Initiative for Biological Systems Engineering, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Juoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India; (M.S.); (K.R.)
- Initiative for Biological Systems Engineering, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Lars M. Blank
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
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Liebal UW, Blank LM, Ebert BE. CO 2 to succinic acid - Estimating the potential of biocatalytic routes. Metab Eng Commun 2018; 7:e00075. [PMID: 30197864 PMCID: PMC6127376 DOI: 10.1016/j.mec.2018.e00075] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 06/07/2018] [Accepted: 06/25/2018] [Indexed: 11/26/2022] Open
Abstract
Microbial carbon dioxide assimilation and conversion to chemical platform molecules has the potential to be developed as economic, sustainable processes. The carbon dioxide assimilation can proceed by a variety of natural pathways and recently even synthetic CO2 fixation routes have been designed. Early assessment of the performance of the different carbon fixation alternatives within biotechnological processes is desirable to evaluate their potential. Here we applied stoichiometric metabolic modeling based on physiological and process data to evaluate different process variants for the conversion of C1 carbon compounds to the industrial relevant platform chemical succinic acid. We computationally analyzed the performance of cyanobacteria, acetogens, methylotrophs, and synthetic CO2 fixation pathways in Saccharomyces cerevisiae in terms of production rates, product yields, and the optimization potential. This analysis provided insight into the economic feasibility and allowed to estimate the future industrial applicability by estimating overall production costs. With reported, or estimated data of engineered or wild type strains, none of the simulated microbial succinate production processes showed a performance allowing competitive production. The main limiting factors were identified as gas and photon transfer and metabolic activities whereas metabolic network structure was not restricting. In simulations with optimized parameters most process alternatives reached economically interesting values, hence, represent promising alternatives to sugar-based fermentations.
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Affiliation(s)
| | - Lars M. Blank
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Worringer Weg 1, D-52074 Aachen, Germany
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Chauhan A, Liebal UW, Vera J, Baltrusch S, Junghanß C, Tiedge M, Fuellen G, Wolkenhauer O, Köhling R. Systems Biology Approaches in Aging Research. Interdiscip Top Gerontol Geriatr 2015; 40:155-76. [DOI: 10.1159/000364981] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Liebal UW, Millat T, Marles-Wright J, Lewis RJ, Wolkenhauer O. Simulations of stressosome activation emphasize allosteric interactions between RsbR and RsbT. BMC Syst Biol 2013; 7:3. [PMID: 23320651 PMCID: PMC3556497 DOI: 10.1186/1752-0509-7-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Accepted: 01/07/2013] [Indexed: 11/10/2022]
Abstract
BACKGROUND The stressosome is a bacterial signalling complex that responds to environmental changes by initiating a protein partner switching cascade, which leads to the release of the alternative sigma factor, σB. Stress perception increases the phosphorylation of the stressosome sensor protein, RsbR, and the scaffold protein, RsbS, by the protein kinase, RsbT. Subsequent dissociation of RsbT from the stressosome activates the σB cascade. However, the sequence of physical events that occur in the stressosome during signal transduction is insufficiently understood. RESULTS Here, we use computational modelling to correlate the structure of the stressosome with the efficiency of the phosphorylation reactions that occur upon activation by stress. In our model, the phosphorylation of any stressosome protein is dependent upon its nearest neighbours and their phosphorylation status. We compare different hypotheses about stressosome activation and find that only the model representing the allosteric activation of the kinase RsbT, by phosphorylated RsbR, qualitatively reproduces the experimental data. CONCLUSIONS Our simulations and the associated analysis of published data support the following hypotheses: (i) a simple Boolean model is capable of reproducing stressosome dynamics, (ii) different stressors induce identical stressosome activation patterns, and we also confirm that (i) phosphorylated RsbR activates RsbT, and (ii) the main purpose of RsbX is to dephosphorylate RsbS-P.
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Affiliation(s)
- Ulf W Liebal
- Department of Systems Biology & Bioinformatics, Institute of Computer Science, University of Rostock, 18051, Rostock, Germany
| | - Thomas Millat
- Department of Systems Biology & Bioinformatics, Institute of Computer Science, University of Rostock, 18051, Rostock, Germany
| | - Jon Marles-Wright
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
- Institute of Structural and Molecular Biology, School of Biological Sciences, Edinburgh University, Edinburgh, EH9 3JR, UK
| | - Richard J Lewis
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, Institute of Computer Science, University of Rostock, 18051, Rostock, Germany
- Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, 7600, South Africa
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Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, Völker U, Wolkenhauer O. Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis. Mol BioSyst 2012; 8:1806-14. [DOI: 10.1039/c2mb25031d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
Appropriate stimulus perception, signal processing and transduction ensure optimal adaptation of bacteria to environmental challenges. In the Gram-positive model bacterium Bacillus subtilis signalling networks and molecular interactions therein are well-studied, making this species a suitable candidate for the application of mathematical modelling. Here, we review systems biology approaches, focusing on chemotaxis, sporulation, σ(B) -dependent general stress response and competence. Processes like chemotaxis and Z-ring assembly depend critically on the subcellular localization of proteins. Environmental response strategies, including sporulation and competence, are characterized by phenotypic heterogeneity in isogenic cultures. The examples of mathematical modelling also include investigations that have demonstrated how operon structure and signalling dynamics are intricately interwoven to establish optimal responses. Our review illustrates that these interdisciplinary approaches offer new insights into the response of B. subtilis to environmental challenges. These case studies reveal modelling as a tool to increase the understanding of complex systems, to help formulating hypotheses and to guide the design of more directed experiments that test predictions.
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Affiliation(s)
- Ulf W Liebal
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, 18051 Rostock, Germany.
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