1
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Lu YA, Brien CMO, Mashek DG, Hu WS, Zhang Q. Kinetic-model-based pathway optimization with application to reverse glycolysis in mammalian cells. Biotechnol Bioeng 2023; 120:216-229. [PMID: 36184902 DOI: 10.1002/bit.28249] [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: 07/09/2022] [Revised: 09/19/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022]
Abstract
Over the last two decades, model-based metabolic pathway optimization tools have been developed for the design of microorganisms to produce desired metabolites. However, few have considered more complex cellular systems such as mammalian cells, which requires the use of nonlinear kinetic models to capture the effects of concentration changes and cross-regulatory interactions. In this study, we develop a new two-stage pathway optimization framework based on kinetic models that incorporate detailed kinetics and regulation information. In Stage 1, a set of optimization problems are solved to identify and rank the enzymes that contribute the most to achieving the metabolic objective. Stage 2 then determines the optimal enzyme interventions for specified desired numbers of enzyme adjustments. It also incorporates multi-scenario optimization, which allows the simultaneous consideration of multiple physiological conditions. We apply the proposed framework to find enzyme adjustments that enable a reverse glucose flow in cultured mammalian cells, thereby eliminating the need for glucose feed in the late culture stage and enhancing process robustness. The computational results demonstrate the efficacy of the proposed approach; it not only captures the important regulations and key enzymes for reverse glycolysis but also identifies differences and commonalities in the metabolic requirements for different carbon sources.
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Affiliation(s)
- Yen-An Lu
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, USA
| | - Conor M O' Brien
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, USA
| | - Douglas G Mashek
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Wei-Shou Hu
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, USA
| | - Qi Zhang
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, USA
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2
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Yang P, Liu W, Chen Y, Gong AD. Engineering the glyoxylate cycle for chemical bioproduction. Front Bioeng Biotechnol 2022; 10:1066651. [PMID: 36532595 PMCID: PMC9755347 DOI: 10.3389/fbioe.2022.1066651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/17/2022] [Indexed: 07/24/2023] Open
Abstract
With growing concerns about environmental issues and sustainable economy, bioproduction of chemicals utilizing microbial cell factories provides an eco-friendly alternative to current petro-based processes. Creating high-performance strains (with high titer, yield, and productivity) through metabolic engineering strategies is critical for cost-competitive production. Commonly, it is inevitable to fine-tuning or rewire the endogenous or heterologous pathways in such processes. As an important pathway involved in the synthesis of many kinds of chemicals, the potential of the glyoxylate cycle in metabolic engineering has been studied extensively these years. Here, we review the metabolic regulation of the glyoxylate cycle and summarize recent achievements in microbial production of chemicals through tuning of the glyoxylate cycle, with a focus on studies implemented in model microorganisms. Also, future prospects for bioproduction of glyoxylate cycle-related chemicals are discussed.
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3
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Peoples J, Ruppe S, Mains K, Cipriano EC, Fox JM. A Kinetic Framework for Modeling Oleochemical Biosynthesis in E. coli. Biotechnol Bioeng 2022; 119:3149-3161. [PMID: 35959746 DOI: 10.1002/bit.28209] [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: 06/02/2022] [Revised: 08/01/2022] [Accepted: 08/07/2022] [Indexed: 11/06/2022]
Abstract
Microorganisms build fatty acids with biocatalytic assembly lines, or fatty acid synthases (FASs), that can be repurposed to produce a broad set of fuels and chemicals. Despite their versatility, the product profiles of FAS-based pathways are challenging to adjust without experimental iteration, and off-target products are common. This study uses a detailed kinetic model of the E. coli FAS as a foundation to model nine oleochemical pathways. These models provide good fits to experimental data and help explain unexpected results from in vivo studies. An analysis of pathways for alkanes and fatty acid ethyl esters, for example, suggests that reductions in titer caused by enzyme overexpression-an experimentally consistent phenomenon-can result from shifts in metabolite pools that are incompatible with the substrate specificities of downstream enzymes, and a focused examination of multiple alcohol pathways indicates that coordinated shifts in enzyme concentrations provide a general means of tuning the product profiles of pathways with promiscuous components. The study concludes by integrating all models into a graphical user interface. The models supplied by this work provide a versatile kinetic framework for studying oleochemical pathways in different biochemical contexts. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Jackson Peoples
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, 3415 Colorado Avenue, Boulder, CO, 80303
| | - Sophia Ruppe
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, 3415 Colorado Avenue, Boulder, CO, 80303
| | - Kathryn Mains
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, 3415 Colorado Avenue, Boulder, CO, 80303
| | - Elia C Cipriano
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, 3415 Colorado Avenue, Boulder, CO, 80303
| | - Jerome M Fox
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, 3415 Colorado Avenue, Boulder, CO, 80303
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4
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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: 7] [Impact Index Per Article: 2.3] [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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5
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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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6
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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7
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Liu Y, Benitez MG, Chen J, Harrison E, Khusnutdinova AN, Mahadevan R. Opportunities and Challenges for Microbial Synthesis of Fatty Acid-Derived Chemicals (FACs). Front Bioeng Biotechnol 2021; 9:613322. [PMID: 33575251 PMCID: PMC7870715 DOI: 10.3389/fbioe.2021.613322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/04/2021] [Indexed: 11/13/2022] Open
Abstract
Global warming and uneven distribution of fossil fuels worldwide concerns have spurred the development of alternative, renewable, sustainable, and environmentally friendly resources. From an engineering perspective, biosynthesis of fatty acid-derived chemicals (FACs) is an attractive and promising solution to produce chemicals from abundant renewable feedstocks and carbon dioxide in microbial chassis. However, several factors limit the viability of this process. This review first summarizes the types of FACs and their widely applications. Next, we take a deep look into the microbial platform to produce FACs, give an outlook for the platform development. Then we discuss the bottlenecks in metabolic pathways and supply possible solutions correspondingly. Finally, we highlight the most recent advances in the fast-growing model-based strain design for FACs biosynthesis.
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Affiliation(s)
- Yilan Liu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Mauricio Garcia Benitez
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Jinjin Chen
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Emma Harrison
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Anna N. Khusnutdinova
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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8
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García-Jiménez B, Torres-Bacete J, Nogales J. Metabolic modelling approaches for describing and engineering microbial communities. Comput Struct Biotechnol J 2020; 19:226-246. [PMID: 33425254 PMCID: PMC7773532 DOI: 10.1016/j.csbj.2020.12.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 12/17/2022] Open
Abstract
Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.
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Affiliation(s)
- Beatriz García-Jiménez
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223-Pozuelo de Alarcón, Madrid, Spain
| | - Jesús Torres-Bacete
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC), Madrid, Spain
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9
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Banerjee D, Eng T, Lau AK, Sasaki Y, Wang B, Chen Y, Prahl JP, Singan VR, Herbert RA, Liu Y, Tanjore D, Petzold CJ, Keasling JD, Mukhopadhyay A. Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale. Nat Commun 2020; 11:5385. [PMID: 33097726 PMCID: PMC7584609 DOI: 10.1038/s41467-020-19171-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/30/2020] [Indexed: 01/06/2023] Open
Abstract
High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).
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Affiliation(s)
- Deepanwita Banerjee
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Thomas Eng
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Andrew K Lau
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yusuke Sasaki
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Brenda Wang
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yan Chen
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jan-Philip Prahl
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Advanced Biofuel and Bioproduct Process Development Unit, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Vasanth R Singan
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Robin A Herbert
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yuzhong Liu
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Deepti Tanjore
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Advanced Biofuel and Bioproduct Process Development Unit, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jay D Keasling
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- QB3 Institute, University of California-Berkeley, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA
- Department of Chemical & Biomolecular Engineering, University of California, Berkeley, CA, 94720, USA
- Department of Bioengineering, University of California, Berkeley, CA, 94720, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, 2970, Horsholm, Denmark
- Synthetic Biochemistry Center, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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10
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Software and Methods for Computational Flux Balance Analysis. Methods Mol Biol 2020. [PMID: 32720154 DOI: 10.1007/978-1-0716-0195-2_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
As genetic engineering of organisms has grown easier and more precise, computational modeling of metabolic systems has played an increasingly important role in both guiding experimental interventions and in understanding the results of metabolic perturbations.
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11
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Arabzadeh M, Sedighi M, Saheb Zamani M, Marashi SA. A system architecture for parallel analysis of flux-balanced metabolic pathways. Comput Biol Chem 2020; 88:107309. [PMID: 32650065 DOI: 10.1016/j.compbiolchem.2020.107309] [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: 02/27/2019] [Revised: 05/05/2020] [Accepted: 06/13/2020] [Indexed: 10/24/2022]
Abstract
Elementary flux mode (EFM) analysis is a well-studied method in constraint-based modeling of metabolic networks. In EFM analysis, a network is decomposed into minimal functional pathways based on the assumption of balanced metabolic fluxes. In this paper, a system architecture is proposed that approximately models the functionality of metabolic networks. The AND/OR graph model is used to represent the metabolic network and each processing element in the system emulates the functionality of a metabolite. The system is implemented on a graphics processing unit (GPU) as the hardware platform using CUDA environment. The proposed architecture takes advantage of the inherent parallelism in the network structure in terms of both pathway and metabolite traversal. The function of each element is defined such that it can find flux-balanced pathways. Pathways in both small and large metabolic networks are applied to the proposed architecture and the results are discussed.
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Affiliation(s)
- Mona Arabzadeh
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran.
| | - Mehdi Sedighi
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran.
| | - Morteza Saheb Zamani
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran.
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
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12
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Hall RJ, Thorpe S, Thomas GH, Wood AJ. Simulating the evolutionary trajectories of metabolic pathways for insect symbionts in the genus Sodalis. Microb Genom 2020; 6:mgen000378. [PMID: 32543366 PMCID: PMC7478623 DOI: 10.1099/mgen.0.000378] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Accepted: 04/27/2020] [Indexed: 01/13/2023] Open
Abstract
Insect-bacterial symbioses are ubiquitous, but there is still much to uncover about how these relationships establish, persist and evolve. The tsetse endosymbiont Sodalis glossinidius displays intriguing metabolic adaptations to its microenvironment, but the process by which this relationship evolved remains to be elucidated. The recent chance discovery of the free-living species of the genus Sodalis, Sodalis praecaptivus, provides a serendipitous starting point from which to investigate the evolution of this symbiosis. Here, we present a flux balance model for S. praecaptivus and empirically verify its predictions. Metabolic modelling is used in combination with a multi-objective evolutionary algorithm to explore the trajectories that S. glossinidius may have undertaken from this starting point after becoming internalized. The order in which key genes are lost is shown to influence the evolved populations, providing possible targets for future in vitro genetic manipulation. This method provides a detailed perspective on possible evolutionary trajectories for S. glossinidius in this fundamental process of evolutionary and ecological change.
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Affiliation(s)
- Rebecca J. Hall
- Department of Biology, University of York, York, YO10 5NG, UK
- School of Life Sciences, University of Nottingham, Nottingham, NG7 2TQ, UK
| | - Stephen Thorpe
- Department of Biology, University of York, York, YO10 5NG, UK
- Department of Chemistry, University of Oxford, Oxford, OX1 3TA, UK
| | - Gavin H. Thomas
- Department of Biology, University of York, York, YO10 5NG, UK
| | - A. Jamie Wood
- Department of Biology, University of York, York, YO10 5NG, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
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13
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Röhl A, Riou T, Bockmayr A. Computing irreversible minimal cut sets in genome-scale metabolic networks via flux cone projection. Bioinformatics 2020; 35:2618-2625. [PMID: 30590390 DOI: 10.1093/bioinformatics/bty1027] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 12/06/2018] [Accepted: 12/14/2018] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Minimal cut sets (MCSs) for metabolic networks are sets of reactions which, if they are removed from the network, prevent a target reaction from carrying flux. To compute MCSs different methods exist, which may fail to find sufficiently many MCSs for larger genome-scale networks. RESULTS Here we introduce irreversible minimal cut sets (iMCSs). These are MCSs that consist of irreversible reactions only. The advantage of iMCSs is that they can be computed by projecting the flux cone of the metabolic network on the set of irreversible reactions, which usually leads to a smaller cone. Using oriented matroid theory, we show how the projected cone can be computed efficiently and how this can be applied to find iMCSs even in large genome-scale networks. AVAILABILITY AND IMPLEMENTATION Software is freely available at https://sourceforge.net/projects/irreversibleminimalcutsets/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Annika Röhl
- Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany
| | - Tanguy Riou
- Department FRANCE, Ecole Centrale de Nantes, Nantes, France
| | - Alexander Bockmayr
- Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany
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14
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Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019; 15:e1007084. [PMID: 31295267 PMCID: PMC6622478 DOI: 10.1371/journal.pcbi.1007084] [Citation(s) in RCA: 148] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
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Affiliation(s)
- Guido Zampieri
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
- Healthcare Innovation Centre, Teesside University, Middlesbrough, United Kingdom
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15
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Vieira V, Maia P, Rocha M, Rocha I. Comparison of pathway analysis and constraint-based methods for cell factory design. BMC Bioinformatics 2019; 20:350. [PMID: 31221092 PMCID: PMC6585037 DOI: 10.1186/s12859-019-2934-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 06/05/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs. RESULTS In this work, we perform an in-depth analysis of potential strategies that can be obtained with both methods, providing a critical comparison of performance, robustness, predicted phenotypes as well as strategy structure and size. To this end, we devised a pipeline including enumeration of strategies from evolutionary algorithms (EA) and minimal cut sets (MCS), filtering and flux analysis of predicted mutants to optimize the production of succinic acid in Saccharomyces cerevisiae. We additionally attempt to generalize problem formulations for MCS enumeration within the context of growth-coupled product synthesis. Strategies from evolutionary algorithms show the best compromise between acceptable growth rates and compound overproduction. However, constrained MCSs lead to a larger variety of phenotypes with several degrees of growth-coupling with production flux. The latter have proven useful in revealing the importance, in silico, of the gamma-aminobutyric acid shunt and manipulation of cofactor pools in growth-coupled designs for succinate production, mechanisms which have also been touted as potentially useful for metabolic engineering. CONCLUSIONS The two main groups of CSOMs are valuable for finding growth-coupled mutants. Despite the limitations in maximum growth rates and large strategy sizes, MCSs help uncover novel mechanisms for compound overproduction and thus, analyzing outputs from both methods provides a richer overview on strategies that can be potentially carried over in vivo.
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Affiliation(s)
- Vítor Vieira
- Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal
| | | | - Miguel Rocha
- Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal
| | - Isabel Rocha
- Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal
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16
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Garcia S, Trinh CT. Modular design: Implementing proven engineering principles in biotechnology. Biotechnol Adv 2019; 37:107403. [PMID: 31181317 DOI: 10.1016/j.biotechadv.2019.06.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 04/23/2019] [Accepted: 06/04/2019] [Indexed: 12/27/2022]
Abstract
Modular design is at the foundation of contemporary engineering, enabling rapid, efficient, and reproducible construction and maintenance of complex systems across applications. Remarkably, modularity has recently been discovered as a governing principle in natural biological systems from genes to proteins to complex networks within a cell and organism communities. The convergent knowledge of natural and engineered modular systems provides a key to drive modern biotechnology to address emergent challenges associated with health, food, energy, and the environment. Here, we first present the theory and application of modular design in traditional engineering fields. We then discuss the significance and impact of modular architectures on systems biology and biotechnology. Next, we focus on the very recent theoretical and experimental advances in modular cell engineering that seeks to enable rapid and systematic development of microbial catalysts capable of efficiently synthesizing a large space of useful chemicals. We conclude with an outlook towards theoretical and practical opportunities for a more systematic and effective application of modular cell engineering in biotechnology.
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Affiliation(s)
- Sergio Garcia
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, United States of America; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America
| | - Cong T Trinh
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, United States of America; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America.
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17
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Xi Y, Wang F. Extreme pathway analysis reveals the organizing rules of metabolic regulation. PLoS One 2019; 14:e0210539. [PMID: 30721240 PMCID: PMC6363282 DOI: 10.1371/journal.pone.0210539] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 12/27/2018] [Indexed: 11/18/2022] Open
Abstract
Cellular systems shift metabolic states by adjusting gene expression and enzyme activities to adapt to physiological and environmental changes. Biochemical and genetic studies are identifying how metabolic regulation affects the selection of metabolic phenotypes. However, how metabolism influences its regulatory architecture still remains unexplored. We present a new method of extreme pathway analysis (the minimal set of conically independent metabolic pathways) to deduce regulatory structures from pure pathway information. Applying our method to metabolic networks of human red blood cells and Escherichia coli, we shed light on how metabolic regulation are organized by showing which reactions within metabolic networks are more prone to transcriptional or allosteric regulation. Applied to a human genome-scale metabolic system, our method detects disease-associated reactions. Thus, our study deepens the understanding of the organizing principle of cellular metabolic regulation and may contribute to metabolic engineering, synthetic biology, and disease treatment.
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Affiliation(s)
- Yanping Xi
- Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
- School of Computer Science and Technology, Fudan University, Shanghai, China
- Shanghai Ji Ai Genetics & IVF Institute, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Fei Wang
- Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
- School of Computer Science and Technology, Fudan University, Shanghai, China
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18
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Multiobjective strain design: A framework for modular cell engineering. Metab Eng 2019; 51:110-120. [DOI: 10.1016/j.ymben.2018.09.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 09/05/2018] [Accepted: 09/05/2018] [Indexed: 01/28/2023]
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19
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Dinh HV, King ZA, Palsson BO, Feist AM. Identification of growth-coupled production strains considering protein costs and kinetic variability. Metab Eng Commun 2018; 7:e00080. [PMID: 30370222 PMCID: PMC6199775 DOI: 10.1016/j.mec.2018.e00080] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 09/25/2018] [Accepted: 10/07/2018] [Indexed: 12/13/2022] Open
Abstract
Conversion of renewable biomass to useful molecules in microbial cell factories can be approached in a rational and systematic manner using constraint-based reconstruction and analysis. Filtering for high confidence in silico designs is critical because in vivo construction and testing of strains is expensive and time consuming. As such, a workflow was devised to analyze the robustness of growth-coupled production when considering the biosynthetic costs of the proteome and variability in enzyme kinetic parameters using a genome-scale model of metabolism and gene expression (ME-model). A collection of 2632 unfiltered knockout designs in Escherichia coli was evaluated by the workflow. A ME-model was used in the workflow to test the designs' growth-coupled production in addition to a less complex genome-scale metabolic model (M-model). The workflow identified 634 M-model growth-coupled designs which met the filtering criteria and 42 robust designs, which met growth-coupled production criteria using both M and ME-models. Knockouts were found to follow a pattern of controlling intermediate metabolite consumption such as pyruvate consumption and high flux subsystems such as glycolysis. Kinetic parameter sampling using the ME-model revealed how enzyme efficiency and pathway tradeoffs can affect growth-coupled production phenotypes.
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Affiliation(s)
- Hoang V. Dinh
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
| | - Zachary A. King
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, DK-2800 Kongens, Lyngby, Denmark
| | - Adam M. Feist
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, DK-2800 Kongens, Lyngby, Denmark
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20
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Koduru L, Lakshmanan M, Lee DY. In silico model-guided identification of transcriptional regulator targets for efficient strain design. Microb Cell Fact 2018; 17:167. [PMID: 30359263 PMCID: PMC6201637 DOI: 10.1186/s12934-018-1015-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 10/20/2018] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application. RESULTS We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification. CONCLUSIONS In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.
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Affiliation(s)
- Lokanand Koduru
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117576, Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore.
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
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21
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Synthetic Gene Regulation in Cyanobacteria. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1080:317-355. [DOI: 10.1007/978-981-13-0854-3_13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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22
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Castañeda MT, Nuñez S, Garelli F, Voget C, De Battista H. Comprehensive analysis of a metabolic model for lipid production in Rhodosporidium toruloides. J Biotechnol 2018; 280:11-18. [PMID: 29787798 DOI: 10.1016/j.jbiotec.2018.05.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 04/27/2018] [Accepted: 05/16/2018] [Indexed: 11/16/2022]
Abstract
The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with flux balance analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism.
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Affiliation(s)
- María Teresita Castañeda
- Centro de Investigación y Desarrollo en Fermentaciones Industriales (CINDEFI), UNLP-CONICET, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina; Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET, Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina.
| | - Sebastián Nuñez
- Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET, Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
| | - Fabricio Garelli
- Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET, Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
| | - Claudio Voget
- Centro de Investigación y Desarrollo en Fermentaciones Industriales (CINDEFI), UNLP-CONICET, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina
| | - Hernán De Battista
- Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET, Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
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23
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Shepelin D, Hansen ASL, Lennen R, Luo H, Herrgård MJ. Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes. Genes (Basel) 2018; 9:E249. [PMID: 29751691 PMCID: PMC5977189 DOI: 10.3390/genes9050249] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 05/02/2018] [Accepted: 05/02/2018] [Indexed: 01/10/2023] Open
Abstract
Microbial cell factories have proven to be an economical means of production for many bulk, specialty, and fine chemical products. However, we still lack both a holistic understanding of organism physiology and the ability to predictively tune enzyme activities in vivo, thus slowing down rational engineering of industrially relevant strains. An alternative concept to rational engineering is to use evolution as the driving force to select for desired changes, an approach often described as evolutionary engineering. In evolutionary engineering, in vivo selections for a desired phenotype are combined with either generation of spontaneous mutations or some form of targeted or random mutagenesis. Evolutionary engineering has been used to successfully engineer easily selectable phenotypes, such as utilization of a suboptimal nutrient source or tolerance to inhibitory substrates or products. In this review, we focus primarily on a more challenging problem-the use of evolutionary engineering for improving the production of chemicals in microbes directly. We describe recent developments in evolutionary engineering strategies, in general, and discuss, in detail, case studies where production of a chemical has been successfully achieved through evolutionary engineering by coupling production to cellular growth.
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Affiliation(s)
- Denis Shepelin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Anne Sofie Lærke Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Rebecca Lennen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Hao Luo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
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24
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Klamt S, Müller S, Regensburger G, Zanghellini J. A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering. Metab Eng 2018; 47:153-169. [PMID: 29427605 PMCID: PMC5992331 DOI: 10.1016/j.ymben.2018.02.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2017] [Revised: 01/22/2018] [Accepted: 02/03/2018] [Indexed: 12/16/2022]
Abstract
BACKGROUND The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. RESULTS We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small example and demonstrate their relevance for metabolic engineering with realistic models of E. coli. CONCLUSIONS We develop a comprehensive mathematical framework for yield optimization in metabolic models. Our theory is particularly useful for the study and rational modification of cell factories designed under given yield and/or rate requirements.
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Affiliation(s)
- Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
| | - Stefan Müller
- Faculty of Mathematics, University of Vienna, Austria.
| | | | - Jürgen Zanghellini
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria.
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25
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Systematic metabolic engineering of Methylomicrobium alcaliphilum 20Z for 2,3-butanediol production from methane. Metab Eng 2018; 47:323-333. [DOI: 10.1016/j.ymben.2018.04.010] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 04/12/2018] [Accepted: 04/13/2018] [Indexed: 11/23/2022]
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26
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Cardoso JGR, Jensen K, Lieven C, Lærke Hansen AS, Galkina S, Beber M, Özdemir E, Herrgård MJ, Redestig H, Sonnenschein N. Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories. ACS Synth Biol 2018; 7:1163-1166. [PMID: 29558112 DOI: 10.1021/acssynbio.7b00423] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .
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Affiliation(s)
- João G. R. Cardoso
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Kristian Jensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Christian Lieven
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Anne Sofie Lærke Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Svetlana Galkina
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Moritz Beber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Emre Özdemir
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Markus J. Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Henning Redestig
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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27
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Constraint-based modeling in microbial food biotechnology. Biochem Soc Trans 2018; 46:249-260. [PMID: 29588387 PMCID: PMC5906707 DOI: 10.1042/bst20170268] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 03/01/2018] [Accepted: 03/02/2018] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.
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28
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Abstract
When aiming to produce a target chemical at high yield, titer, and productivity, various combinations of genetic parts available to build the target pathway can generate a large number of strains for characterization. This engineering approach will become increasingly laborious and expensive when seeking to develop desirable strains for optimal production of a large space of biochemicals due to extensive screening. Our recent theoretical development of modular cell (MODCELL) design principles can offer a promising solution for rapid generation of optimal strains by coupling a modular cell with exchangeable production modules in a plug-and-play fashion. In this study, we experimentally validated some design properties of MODCELL by demonstrating the following: (i) a modular (chassis) cell is required to couple with a production module, a heterologous ethanol pathway, as a testbed, (ii) degree of coupling between the modular cell and production modules can be modulated to enhance growth and product synthesis, (iii) a modular cell can be used as a host to select an optimal pyruvate decarboxylase (PDC) of the ethanol production module and to help identify a hypothetical PDC protein, and (iv) adaptive laboratory evolution based on growth selection of the modular cell can enhance growth and product synthesis rates. We envision that the MODCELL design provides a powerful prototype for modular cell engineering to rapidly create optimal strains for synthesis of a large space of biochemicals.
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Affiliation(s)
- Brandon Wilbanks
- Department of Chemical and
Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Donovan S. Layton
- Department of Chemical and
Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Sergio Garcia
- Department of Chemical and
Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Cong T. Trinh
- Department of Chemical and
Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
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29
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Arabzadeh M, Saheb Zamani M, Sedighi M, Marashi SA. A graph-based approach to analyze flux-balanced pathways in metabolic networks. Biosystems 2018; 165:40-51. [PMID: 29337084 DOI: 10.1016/j.biosystems.2017.12.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 11/02/2017] [Accepted: 12/05/2017] [Indexed: 10/18/2022]
Abstract
An elementary flux mode (EFM) is a pathway with minimum set of reactions that are functional in steady-state constrained space. Due to the high computational complexity of calculating EFMs, different approaches have been proposed to find these flux-balanced pathways. In this paper, an approach to find a subset of EFMs is proposed based on a graph data model. The given metabolic network is mapped to the graph model and decisions for reaction inclusion can be made based on metabolites and their associated reactions. This notion makes the approach more convenient to categorize the output pathways. Implications of the proposed method on metabolic networks are discussed.
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Affiliation(s)
- Mona Arabzadeh
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran.
| | - Morteza Saheb Zamani
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran.
| | - Mehdi Sedighi
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran.
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
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30
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Campodonico MA, Sukumara S, Feist AM, Herrgård MJ. Computational Methods to Assess the Production Potential of Bio-Based Chemicals. Methods Mol Biol 2018; 1671:97-116. [PMID: 29170955 DOI: 10.1007/978-1-4939-7295-1_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Elevated costs and long implementation times of bio-based processes for producing chemicals represent a bottleneck for moving to a bio-based economy. A prospective analysis able to elucidate economically and technically feasible product targets at early research phases is mandatory. Computational tools can be implemented to explore the biological and technical spectrum of feasibility, while constraining the operational space for desired chemicals. In this chapter, two different computational tools for assessing potential for bio-based production of chemicals from different perspectives are described in detail. The first tool is GEM-Path: an algorithm to compute all structurally possible pathways from one target molecule to the host metabolome. The second tool is a framework for Modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes, and economic impact assessment. Integrating GEM-Path and MuSIC will play a vital role in supporting early phases of research efforts and guide the policy makers with decisions, as we progress toward planning a sustainable chemical industry.
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Affiliation(s)
- Miguel A Campodonico
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
| | - Sumesh Sukumara
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Adam M Feist
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0412, USA
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
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Hartmann A, Vila-Santa A, Kallscheuer N, Vogt M, Julien-Laferrière A, Sagot MF, Marienhagen J, Vinga S. OptPipe - a pipeline for optimizing metabolic engineering targets. BMC SYSTEMS BIOLOGY 2017; 11:143. [PMID: 29268790 PMCID: PMC5740890 DOI: 10.1186/s12918-017-0515-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 11/21/2017] [Indexed: 11/10/2022]
Abstract
BACKGROUND We propose OptPipe - a Pipeline for Optimizing Metabolic Engineering Targets, based on a consensus approach. The method generates consensus hypotheses for metabolic engineering applications by combining several optimization solutions obtained from distinct algorithms. The solutions are ranked according to several objectives, such as biomass and target production, by using the rank product tests corrected for multiple comparisons. RESULTS OptPipe was applied in a genome-scale model of Corynebacterium glutamicum for maximizing malonyl-CoA, which is a valuable precursor for many phenolic compounds. In vivo experimental validation confirmed increased malonyl-CoA level in case of ΔsdhCAB deletion, as predicted in silico. CONCLUSIONS A method was developed to combine the optimization solutions provided by common knockout prediction procedures and rank the suggested mutants according to the expected growth rate, production and a new adaptability measure. The implementation of the pipeline along with the complete documentation is freely available at https://github.com/AndrasHartmann/OptPipe .
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Affiliation(s)
- András Hartmann
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001 Portugal
| | - Ana Vila-Santa
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001 Portugal
| | - Nicolai Kallscheuer
- Institute of Bio- and Geosciences, IBG-1: Biotechnology Forschungszentrum Jülich GmbH, Jülich, D-52425 Germany
| | - Michael Vogt
- Institute of Bio- and Geosciences, IBG-1: Biotechnology Forschungszentrum Jülich GmbH, Jülich, D-52425 Germany
| | - Alice Julien-Laferrière
- EPI ERABLE, Inria Grenoble, Rhône-Alpes, France
- Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, F-69622 France
| | - Marie-France Sagot
- EPI ERABLE, Inria Grenoble, Rhône-Alpes, France
- Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, F-69622 France
| | - Jan Marienhagen
- Institute of Bio- and Geosciences, IBG-1: Biotechnology Forschungszentrum Jülich GmbH, Jülich, D-52425 Germany
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisbon, 1049-001 Portugal
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Klamt S, Mahadevan R, Hädicke O. When Do Two-Stage Processes Outperform One-Stage Processes? Biotechnol J 2017; 13. [PMID: 29131522 DOI: 10.1002/biot.201700539] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 10/26/2017] [Indexed: 12/30/2022]
Abstract
Apart from product yield and titer, volumetric productivity is a key performance indicator for many biotechnological processes. Due to the inherent trade-off between the production of biomass as catalyst and of the actual target product, yield and volumetric productivity cannot be optimized simultaneously. Therefore, in combination with genetic techniques for dynamic regulation of metabolic fluxes, two-stage fermentations (TSFs) with separated growth and production phase have recently gained much interest because of their potential to improve the productivity of bioprocesses while still allowing high product yields. However, despite some successful case studies, so far it has not been discussed and analyzed systematically whether or under which conditions a TSF guarantees superior productivity compared to one-stage fermentation (OSF). In this study, we use mathematical models to demonstrate that the volumetric productivity of a TSF is not automatically better than of a corresponding OSF. Our analysis reveals that the sharp decrease of the specific substrate uptake rate usually observed in (non-growth) production phases severely impacts the volumetric productivity and thus raises a big challenge for designing competitive TSF processes. We discuss possible approaches such as enforced ATP wasting to improve substrate utilization rates in the production phase by which TSF processes can become superior to OSF. We also analyze additional factors influencing the relative performance of OSF and TSF and show that OSF processes can be more appropriate if a high product yield is an economic constraint. In conclusion, a careful assessment of the trade-offs between substrate uptake rates, yields, and productivity is necessary when deciding for OSF vs. TSF processes.
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Affiliation(s)
- Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering & Applied Chemistry, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Oliver Hädicke
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany
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33
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Use of CellNetAnalyzer in biotechnology and metabolic engineering. J Biotechnol 2017; 261:221-228. [DOI: 10.1016/j.jbiotec.2017.05.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/28/2017] [Accepted: 05/03/2017] [Indexed: 01/28/2023]
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von Kamp A, Klamt S. Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms. Nat Commun 2017; 8:15956. [PMID: 28639622 PMCID: PMC5489714 DOI: 10.1038/ncomms15956] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 05/16/2017] [Indexed: 12/13/2022] Open
Abstract
Computational modelling of metabolic networks has become an established procedure in the metabolic engineering of production strains. One key principle that is frequently used to guide the rational design of microbial cell factories is the stoichiometric coupling of growth and product synthesis, which makes production of the desired compound obligatory for growth. Here we show that the coupling of growth and production is feasible under appropriate conditions for almost all metabolites in genome-scale metabolic models of five major production organisms. These organisms comprise eukaryotes and prokaryotes as well as heterotrophic and photoautotrophic organisms, which shows that growth coupling as a strain design principle has a wide applicability. The feasibility of coupling is proven by calculating appropriate reaction knockouts, which enforce the coupling behaviour. The study presented here is the most comprehensive computational investigation of growth-coupled production so far and its results are of fundamental importance for rational metabolic engineering.
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Affiliation(s)
- Axel von Kamp
- ARB Group, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, Magdeburg 39106, Germany
| | - Steffen Klamt
- ARB Group, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, Magdeburg 39106, Germany
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35
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Pandit AV, Srinivasan S, Mahadevan R. Redesigning metabolism based on orthogonality principles. Nat Commun 2017; 8:15188. [PMID: 28555623 PMCID: PMC5459945 DOI: 10.1038/ncomms15188] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 03/08/2017] [Indexed: 01/25/2023] Open
Abstract
Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering.
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Affiliation(s)
- Aditya Vikram Pandit
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5
| | - Shyam Srinivasan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario, CanadaM5S 3G9
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36
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Klamt S, Regensburger G, Gerstl MP, Jungreuthmayer C, Schuster S, Mahadevan R, Zanghellini J, Müller S. From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints. PLoS Comput Biol 2017; 13:e1005409. [PMID: 28406903 PMCID: PMC5390976 DOI: 10.1371/journal.pcbi.1005409] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [FBA]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks.
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Affiliation(s)
- Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Georg Regensburger
- Institute for Algebra, Johannes Kepler University Linz (JKU), Linz, Austria
| | - Matthias P. Gerstl
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Biotechnology, Vienna, Austria
| | - Christian Jungreuthmayer
- Austrian Centre of Biotechnology, Vienna, Austria
- TGM - Technologisches Gewerbemuseum, Vienna, Austria
| | - Stefan Schuster
- Department of Bioinformatics, Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Jena, Germany
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering & Applied Chemistry, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Jürgen Zanghellini
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Biotechnology, Vienna, Austria
| | - Stefan Müller
- Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences, Linz, Austria
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37
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Hansen ASL, Lennen RM, Sonnenschein N, Herrgård MJ. Systems biology solutions for biochemical production challenges. Curr Opin Biotechnol 2017; 45:85-91. [PMID: 28319856 DOI: 10.1016/j.copbio.2016.11.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Revised: 11/20/2016] [Accepted: 11/23/2016] [Indexed: 11/28/2022]
Abstract
There is an urgent need to significantly accelerate the development of microbial cell factories to produce fuels and chemicals from renewable feedstocks in order to facilitate the transition to a biobased society. Methods commonly used within the field of systems biology including omics characterization, genome-scale metabolic modeling, and adaptive laboratory evolution can be readily deployed in metabolic engineering projects. However, high performance strains usually carry tens of genetic modifications and need to operate in challenging environmental conditions. This additional complexity compared to basic science research requires pushing systems biology strategies to their limits and often spurs innovative developments that benefit fields outside metabolic engineering. Here we survey recent advanced applications of systems biology methods in engineering microbial production strains for biofuels and -chemicals.
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Affiliation(s)
- Anne Sofie Lærke Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark
| | - Rebecca M Lennen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark
| | - Nikolaus Sonnenschein
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs., Lyngby, Denmark.
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38
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Hädicke O, Klamt S. EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model. Sci Rep 2017; 7:39647. [PMID: 28045126 PMCID: PMC5206746 DOI: 10.1038/srep39647] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 11/25/2016] [Indexed: 02/06/2023] Open
Abstract
Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli's central metabolism.
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Affiliation(s)
- Oliver Hädicke
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany
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39
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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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40
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Shabestary K, Hudson EP. Computational metabolic engineering strategies for growth-coupled biofuel production by Synechocystis. Metab Eng Commun 2016; 3:216-226. [PMID: 29468126 PMCID: PMC5779732 DOI: 10.1016/j.meteno.2016.07.003] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Revised: 05/31/2016] [Accepted: 07/19/2016] [Indexed: 11/28/2022] Open
Abstract
Chemical and fuel production by photosynthetic cyanobacteria is a promising technology but to date has not reached competitive rates and titers. Genome-scale metabolic modeling can reveal limitations in cyanobacteria metabolism and guide genetic engineering strategies to increase chemical production. Here, we used constraint-based modeling and optimization algorithms on a genome-scale model of Synechocystis PCC6803 to find ways to improve productivity of fermentative, fatty-acid, and terpene-derived fuels. OptGene and MOMA were used to find heuristics for knockout strategies that could increase biofuel productivity. OptKnock was used to find a set of knockouts that led to coupling between biofuel and growth. Our results show that high productivity of fermentation or reversed beta-oxidation derived alcohols such as 1-butanol requires elimination of NADH sinks, while terpenes and fatty-acid based fuels require creating imbalances in intracellular ATP and NADPH production and consumption. The FBA-predicted productivities of these fuels are at least 10-fold higher than those reported so far in the literature. We also discuss the physiological and practical feasibility of implementing these knockouts. This work gives insight into how cyanobacteria could be engineered to reach competitive biofuel productivities.
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Affiliation(s)
| | - Elton P. Hudson
- School of Biotechnology, KTH - Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
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41
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Trinh CT, Mendoza B. Modular cell design for rapid, efficient strain engineering toward industrialization of biology. Curr Opin Chem Eng 2016. [DOI: 10.1016/j.coche.2016.07.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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42
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Harder BJ, Bettenbrock K, Klamt S. Model-based metabolic engineering enables high yield itaconic acid production by Escherichia coli. Metab Eng 2016; 38:29-37. [DOI: 10.1016/j.ymben.2016.05.008] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 05/03/2016] [Accepted: 05/31/2016] [Indexed: 10/21/2022]
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43
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Machado D, Herrgård MJ, Rocha I. Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction. PLoS Comput Biol 2016; 12:e1005140. [PMID: 27711110 PMCID: PMC5053500 DOI: 10.1371/journal.pcbi.1005140] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 09/13/2016] [Indexed: 12/05/2022] Open
Abstract
Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.
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Affiliation(s)
- Daniel Machado
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Markus J. Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Horsølm, Denmark
| | - Isabel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
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44
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Jian X, Zhou S, Zhang C, Hua Q. In silico identification of gene amplification targets based on analysis of production and growth coupling. Biosystems 2016; 145:1-8. [DOI: 10.1016/j.biosystems.2016.05.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 05/03/2016] [Accepted: 05/04/2016] [Indexed: 11/16/2022]
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45
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Villaverde AF, Bongard S, Mauch K, Balsa-Canto E, Banga JR. Metabolic engineering with multi-objective optimization of kinetic models. J Biotechnol 2016; 222:1-8. [PMID: 26826510 DOI: 10.1016/j.jbiotec.2016.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 12/30/2015] [Accepted: 01/11/2016] [Indexed: 10/22/2022]
Abstract
Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.
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Affiliation(s)
- Alejandro F Villaverde
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain; Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; Department of Systems and Control Engineering, Universidade de Vigo, Rua Maxwell, 36310 Vigo, Spain
| | - Sophia Bongard
- Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany
| | - Klaus Mauch
- Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany
| | - Eva Balsa-Canto
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
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46
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Zhang C, Hua Q. Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine. Front Physiol 2016; 6:413. [PMID: 26779040 PMCID: PMC4703781 DOI: 10.3389/fphys.2015.00413] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 12/15/2015] [Indexed: 12/21/2022] Open
Abstract
Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.
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Affiliation(s)
- Cheng Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and TechnologyShanghai, China
| | - Qiang Hua
- State Key Laboratory of Bioreactor Engineering, East China University of Science and TechnologyShanghai, China
- Shanghai Collaborative Innovation Center for Biomanufacturing TechnologyShanghai, China
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King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 2016; 44:D515-22. [PMID: 26476456 PMCID: PMC4702785 DOI: 10.1093/nar/gkv1049] [Citation(s) in RCA: 481] [Impact Index Per Article: 60.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 09/27/2015] [Accepted: 10/02/2015] [Indexed: 11/14/2022] Open
Abstract
Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.
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Affiliation(s)
- Zachary A King
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Justin Lu
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Andreas Dräger
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Philip Miller
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Stephen Federowicz
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Joshua A Lerman
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Ali Ebrahim
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA 92093, USA
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