1
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van Rosmalen R, Moreno-Paz S, Duman-Özdamar Z, Suarez-Diez M. CFSA: Comparative flux sampling analysis as a guide for strain design. Metab Eng Commun 2024; 19:e00244. [PMID: 39072282 PMCID: PMC11283130 DOI: 10.1016/j.mec.2024.e00244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/30/2024] Open
Abstract
Genome-scale metabolic models of microbial metabolism have extensively been used to guide the design of microbial cell factories, still, many of the available strain design algorithms often fail to produce a reduced list of targets for improved performance that can be implemented and validated in a step-wise manner. We present Comparative Flux Sampling Analysis (CFSA), a strain design method based on the extensive comparison of complete metabolic spaces corresponding to maximal or near-maximal growth and production phenotypes. The comparison is complemented by statistical analysis to identify reactions with altered flux that are suggested as targets for genetic interventions including up-regulations, down-regulations and gene deletions. We applied CFSA to the production of lipids by Cutaneotrichosporon oleaginosus and naringenin by Saccharomyces cerevisiae identifying engineering targets in agreement with previous studies as well as new interventions. CFSA is an easy-to-use, robust method that suggests potential metabolic engineering targets for growth-uncoupled production that can be applied to the design of microbial cell factories.
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Affiliation(s)
| | | | - Z.E. Duman-Özdamar
- Laboratory of Systems and Synthetic Biology, Stippeneng 4 6708 WE Wageningen, the Netherlands
| | - M. Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Stippeneng 4 6708 WE Wageningen, the Netherlands
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2
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Schulz-Mirbach H, Dronsella B, He H, Erb TJ. Creating new-to-nature carbon fixation: A guide. Metab Eng 2024; 82:12-28. [PMID: 38160747 DOI: 10.1016/j.ymben.2023.12.012] [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: 10/10/2023] [Revised: 12/23/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024]
Abstract
Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.
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Affiliation(s)
- Helena Schulz-Mirbach
- Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043, Marburg, Germany
| | - Beau Dronsella
- Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043, Marburg, Germany; Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Hai He
- Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043, Marburg, Germany
| | - Tobias J Erb
- Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Str. 10, 35043, Marburg, Germany; Center for Synthetic Microbiology (SYNMIKRO), Karl-von-Frisch-Str. 16, D-35043, Marburg, Germany.
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3
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Morrissey J, Strain B, Kontoravdi C. Flux Balance Analysis of Mammalian Cell Systems. Methods Mol Biol 2024; 2774:119-134. [PMID: 38441762 DOI: 10.1007/978-1-0716-3718-0_9] [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] [Indexed: 03/07/2024]
Abstract
Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.
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Affiliation(s)
- James Morrissey
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London, UK.
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4
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Motamedian E, Berzins K, Muiznieks R, Stalidzans E. OptEnvelope: A target point guided method for growth-coupled production using knockouts. PLoS One 2023; 18:e0294313. [PMID: 37972019 PMCID: PMC10653430 DOI: 10.1371/journal.pone.0294313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023] Open
Abstract
Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a mathematic model of metabolism is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is presented based on finding minimal set of active reactions for a target point in the feasible solution space (envelope) using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space envelope in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results illustrate that OptEnvelope is capable to find multiple strong coupled envelopes located in the desired solution space because of its novel target point oriented strategy of envelope search. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.
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Affiliation(s)
- Ehsan Motamedian
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
| | - Kristaps Berzins
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
| | - Reinis Muiznieks
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
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5
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Cai J, Liao X, Mao Y, Wang R, Li H, Ma H. Designing gene manipulation schedules for high throughput parallel construction of objective strains. Biotechnol J 2023; 18:e2200578. [PMID: 37300341 DOI: 10.1002/biot.202200578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 05/24/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023]
Abstract
Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.
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Affiliation(s)
- Jingyi Cai
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Xiaoping Liao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
- Haihe Laboratory of Synthetic Biology, Tianjin, China
| | - Yufeng Mao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Ruoyu Wang
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Haoran Li
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
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6
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Assessing and reducing phenotypic instability in cyanobacteria. Curr Opin Biotechnol 2023; 80:102899. [PMID: 36724584 DOI: 10.1016/j.copbio.2023.102899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/23/2022] [Accepted: 01/06/2023] [Indexed: 01/31/2023]
Abstract
Cyanobacteria have promising potential as sustainable cell factories. However, one challenge that is still largely unreported in scaling-up cyanobacteria bioproduction is phenotypic instability, where the emergence and selection of nonproducing cells leading to loss in production has longer evolutionary timescales to take place in industrial-scale bioreactors. Quantifying phenotypic instability early on in strain development allows researchers to make informed decisions on whether to proceed with scalable designs, or if present, devise countermeasures to reduce instability. One particularly effective strategy to mitigate instability is the use of genome-scale metabolic models to design growth-coupled production strains. In silico studies have predicted that creating certain cofactor imbalances or removing recycling reactions in cyanobacteria can be exploited to stably produce a wide variety of metabolites.
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7
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Hu M, Dinh HV, Shen Y, Suthers PF, Foster CJ, Call CM, Ye X, Pratas J, Fatma Z, Zhao H, Rabinowitz JD, Maranas CD. Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale. Metab Eng 2023; 76:1-17. [PMID: 36603705 DOI: 10.1016/j.ymben.2023.01.001] [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: 11/01/2022] [Revised: 12/22/2022] [Accepted: 01/01/2023] [Indexed: 01/04/2023]
Abstract
The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.
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Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Catherine M Call
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Xuanjia Ye
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Jimmy Pratas
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Zia Fatma
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
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8
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Schneider P, Bekiaris PS, von Kamp A, Klamt S. StrainDesign: a comprehensive Python package for computational design of metabolic networks. Bioinformatics 2022; 38:4981-4983. [PMID: 36111857 PMCID: PMC9620819 DOI: 10.1093/bioinformatics/btac632] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/17/2022] [Accepted: 09/15/2022] [Indexed: 10/05/2023] Open
Abstract
SUMMARY Various constraint-based optimization approaches have been developed for the computational analysis and design of metabolic networks. Herein, we present StrainDesign, a comprehensive Python package that builds upon the COBRApy toolbox and integrates the most popular metabolic design algorithms, including nested strain optimization methods such as OptKnock, RobustKnock and OptCouple as well as the more general minimal cut sets approach. The optimization approaches are embedded in individual modules, which can also be combined for setting up more elaborate strain design problems. Advanced features, such as the efficient integration of GPR rules and the possibility to consider gene and reaction additions or regulatory interventions, have been generalized and are available for all modules. The package uses state-of-the-art preprocessing methods, supports multiple solvers and provides a number of enhanced tools for analyzing computed intervention strategies including 2D and 3D plots of user-selected metabolic fluxes or yields. Furthermore, a user-friendly interface for the StrainDesign package has been implemented in the GUI-based metabolic modeling software CNApy. StrainDesign provides thus a unique and rich framework for computational strain design in Python, uniting many algorithmic developments in the field and allowing modular extension in the future. AVAILABILITY AND IMPLEMENTATION The StrainDesign package can be retrieved from PyPi, Anaconda and GitHub (https://github.com/klamt-lab/straindesign) and is also part of the latest CNApy package.
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Affiliation(s)
- Philipp Schneider
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Pavlos Stephanos Bekiaris
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Axel von Kamp
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Steffen Klamt
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
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9
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Jouhten P, Konstantinidis D, Pereira F, Andrejev S, Grkovska K, Castillo S, Ghiachi P, Beltran G, Almaas E, Mas A, Warringer J, Gonzalez R, Morales P, Patil KR. Predictive evolution of metabolic phenotypes using model-designed environments. Mol Syst Biol 2022; 18:e10980. [PMID: 36201279 PMCID: PMC9536503 DOI: 10.15252/msb.202210980] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 11/04/2022] Open
Abstract
Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.
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Affiliation(s)
- Paula Jouhten
- European Molecular Biology LaboratoryHeidelbergGermany
- VTT Technical Research Centre of Finland LtdEspooFinland
- Department of Bioproducts and BiosystemsAalto UniversityEspooFinland
| | | | | | | | | | | | - Payam Ghiachi
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Gemma Beltran
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Eivind Almaas
- Department of Biotechnology and Food ScienceNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Albert Mas
- Departament Bioquímica i Biotecnologia, Facultat d'EnologiaUniversitat Rovira i VirgiliTarragonaSpain
| | - Jonas Warringer
- Department of Chemistry and Molecular BiologyUniversity of GothenburgGothenburgSweden
| | - Ramon Gonzalez
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Pilar Morales
- Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraLogroñoSpain
| | - Kiran R Patil
- European Molecular Biology LaboratoryHeidelbergGermany
- Medical Research Council (MRC) Toxicology UnitUniversity of CambridgeCambridgeUK
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10
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Ye C, Wei X, Shi T, Sun X, Xu N, Gao C, Zou W. Genome-scale metabolic network models: from first-generation to next-generation. Appl Microbiol Biotechnol 2022; 106:4907-4920. [PMID: 35829788 DOI: 10.1007/s00253-022-12066-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 06/24/2022] [Accepted: 07/02/2022] [Indexed: 11/26/2022]
Abstract
Over the last two decades, thousands of genome-scale metabolic network models (GSMMs) have been constructed. These GSMMs have been widely applied in various fields, ranging from network interaction analysis, to cell phenotype prediction. However, due to the lack of constraints, the prediction accuracy of first-generation GSMMs was limited. To overcome these limitations, the next-generation GSMMs were developed by integrating omics data, adding constrain condition, integrating different biological models, and constructing whole-cell models. Here, we review recent advances of GSMMs from the first generation to the next generation. Then, we discuss the major application of GSMMs in industrial biotechnology, such as predicting phenotypes and guiding metabolic engineering. In addition, human health applications, including understanding biological mechanisms, discovering biomarkers and drug targets, are also summarized. Finally, we address the challenges and propose new trend of GSMMs. KEY POINTS: •This mini-review updates the literature on almost all published GSMMs since 1999. •Detailed insights into the development of the first- and next-generation GSMMs. •The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized.
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Affiliation(s)
- Chao Ye
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China.
| | - Xinyu Wei
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Tianqiong Shi
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Xiaoman Sun
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Nan Xu
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, China
| | - Cong Gao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, China
| | - Wei Zou
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin, 644005, China.
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11
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Kovács SC, Szappanos B, Tengölics R, Notebaart RA, Papp B. Underground metabolism as a rich reservoir for pathway engineering. Bioinformatics 2022; 38:3070-3077. [PMID: 35441658 PMCID: PMC9154287 DOI: 10.1093/bioinformatics/btac282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/25/2022] Open
Abstract
Motivation Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level ‘underground’ enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering. Results Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development. Availability and implementation The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Szabolcs Cselgő Kovács
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
| | - Balázs Szappanos
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary.,Department of Biotechnology, University of Szeged, Szeged, Hungary
| | - Roland Tengölics
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
| | - Richard A Notebaart
- Food Microbiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Balázs Papp
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
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12
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Jiang S, Otero-Muras I, Banga JR, Wang Y, Kaiser M, Krasnogor N. OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production. ACS Synth Biol 2022; 11:1531-1541. [PMID: 35389631 PMCID: PMC9016760 DOI: 10.1021/acssynbio.1c00610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Computational
tools have been widely adopted for strain optimization
in metabolic engineering, contributing to numerous success stories
of producing industrially relevant biochemicals. However, most of
these tools focus on single metabolic intervention strategies (either
gene/reaction knockout or amplification alone) and rely on hypothetical
optimality principles (e.g., maximization of growth) and precise gene
expression (e.g., fold changes) for phenotype prediction. This paper
introduces OptDesign, a new two-step strain design strategy. In the
first step, OptDesign selects regulation candidates that have a noticeable
flux difference between the wild type and production strains. In the
second step, it computes optimal design strategies with limited manipulations
(combining regulation and knockout), leading to high biochemical production.
The usefulness and capabilities of OptDesign are demonstrated for
the production of three biochemicals in Escherichia
coli using the latest genome-scale metabolic model
iML1515, showing highly consistent results with previous studies while
suggesting new manipulations to boost strain performance. The source
code is available at https://github.com/chang88ye/OptDesign.
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Affiliation(s)
- Shouyong Jiang
- Department of Computing Science, University of Aberdeen, Aberdeen AB24 3FX, U.K
| | - Irene Otero-Muras
- Institute for Integrative Systems Biology, UV-CSIC, Valencia 46980, Spain
| | - Julio R. Banga
- Computational Biology Lab, MBG-CSIC, Pontevedra 36143, Spain
| | - Yong Wang
- School of Automation, Central South University, Changsha 410083, China
| | - Marcus Kaiser
- School of Medicine, University of Nottingham, Nottingham NG7 2RD, U.K
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13
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Nowrouzi B, Rios-Solis L. Redox metabolism for improving whole-cell P450-catalysed terpenoid biosynthesis. Crit Rev Biotechnol 2021; 42:1213-1237. [PMID: 34749553 DOI: 10.1080/07388551.2021.1990210] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The growing preference for producing cytochrome P450-mediated natural products in microbial systems stems from the challenging nature of the organic chemistry approaches. The P450 enzymes are redox-dependent proteins, through which they source electrons from reducing cofactors to drive their activities. Widely researched in biochemistry, most of the previous studies have extensively utilised expensive cell-free assays to reveal mechanistic insights into P450 functionalities in presence of commercial redox partners. However, in the context of microbial bioproduction, the synergic activity of P450- reductase proteins in microbial systems have not been largely investigated. This is mainly due to limited knowledge about their mutual interactions in the context of complex systems. Hence, manipulating the redox potential for natural product synthesis in microbial chassis has been limited. As the potential of redox state as crucial regulator of P450 biocatalysis has been greatly underestimated by the scientific community, in this review, we re-emphasize their pivotal role in modulating the in vivo P450 activity through affecting the product profile and yield. Particularly, we discuss the applications of widely used in vivo redox engineering methodologies for natural product synthesis to provide further suggestions for patterning on P450-based terpenoids production in microbial platforms.
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Affiliation(s)
- Behnaz Nowrouzi
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, UK.,Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, UK
| | - Leonardo Rios-Solis
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, UK.,Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, UK
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14
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Occhipinti A, Hamadi Y, Kugler H, Wintersteiger CM, Yordanov B, Angione C. Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2339-2352. [PMID: 32248120 DOI: 10.1109/tcbb.2020.2973386] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.
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15
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Stella RG, Gertzen CGW, Smits SHJ, Gätgens C, Polen T, Noack S, Frunzke J. Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum. Metab Eng 2021; 68:162-173. [PMID: 34628038 DOI: 10.1016/j.ymben.2021.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/27/2021] [Accepted: 10/03/2021] [Indexed: 12/13/2022]
Abstract
Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.
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Affiliation(s)
- Roberto G Stella
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Christoph G W Gertzen
- Center for Structural Studies (CSS), Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany; Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - Sander H J Smits
- Center for Structural Studies (CSS), Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany; Institute of Biochemistry, Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - Cornelia Gätgens
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Tino Polen
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany; Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Julia Frunzke
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany.
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16
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Schneider P, Mahadevan R, Klamt S. Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs. Biotechnol J 2021; 16:e2100236. [PMID: 34432943 DOI: 10.1002/biot.202100236] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/08/2022]
Abstract
A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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17
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Wu Y, Jameel A, Xing XH, Zhang C. Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution. Trends Biotechnol 2021; 40:38-59. [PMID: 33958227 DOI: 10.1016/j.tibtech.2021.04.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/17/2021] [Accepted: 04/01/2021] [Indexed: 12/18/2022]
Abstract
Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.
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Affiliation(s)
- Yinan Wu
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Aysha Jameel
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Xin-Hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
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18
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Vila-Santa A, Islam MA, Ferreira FC, Prather KLJ, Mira NP. Prospecting Biochemical Pathways to Implement Microbe-Based Production of the New-to-Nature Platform Chemical Levulinic Acid. ACS Synth Biol 2021; 10:724-736. [PMID: 33764057 DOI: 10.1021/acssynbio.0c00518] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Levulinic acid is a versatile platform molecule with potential to be used as an intermediate in the synthesis of many value-added products used across different industries, from cosmetics to fuels. Thus far, microbial biosynthetic pathways having levulinic acid as a product or an intermediate are not known, which restrains the development and optimization of a microbe-based process envisaging the sustainable bioproduction of this chemical. One of the doors opened by synthetic biology in the design of microbial systems is the implementation of new-to-nature pathways, that is, the assembly of combinations of enzymes not observed in vivo, where the enzymes can use not only their native substrates but also non-native ones, creating synthetic steps that enable the production of novel compounds. Resorting to a combined approach involving complementary computational tools and extensive manual curation, in this work, we provide a thorough prospect of candidate biosynthetic pathways that can be assembled for the production of levulinic acid in Escherichia coli or Saccharomyces cerevisiae. Out of the hundreds of combinations screened, five pathways were selected as best candidates on the basis of the availability of substrates and of candidate enzymes to catalyze the synthetic steps (that is, those steps that involve conversions not previously described). Genome-scale metabolic modeling was used to assess the performance of these pathways in the two selected hosts and to anticipate possible bottlenecks. Not only does the herein described approach offer a platform for the future implementation of the microbial production of levulinic acid but also it provides an organized research strategy that can be used as a framework for the implementation of other new-to-nature biosynthetic pathways for the production of value-added chemicals, thus fostering the emerging field of synthetic industrial microbiotechnology.
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Affiliation(s)
- Ana Vila-Santa
- Department of Bioengineering and Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
| | - M. Ahsanul Islam
- Department of Chemical Engineering, Loughborough University, Leicestershire, LE11 3TU Loughborough, United Kingdom
| | - Frederico C. Ferreira
- Department of Bioengineering and Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
| | - Kristala L. J. Prather
- Department of Chemical Engineering and Center for Integrative Synthetic Biology (CISB), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Nuno P. Mira
- Department of Bioengineering and Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
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19
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Mol V, Bennett M, Sánchez BJ, Lisowska BK, Herrgård MJ, Nielsen AT, Leak DJ, Sonnenschein N. Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism. Metab Eng 2021; 65:123-134. [PMID: 33753231 DOI: 10.1016/j.ymben.2021.03.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 03/01/2021] [Indexed: 12/27/2022]
Abstract
Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.
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Affiliation(s)
- Viviënne Mol
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Martyn Bennett
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Benjamín J Sánchez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Beata K Lisowska
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; BioInnovation Institute, Copenhagen N, Denmark
| | - Alex Toftgaard Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - David J Leak
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom.
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
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20
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Rugbjerg P, Olsson L. The future of self-selecting and stable fermentations. J Ind Microbiol Biotechnol 2020; 47:993-1004. [PMID: 33136197 PMCID: PMC7695646 DOI: 10.1007/s10295-020-02325-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/17/2020] [Indexed: 02/08/2023]
Abstract
Unfavorable cell heterogeneity is a frequent risk during bioprocess scale-up and characterized by rising frequencies of low-producing cells. Low-producing cells emerge by both non-genetic and genetic variation and will enrich due to their higher specific growth rate during the extended number of cell divisions of large-scale bioproduction. Here, we discuss recent strategies for synthetic stabilization of fermentation populations and argue for their application to make cell factory designs that better suit industrial needs. Genotype-directed strategies leverage DNA-sequencing data to inform strain design. Self-selecting phenotype-directed strategies couple high production with cell proliferation, either by redirected metabolic pathways or synthetic product biosensing to enrich for high-performing cell variants. Evaluating production stability early in new cell factory projects will guide heterogeneity-reducing design choices. As good initial metrics, we propose production half-life from standardized serial-passage stability screens and production load, quantified as production-associated percent-wise growth rate reduction. Incorporating more stable genetic designs will greatly increase scalability of future cell factories through sustaining a high-production phenotype and enabling stable long-term production.
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Affiliation(s)
- Peter Rugbjerg
- Enduro Genetics ApS, Copenhagen, Denmark. .,Department of Biology and Biological Engineering, Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden.
| | - Lisbeth Olsson
- Department of Biology and Biological Engineering, Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden
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21
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Jiang S, Wang Y, Kaiser M, Krasnogor N. NIHBA: a network interdiction approach for metabolic engineering design. Bioinformatics 2020; 36:3482-3492. [PMID: 32167529 PMCID: PMC7267835 DOI: 10.1093/bioinformatics/btaa163] [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: 10/06/2019] [Revised: 02/28/2020] [Accepted: 03/10/2020] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. RESULTS Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article). AVAILABILITY AND IMPLEMENTATION Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. CONTACT math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shouyong Jiang
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Yong Wang
- School of Automation, Central South University, Changsha 410083, China
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Natalio Krasnogor
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
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22
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Godara A, Kao KC. Adaptive laboratory evolution for growth coupled microbial production. World J Microbiol Biotechnol 2020; 36:175. [DOI: 10.1007/s11274-020-02946-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 10/08/2020] [Indexed: 12/18/2022]
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Antonakoudis A, Barbosa R, Kotidis P, Kontoravdi C. The era of big data: Genome-scale modelling meets machine learning. Comput Struct Biotechnol J 2020; 18:3287-3300. [PMID: 33240470 PMCID: PMC7663219 DOI: 10.1016/j.csbj.2020.10.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 12/15/2022] Open
Abstract
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
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Affiliation(s)
| | | | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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24
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Guirimand G, Kulagina N, Papon N, Hasunuma T, Courdavault V. Innovative Tools and Strategies for Optimizing Yeast Cell Factories. Trends Biotechnol 2020; 39:488-504. [PMID: 33008642 DOI: 10.1016/j.tibtech.2020.08.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/24/2020] [Accepted: 08/27/2020] [Indexed: 12/13/2022]
Abstract
Metabolic engineering (ME) aims to develop efficient microbial cell factories that can produce a wide variety of valuable compounds, ideally at the highest yield and from various feedstocks. We summarize recent developments in ME methods for tailoring different yeast cell factories (YCFs). In particular, we highlight the most timely and cutting-edge molecular tools and strategies for biosynthetic pathway optimization (including genome-editing tools), combinatorial transcriptional and post-transcriptional engineering (cis/trans regulators), dynamic control of metabolic fluxes (e.g., rewiring of primary metabolism), and spatial reconfiguration of metabolic pathways. Finally, we discuss challenges and perspectives for adaptive laboratory evolution (ALE) of yeast to advance ME of microbial cell factories.
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Affiliation(s)
- Gregory Guirimand
- Graduate School of Sciences, Technology and Innovation, Kobe University, Kobe, Japan; Biomolécules et Biotechnologies Végétales (BBV), Équipe d'Accueil (EA) 2106, Université de Tours, Tours, France
| | - Natalja Kulagina
- Biomolécules et Biotechnologies Végétales (BBV), Équipe d'Accueil (EA) 2106, Université de Tours, Tours, France
| | - Nicolas Papon
- Groupe d'Etude des Interactions Hôte-Pathogène (GEIHP), EA 3142, Université Angers and Université Brest, Structure Féderative de Recherche (SFR) 4208 Interactions Cellulaires et Applications Thérapeutiques (ICAT), Angers, France
| | - Tomohisa Hasunuma
- Graduate School of Sciences, Technology and Innovation, Kobe University, Kobe, Japan; Engineering Biology Research Center, Kobe University, Kobe, Japan.
| | - Vincent Courdavault
- Biomolécules et Biotechnologies Végétales (BBV), Équipe d'Accueil (EA) 2106, Université de Tours, Tours, France.
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25
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Schneider P, von Kamp A, Klamt S. An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets. PLoS Comput Biol 2020; 16:e1008110. [PMID: 32716928 PMCID: PMC7410339 DOI: 10.1371/journal.pcbi.1008110] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 08/06/2020] [Accepted: 06/30/2020] [Indexed: 02/07/2023] Open
Abstract
The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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26
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Abstract
Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.
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Gibson B, Dahabieh M, Krogerus K, Jouhten P, Magalhães F, Pereira R, Siewers V, Vidgren V. Adaptive Laboratory Evolution of Ale and Lager Yeasts for Improved Brewing Efficiency and Beer Quality. Annu Rev Food Sci Technol 2020; 11:23-44. [DOI: 10.1146/annurev-food-032519-051715] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Yeasts directly impact the efficiency of brewery fermentations as well as the character of the beers produced. In recent years, there has been renewed interest in yeast selection and development inspired by the demand to utilize resources more efficiently and the need to differentiate beers in a competitive market. Reviewed here are the different, non-genetically modified (GM) approaches that have been considered, including bioprospecting, hybridization, and adaptive laboratory evolution (ALE). Particular emphasis is placed on the latter, which represents an extension of the processes that have led to the domestication of strains already used in commercial breweries. ALE can be used to accentuate the positive traits of brewing yeast as well as temper some of the traits that are less desirable from a modern brewer's perspective. This method has the added advantage of being non-GM and therefore suitable for food and beverage production.
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Affiliation(s)
- B. Gibson
- VTT Technical Research Centre of Finland Ltd, FI-02044 Espoo, Finland
| | - M. Dahabieh
- Renaissance BioScience, Vancouver, British Columbia, Canada, V6T1Z3
| | - K. Krogerus
- VTT Technical Research Centre of Finland Ltd, FI-02044 Espoo, Finland
| | - P. Jouhten
- VTT Technical Research Centre of Finland Ltd, FI-02044 Espoo, Finland
| | - F. Magalhães
- VTT Technical Research Centre of Finland Ltd, FI-02044 Espoo, Finland
| | - R. Pereira
- Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
| | - V. Siewers
- Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden
| | - V. Vidgren
- VTT Technical Research Centre of Finland Ltd, FI-02044 Espoo, Finland
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28
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Aslan S, Noor E, Benito Vaquerizo S, Lindner SN, Bar-Even A. Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate. Metab Eng 2019; 57:96-109. [PMID: 31491545 DOI: 10.1016/j.ymben.2019.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 08/05/2019] [Accepted: 09/02/2019] [Indexed: 11/16/2022]
Abstract
Microbial biosensors are used to detect the presence of compounds provided externally or produced internally. The latter case is commonly constrained by the need to screen a large library of enzyme or pathway variants to identify those that can efficiently generate the desired compound. To address this limitation, we suggest the use of metabolic sensor strains which can grow only if the relevant compound is present and thus replace screening with direct selection. We used a computational platform to design metabolic sensor strains with varying dependencies on a specific compound. Our method systematically explores combinations of gene deletions and identifies how the growth requirement for a compound changes with the media composition. We demonstrate this approach by constructing a set of E. coli glycerate sensor strains. In each of these strains a different set of enzymes is disrupted such that central metabolism is effectively dissected into multiple segments, each requiring a dedicated carbon source. We find an almost perfect match between the predicted and experimental dependence on glycerate and show that the strains can be used to accurately detect glycerate concentrations across two orders of magnitude. Apart from demonstrating the potential application of metabolic sensor strains, our work reveals key phenomena in central metabolism, including spontaneous degradation of central metabolites and the importance of metabolic sinks for balancing small metabolic networks.
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Affiliation(s)
- Selçuk Aslan
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Elad Noor
- Institute of Molecular Systems Biology, ETH Zürich, Otto-Stern-Weg 3, 8093, Zürich, Switzerland
| | - Sara Benito Vaquerizo
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Arren Bar-Even
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany.
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29
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Daud KM, Mohamad MS, Zakaria Z, Hassan R, Shah ZA, Deris S, Ibrahim Z, Napis S, Sinnott RO. A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout. Comput Biol Med 2019; 113:103390. [PMID: 31450056 DOI: 10.1016/j.compbiomed.2019.103390] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 08/15/2019] [Accepted: 08/15/2019] [Indexed: 01/06/2023]
Abstract
Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.
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Affiliation(s)
- Kauthar Mohd Daud
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Mohd Saberi Mohamad
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia; Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600, Jeli, Kelantan, Malaysia.
| | - Zalmiyah Zakaria
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Rohayanti Hassan
- Software Engineering Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
| | - Zuraini Ali Shah
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Safaai Deris
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia; Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600, Jeli, Kelantan, Malaysia
| | - Zuwairie Ibrahim
- Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Suhaimi Napis
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Richard O Sinnott
- School of Computing and Information Systems, Melbourne School of Engineering, University of Melbourne, Victoria, 3010, Australia
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30
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Sandberg TE, Salazar MJ, Weng LL, Palsson BO, Feist AM. The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology. Metab Eng 2019; 56:1-16. [PMID: 31401242 DOI: 10.1016/j.ymben.2019.08.004] [Citation(s) in RCA: 254] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 08/01/2019] [Accepted: 08/05/2019] [Indexed: 12/21/2022]
Abstract
Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.
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Affiliation(s)
- Troy E Sandberg
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Michael J Salazar
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Liam L Weng
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark.
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