1
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Hu M, Suthers PF, Maranas CD. KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states. Metab Eng 2024; 82:123-133. [PMID: 38336004 DOI: 10.1016/j.ymben.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/24/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.
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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
| | - 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
| | - 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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2
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van Lent P, Schmitz J, Abeel T. Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering. ACS Synth Biol 2023; 12:2588-2599. [PMID: 37616156 PMCID: PMC10510747 DOI: 10.1021/acssynbio.3c00186] [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: 03/30/2023] [Indexed: 08/25/2023]
Abstract
Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.
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Affiliation(s)
- Paul van Lent
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
| | - Joep Schmitz
- Department
of Science and Research, Joep Schmitz -
dsm-firmenich, Science & Research, P.O. Box 1, 2600
MA Delft, The Netherlands
| | - Thomas Abeel
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
- Infectious
Disease and Microbiome Program, Broad Institute
of MIT and Harvard, Cambridge, Massachusetts 02142, United States
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3
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Sieow BFL, De Sotto R, Seet ZRD, Hwang IY, Chang MW. Synthetic Biology Meets Machine Learning. Methods Mol Biol 2023; 2553:21-39. [PMID: 36227537 DOI: 10.1007/978-1-0716-2617-7_2] [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: 06/16/2023]
Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
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Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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4
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de Falco B, Giannino F, Carteni F, Mazzoleni S, Kim DH. Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas. RSC Adv 2022; 12:25528-25548. [PMID: 36199351 PMCID: PMC9449821 DOI: 10.1039/d2ra03326g] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/26/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.
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Affiliation(s)
- Bruna de Falco
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
| | - Francesco Giannino
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Fabrizio Carteni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Stefano Mazzoleni
- Department of Agricultural Sciences, University of Naples Federico II Portici 80055 Italy
| | - Dong-Hyun Kim
- Center for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham NG7 2RD UK
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5
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Sabzevari M, Szedmak S, Penttilä M, Jouhten P, Rousu J. Strain design optimization using reinforcement learning. PLoS Comput Biol 2022; 18:e1010177. [PMID: 35658018 PMCID: PMC9200333 DOI: 10.1371/journal.pcbi.1010177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 06/15/2022] [Accepted: 05/06/2022] [Indexed: 11/18/2022] Open
Abstract
Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization.
In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. Our method is model-free and does not assume prior knowledge of the microbe’s metabolic network or its regulation. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains.
We demonstrate the method’s capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. We investigate the method’s performance relevant for practical applicability in strain engineering i.e. the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library.
Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels.
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Affiliation(s)
- Maryam Sabzevari
- Department of Computer Science, Aalto University, Espoo, Finland
- * E-mail: ,
| | - Sandor Szedmak
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Merja Penttilä
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Paula Jouhten
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, Finland
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6
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Quantitative metabolic fluxes regulated by trans-omic networks. Biochem J 2022; 479:787-804. [PMID: 35356967 PMCID: PMC9022981 DOI: 10.1042/bcj20210596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 12/21/2022]
Abstract
Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
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7
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Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes. FERMENTATION-BASEL 2021. [DOI: 10.3390/fermentation7040220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.
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8
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Alsiyabi A, Chowdhury NB, Long D, Saha R. Enhancing in silico strain design predictions through next generation metabolic modeling approaches. Biotechnol Adv 2021; 54:107806. [PMID: 34298108 DOI: 10.1016/j.biotechadv.2021.107806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/22/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023]
Abstract
The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.
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Affiliation(s)
- Adil Alsiyabi
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, United States of America
| | - Niaz Bahar Chowdhury
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, United States of America
| | - Dianna Long
- Complex Biosystems, University of Nebraska-Lincoln, United States of America
| | - Rajib Saha
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, United States of America; Complex Biosystems, University of Nebraska-Lincoln, United States of America.
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9
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10
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Tafur Rangel AE, Ríos W, Mejía D, Ojeda C, Carlson R, Gómez Ramírez JM, González Barrios AF. In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products. Front Genet 2021; 12:633073. [PMID: 33868371 PMCID: PMC8044919 DOI: 10.3389/fgene.2021.633073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/23/2021] [Indexed: 11/13/2022] Open
Abstract
Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout's (feature's) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.
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Affiliation(s)
- Albert Enrique Tafur Rangel
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, Colombia
- Grupo de Investigación CINBIOS, Department of Microbiology, Universidad Popular del Cesar, Valledupar, Colombia
| | - Wendy Ríos
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Daisy Mejía
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Carmen Ojeda
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Ross Carlson
- Center for Biofilm Engineering, Montana State University, Bozeman, MT, United States
| | - Jorge Mario Gómez Ramírez
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Andrés Fernando González Barrios
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, Colombia
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11
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Novel allosteric inhibition of phosphoribulokinase identified by ensemble kinetic modeling of Synechocystis sp. PCC 6803 metabolism. Metab Eng Commun 2020; 11:e00153. [PMID: 33312875 PMCID: PMC7721636 DOI: 10.1016/j.mec.2020.e00153] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 10/17/2020] [Accepted: 11/17/2020] [Indexed: 12/11/2022] Open
Abstract
The present study attempted a computer simulation of the metabolism of a model cyanobacteria, Synechocystis sp. PCC 6803 (PCC 6803) to predict allosteric inhibitions that are likely to occur in photoautotrophic and mixotrophic conditions as well as in a metabolically engineered strain. PCC 6803 is a promising host for direct biochemical production from CO2; however, further investigation of allosteric regulation is required for rational metabolic engineering to produce target compounds. Herein, ensemble modeling of microbial metabolism was applied to build accurate predictive models by synthesizing the results of multiple models with different parameter sets into a single score to identify plausible allosteric inhibitions. The data driven-computer simulation using metabolic flux, enzyme abundance, and metabolite concentration data successfully identified candidates for allosteric inhibition. The enzyme assay experiment using the recombinant protein confirmed isocitrate was a non-competitive inhibitor of phosphoribulokinase as a novel allosteric regulation of cyanobacteria metabolism.
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12
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Zhang J, Petersen SD, Radivojevic T, Ramirez A, Pérez-Manríquez A, Abeliuk E, Sánchez BJ, Costello Z, Chen Y, Fero MJ, Martin HG, Nielsen J, Keasling JD, Jensen MK. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nat Commun 2020; 11:4880. [PMID: 32978375 PMCID: PMC7519671 DOI: 10.1038/s41467-020-17910-1] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 07/27/2020] [Indexed: 12/23/2022] Open
Abstract
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
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Affiliation(s)
- Jie Zhang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs., Lyngby, Denmark
| | - Søren D Petersen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs., Lyngby, Denmark
| | - Tijana Radivojevic
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- DOE Agile BioFoundry, Emeryville, CA, USA
| | | | | | | | - Benjamín J Sánchez
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs., Lyngby, Denmark
| | - Zak Costello
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- DOE Agile BioFoundry, Emeryville, CA, USA
| | - Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Hector Garcia Martin
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- DOE Agile BioFoundry, Emeryville, CA, USA
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
| | - Jens Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs., Lyngby, Denmark
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- BioInnovation Institute, Ole Maaløes Vej 3, DK-2200, Copenhagen N, Denmark
| | - Jay D Keasling
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs., Lyngby, Denmark
- Joint BioEnergy Institute, Emeryville, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Chemical and Biomolecular Engineering & Department of Bioengineering, University of California, Berkeley, CA, USA
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes of Advanced Technologies, Shenzhen, China
| | - Michael K Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs., Lyngby, Denmark.
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13
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Volk MJ, Lourentzou I, Mishra S, Vo LT, Zhai C, Zhao H. Biosystems Design by Machine Learning. ACS Synth Biol 2020; 9:1514-1533. [PMID: 32485108 DOI: 10.1021/acssynbio.0c00129] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
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14
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Tian C, Yang J, Li Y, Zhang T, Li J, Ren C, Men Y, Chen P, You C, Sun Y, Ma Y. Artificially designed routes for the conversion of starch to value-added mannosyl compounds through coupling in vitro and in vivo metabolic engineering strategies. Metab Eng 2020; 61:215-224. [PMID: 32623008 DOI: 10.1016/j.ymben.2020.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 06/09/2020] [Accepted: 06/21/2020] [Indexed: 02/08/2023]
Abstract
Starch/cellulose has become the major feedstock for manufacturing biofuels and biochemicals because of their abundance and sustainability. In this study, we presented an artificially designed "starch-mannose-fermentation" biotransformation process through coupling the advantages of in vivo and in vitro metabolic engineering strategies together. Starch was initially converted into mannose via an in vitro metabolic engineering biosystem, and then mannose was fermented by engineered microorganisms for biomanufacturing valuable mannosyl compounds. The in vitro metabolic engineering biosystem based on phosphorylation/dephosphorylation reactions was thermodynamically favorable and the conversion rate reached 81%. The mannose production using whole-cell biocatalysts reached 75.4 g/L in a 30-L reactor, indicating the potential industrial application. Furthermore, the produced mannose in the reactor was directly served as feedstock for the fermentation process to bottom-up produced 19.2 g/L mannosyl-oligosaccharides (MOS) and 7.2 g/L mannosylglycerate (MG) using recombinant Corynebacterium glutamicum strains. Notably, such a mannose fermentation process facilitated the synthesis of MOS, which has not been achieved under glucose fermentation and improved MG production by 2.6-fold than that using the same C-mole of glucose. This approach also allowed access to produce other kinds of mannosyl derivatives from starch.
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Affiliation(s)
- Chaoyu Tian
- University of Chinese Academy of Sciences, Beijing, 100049, China; National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Jiangang Yang
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Yunjie Li
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Tong Zhang
- University of Chinese Academy of Sciences, Beijing, 100049, China; National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Jiao Li
- University of Chinese Academy of Sciences, Beijing, 100049, China; National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Chenxi Ren
- University of Chinese Academy of Sciences, Beijing, 100049, China; National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Yan Men
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Peng Chen
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Chun You
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Yuanxia Sun
- University of Chinese Academy of Sciences, Beijing, 100049, China; National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Yanhe Ma
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
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15
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Gysi DM, Nowick K. Construction, comparison and evolution of networks in life sciences and other disciplines. J R Soc Interface 2020; 17:20190610. [PMID: 32370689 PMCID: PMC7276545 DOI: 10.1098/rsif.2019.0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
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Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, 04109 Leipzig, Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, 04109 Leipzig, Germany
- Center for Complex Networks Research, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Faculty of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Straβe 1-3, 14195 Berlin, Germany
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16
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Schroeder WL, Harris SD, Saha R. Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications. iScience 2020; 23:100980. [PMID: 32240950 PMCID: PMC7115120 DOI: 10.1016/j.isci.2020.100980] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/28/2020] [Accepted: 03/09/2020] [Indexed: 02/06/2023] Open
Abstract
The polyextremotolerant black yeast Exophiala dermatitidis is a tractable model system for investigation of adaptations that support growth under extreme conditions. Foremost among these adaptations are melanogenesis and carotenogenesis. A particularly important question is their metabolic production cost. However, investigation of this issue has been hindered by a relatively poor systems-level understanding of E. dermatitidis metabolism. To address this challenge, a genome-scale model (iEde2091) was developed. Using iEde2091, carotenoids were found to be more expensive to produce than melanins. Given their overlapping protective functions, this suggests that carotenoids have an underexplored yet important role in photo-protection. Furthermore, multiple defensive pigments with overlapping functions might allow E. dermatitidis to minimize cost. Because iEde2091 revealed that E. dermatitidis synthesizes the same melanins as humans and the active sites of the key tyrosinase enzyme are highly conserved this model may enable a broader understanding of melanin production across kingdoms.
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Affiliation(s)
- Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, NE 68588, USA
| | - Steven D Harris
- Department of Biological Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, NE 68588, USA.
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17
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Mesquita TJB, Giordano RDC, Zangirolami TC, Horta ACL. In silico Metabolic Flux Data Flexibilization for Advanced Bioreactor Control Applications. Ind Biotechnol (New Rochelle N Y) 2020. [DOI: 10.1089/ind.2020.29197.tjm] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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18
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Foster CJ, Gopalakrishnan S, Antoniewicz MR, Maranas CD. From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline. PLoS Comput Biol 2019; 15:e1007319. [PMID: 31504032 PMCID: PMC6759195 DOI: 10.1371/journal.pcbi.1007319] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 09/24/2019] [Accepted: 08/02/2019] [Indexed: 12/02/2022] Open
Abstract
Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.
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Affiliation(s)
- Charles J. Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Saratram Gopalakrishnan
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Maciek R. Antoniewicz
- Department of Chemical and Biomolecular Engineering, University of Delaware. Newark, Delaware, United States of America
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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19
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Kim OD, Rocha M, Maia P. A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering. Front Microbiol 2018; 9:1690. [PMID: 30108559 PMCID: PMC6079213 DOI: 10.3389/fmicb.2018.01690] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 07/06/2018] [Indexed: 12/03/2022] Open
Abstract
Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.
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Affiliation(s)
- Osvaldo D Kim
- SilicoLife Lda, Braga, Portugal.,Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, Universidade do Minho, Braga, Portugal
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20
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Costello Z, Martin HG. A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. NPJ Syst Biol Appl 2018; 4:19. [PMID: 29872542 PMCID: PMC5974308 DOI: 10.1038/s41540-018-0054-3] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 04/11/2018] [Accepted: 04/20/2018] [Indexed: 02/01/2023] Open
Abstract
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.
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Affiliation(s)
- Zak Costello
- 1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA.,DOE Agile Biofoundry, Emeryville, CA USA.,3DOE Joint BioEnergy Institute, Emeryville, CA USA
| | - Hector Garcia Martin
- 1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA.,DOE Agile Biofoundry, Emeryville, CA USA.,3DOE Joint BioEnergy Institute, Emeryville, CA USA.,4BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
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21
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Genome-Scale In Silico Analysis for Enhanced Production of Succinic Acid in Zymomonas mobilis. Processes (Basel) 2018. [DOI: 10.3390/pr6040030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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22
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Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design. Methods Mol Biol 2018; 1671:63-82. [PMID: 29170953 DOI: 10.1007/978-1-4939-7295-1_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Engineering biological systems that are capable of overproducing products of interest is the ultimate goal of any biotechnology application. To this end, stoichiometric (or steady state) and kinetic models are increasingly becoming available for a variety of organisms including prokaryotes, eukaryotes, and microbial communities. This ever-accelerating pace of such model reconstructions has also spurred the development of optimization-based strain design techniques. This chapter highlights a number of such frameworks developed in recent years in order to generate testable hypotheses (in terms of genetic interventions), thus addressing the challenges in metabolic engineering. In particular, three major methods are covered in detail including two methods for designing strains (i.e., one stoichiometric model-based and the other by integrating kinetic information into a stoichiometric model) and one method for analyzing microbial communities.
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23
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Matsuoka Y, Kurata H. Modeling and simulation of the redox regulation of the metabolism in Escherichia coli at different oxygen concentrations. BIOTECHNOLOGY FOR BIOFUELS 2017; 10:183. [PMID: 28725263 PMCID: PMC5512849 DOI: 10.1186/s13068-017-0867-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Microbial production of biofuels and biochemicals from renewable feedstocks has received considerable recent attention from environmental protection and energy production perspectives. Many biofuels and biochemicals are produced by fermentation under oxygen-limited conditions following initiation of aerobic cultivation to enhance the cell growth rate. Thus, it is of significant interest to investigate the effect of dissolved oxygen concentration on redox regulation in Escherichia coli, a particularly popular cellular factory due to its high growth rate and well-characterized physiology. For this, the systems biology approach such as modeling is powerful for the analysis of the metabolism and for the design of microbial cellular factories. RESULTS Here, we developed a kinetic model that describes the dynamics of fermentation by taking into account transcription factors such as ArcA/B and Fnr, respiratory chain reactions and fermentative pathways, and catabolite regulation. The hallmark of the kinetic model is its ability to predict the dynamics of metabolism at different dissolved oxygen levels and facilitate the rational design of cultivation methods. The kinetic model was verified based on the experimental data for a wild-type E. coli strain. The model reasonably predicted the metabolic characteristics and molecular mechanisms of fnr and arcA gene-knockout mutants. Moreover, an aerobic-microaerobic dual-phase cultivation method for lactate production in a pfl-knockout mutant exhibited promising yield and productivity. CONCLUSIONS It is quite important to understand metabolic regulation mechanisms from both scientific and engineering points of view. In particular, redox regulation in response to oxygen limitation is critically important in the practical production of biofuel and biochemical compounds. The developed model can thus be used as a platform for designing microbial factories to produce a variety of biofuels and biochemicals.
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Affiliation(s)
- Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502 Japan
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24
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Englaender JA, Jones JA, Cress BF, Kuhlman TE, Linhardt RJ, Koffas MAG. Effect of Genomic Integration Location on Heterologous Protein Expression and Metabolic Engineering in E. coli. ACS Synth Biol 2017; 6:710-720. [PMID: 28055177 DOI: 10.1021/acssynbio.6b00350] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Chromosomal integration offers a selection-free alternative to DNA plasmids for expression of foreign proteins and metabolic pathways. Episomal plasmid DNA is convenient but has drawbacks including increased metabolic burden and the requirement for selection in the form of antibiotics. E. coli has long been used for the expression of foreign proteins and for the production of valuable metabolites by expression of complete metabolic pathways. The gene encoding the fluorescent reporter protein mCherry was integrated into four genomic loci on the E. coli chromosome to measure protein expression at each site. Expression levels ranged from 25% to 500% compared to the gene expressed on a high-copy plasmid. Modular expression of DNA is one of the most commonly used methods for optimizing metabolite production by metabolic engineering. By combining a recently developed method for integration of large synthetic DNA constructs into the genome, we were able to integrate two foreign pathways into the same four genomic loci. We have demonstrated that only one of the genomic loci resulted in the production of violacein, and that all four loci produced trans-cinnamic acid from the TAL pathway.
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Affiliation(s)
- Jacob A Englaender
- Department of Biological Sciences, Rensselaer Polytechnic Institute , Troy, New York 12180, United States
| | - J Andrew Jones
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute , Troy, New York 12180, United States
- Department of Chemistry, Hamilton College , Clinton, New York 13323, United States
| | - Brady F Cress
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute , Troy, New York 12180, United States
| | - Thomas E Kuhlman
- Department of Physics, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
- Carl. R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States
| | - Robert J Linhardt
- Department of Biological Sciences, Rensselaer Polytechnic Institute , Troy, New York 12180, United States
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute , Troy, New York 12180, United States
| | - Mattheos A G Koffas
- Department of Biological Sciences, Rensselaer Polytechnic Institute , Troy, New York 12180, United States
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute , Troy, New York 12180, United States
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25
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MacGillivray M, Ko A, Gruber E, Sawyer M, Almaas E, Holder A. Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Sci Rep 2017; 7:268. [PMID: 28325918 PMCID: PMC5427939 DOI: 10.1038/s41598-017-00170-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 02/13/2017] [Indexed: 02/04/2023] Open
Abstract
Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming (i) equilibrium of a linear system of ordinary differential equations, and (ii) deterministic data. However, the steady-state assumption is biologically imperfect, and several key stoichiometric coefficients are experimentally inferred from situations of inherent variation. We propose an approach that explicitly acknowledges heterogeneity and conducts a robust analysis of metabolic pathways (RAMP). The basic assumption of steady state is relaxed, and we model the innate heterogeneity of cells probabilistically. Our mathematical study of the stochastic problem shows that FBA is a limiting case of our RAMP method. Moreover, RAMP has the properties that: A) metabolic states are (Lipschitz) continuous with regards to the probabilistic modeling parameters, B) convergent metabolic states are solutions to the deterministic FBA paradigm as the stochastic elements dissipate, and C) RAMP can identify biologically tolerable diversity of a metabolic network in an optimized culture. We benchmark RAMP against traditional FBA on genome-scale metabolic reconstructed models of E. coli, calculating essential genes and comparing with experimental flux data.
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Affiliation(s)
- Michael MacGillivray
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Amy Ko
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Emily Gruber
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Miranda Sawyer
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| | - Allen Holder
- Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA.
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26
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Application of theoretical methods to increase succinate production in engineered strains. Bioprocess Biosyst Eng 2016; 40:479-497. [PMID: 28040871 DOI: 10.1007/s00449-016-1729-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 12/16/2016] [Indexed: 12/19/2022]
Abstract
Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.
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27
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Khodayari A, Maranas CD. A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Nat Commun 2016; 7:13806. [PMID: 27996047 PMCID: PMC5187423 DOI: 10.1038/ncomms13806] [Citation(s) in RCA: 144] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 11/03/2016] [Indexed: 01/03/2023] Open
Abstract
Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).
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Affiliation(s)
- Ali Khodayari
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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28
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Mienda BS. Genome-scale metabolic models as platforms for strain design and biological discovery. J Biomol Struct Dyn 2016; 35:1863-1873. [DOI: 10.1080/07391102.2016.1197153] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Bashir Sajo Mienda
- Faculty of Biosciences and Medical Engineering, Department of Biosciences and Health Sciences, Bioinformatics Research Group (BIRG), Universiti Teknologi Malaysia, Johor Bahru, Skudai 81310, Malaysia
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29
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Machado D, Zhuang KH, Sonnenschein N, Herrgård MJ. Editorial: Current Challenges in Modeling Cellular Metabolism. Front Bioeng Biotechnol 2015; 3:193. [PMID: 26636080 PMCID: PMC4659907 DOI: 10.3389/fbioe.2015.00193] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 11/09/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Daniel Machado
- Centre of Biological Engineering, University of Minho , Braga , Portugal
| | - Kai H Zhuang
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark , Hørsholm , Denmark
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30
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Designing overall stoichiometric conversions and intervening metabolic reactions. Sci Rep 2015; 5:16009. [PMID: 26530953 PMCID: PMC4632160 DOI: 10.1038/srep16009] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 10/07/2015] [Indexed: 02/07/2023] Open
Abstract
Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C2+ metabolites with higher carbon efficiency.
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31
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López García de Lomana A, Schäuble S, Valenzuela J, Imam S, Carter W, Bilgin DD, Yohn CB, Turkarslan S, Reiss DJ, Orellana MV, Price ND, Baliga NS. Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii. BIOTECHNOLOGY FOR BIOFUELS 2015; 8:207. [PMID: 26633994 PMCID: PMC4667458 DOI: 10.1186/s13068-015-0391-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 11/17/2015] [Indexed: 05/08/2023]
Abstract
BACKGROUND Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii. RESULTS We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal. CONCLUSIONS In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.
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Affiliation(s)
| | - Sascha Schäuble
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-University Jena, Jena, Germany
- />Research Group Theoretical Systems Biology, Friedrich-Schiller-University Jena, Jena, Germany
| | - Jacob Valenzuela
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - Saheed Imam
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - Warren Carter
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | | | | | - Serdar Turkarslan
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - David J. Reiss
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - Mónica V. Orellana
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Polar Science Center, University of Washington, Seattle, WA USA
| | - Nathan D. Price
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Departments of Bioengineering and Computer Science and Engineering, University of Washington, Seattle, WA USA
- />Molecular and Cellular Biology Program, University of Washington, Seattle, WA USA
| | - Nitin S. Baliga
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Departments of Biology and Microbiology, University of Washington, Seattle, WA USA
- />Molecular and Cellular Biology Program, University of Washington, Seattle, WA USA
- />Lawrence Berkeley National Lab, Berkeley, CA USA
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