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Legon L, Corre C, Bates DG, Mannan AA. gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs. Bioinformatics 2022; 38:3657-3659. [PMID: 35642935 PMCID: PMC9272801 DOI: 10.1093/bioinformatics/btac376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/09/2022] [Accepted: 05/30/2022] [Indexed: 11/14/2022] Open
Abstract
Motivation A widely applicable strategy to create cell factories is to knockout (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesize at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow. Results To address this multi-objective optimization problem, we developed a software tool named gcFront—using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth-coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run—granting users flexibility in selecting designs to take to the lab. Availability and implementation gcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Laurence Legon
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, UK
- Warwick Integrative Synthetic Biology Centre, School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Christophe Corre
- Warwick Integrative Synthetic Biology Centre, School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
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Leggieri PA, Liu Y, Hayes M, Connors B, Seppälä S, O'Malley MA, Venturelli OS. Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes. Annu Rev Biomed Eng 2021; 23:169-201. [PMID: 33781078 PMCID: PMC8277735 DOI: 10.1146/annurev-bioeng-082120-022836] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.
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Affiliation(s)
- Patrick A Leggieri
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Yiyi Liu
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Madeline Hayes
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
| | - Bryce Connors
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Susanna Seppälä
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Michelle A O'Malley
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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3
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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Schneider P, Klamt S. Characterizing and ranking computed metabolic engineering strategies. Bioinformatics 2020; 35:3063-3072. [PMID: 30649194 PMCID: PMC6735923 DOI: 10.1093/bioinformatics/bty1065] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 11/28/2018] [Accepted: 01/07/2019] [Indexed: 01/06/2023] Open
Abstract
MOTIVATION The computer-aided design of metabolic intervention strategies has become a key component of an integrated metabolic engineering approach and a broad range of methods and algorithms has been developed for this task. Many of these algorithms enforce coupling of growth with product synthesis and may return thousands of possible intervention strategies from which the most suitable strategy must then be selected. RESULTS This work focuses on how to evaluate and rank, in a meaningful way, a given pool of computed metabolic engineering strategies for growth-coupled product synthesis. Apart from straightforward criteria, such as a preferably small number of necessary interventions, a reasonable growth rate and a high product yield, we present several new criteria useful to pick the most suitable intervention strategy. Among others, we investigate the robustness of the intervention strategies by searching for metabolites that may disrupt growth coupling when accumulated or secreted and by checking whether the interventions interrupt pathways at their origin (preferable) or at downstream steps. We also assess thermodynamic properties of the pathway(s) favored by the intervention strategy. Furthermore, strategies that have a significant overlap with alternative solutions are ranked higher because they provide flexibility in implementation. We also introduce the notion of equivalence classes for grouping intervention strategies with identical solution spaces. Our ranking procedure involves in total ten criteria and we demonstrate its applicability by assessing knockout-based intervention strategies computed in a genome-scale model of E.coli for the growth-coupled synthesis of l-methionine and of the heterologous product 1,4-butanediol. AVAILABILITY AND IMPLEMENTATION The MATLAB scripts that were used to characterize and rank the example intervention strategies are available at http://www2.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany
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Abstract
Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.
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Milke L, Marienhagen J. Engineering intracellular malonyl-CoA availability in microbial hosts and its impact on polyketide and fatty acid synthesis. Appl Microbiol Biotechnol 2020; 104:6057-6065. [PMID: 32385515 PMCID: PMC7316851 DOI: 10.1007/s00253-020-10643-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/09/2020] [Accepted: 04/21/2020] [Indexed: 12/20/2022]
Abstract
Malonyl-CoA is an important central metabolite serving as the basic building block for the microbial synthesis of many pharmaceutically interesting polyketides, but also fatty acid-derived compounds including biofuels. Especially Saccharomyces cerevisiae, Escherichia coli, and Corynebacterium glutamicum have been engineered towards microbial synthesis of such compounds in recent years. However, developed strains and processes often suffer from insufficient productivity. Usually, tightly regulated intracellular malonyl-CoA availability is regarded as the decisive bottleneck limiting overall product formation. Therefore, metabolic engineering towards improved malonyl-CoA availability is essential to design efficient microbial cell factories for the production of polyketides and fatty acid derivatives. This review article summarizes metabolic engineering strategies to improve intracellular malonyl-CoA formation in industrially relevant microorganisms and its impact on productivity and product range, with a focus on polyketides and other malonyl-CoA-dependent products.Key Points• Malonyl-CoA is the central building block of polyketide synthesis.• Increasing acetyl-CoA supply is pivotal to improve malonyl-CoA availability.• Improved acetyl-CoA carboxylase activity increases availability of malonyl-CoA.• Fatty acid synthesis as an ambivalent target to improve malonyl-CoA supply.
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Affiliation(s)
- Lars Milke
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Jan Marienhagen
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany. .,Institute of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074, Aachen, Germany. .,Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.
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Andrade R, Doostmohammadi M, Santos JL, Sagot MF, Mira NP, Vinga S. MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering. BMC Bioinformatics 2020; 21:69. [PMID: 32093622 PMCID: PMC7041195 DOI: 10.1186/s12859-020-3377-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 01/20/2020] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. RESULTS Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. CONCLUSIONS The multi-objective programming framework we developed, called MOMO, is open-source and uses POLYSCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. MOMO is available at http://momo-sysbio.gforge.inria.fr.
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Affiliation(s)
- Ricardo Andrade
- ERABLE European Team, INRIA, Rhône-Alpes, France
- Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, Villeurbanne, F-69622, France
- Institute of Mathematics and Statistics, Universidade de São Paulo, Sao Paulo, Brazil
| | - Mahdi Doostmohammadi
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Department of Management Science, University of Strathclyde, Glasgow, UK
| | - João L Santos
- Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisbon, Portugal
| | - Marie-France Sagot
- ERABLE European Team, INRIA, Rhône-Alpes, France
- Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, Villeurbanne, F-69622, France
| | - Nuno P Mira
- Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Department of Bioengineering, Universidade de Lisboa, Lisbon, Portugal.
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
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Milke L, Ferreira P, Kallscheuer N, Braga A, Vogt M, Kappelmann J, Oliveira J, Silva AR, Rocha I, Bott M, Noack S, Faria N, Marienhagen J. Modulation of the central carbon metabolism of Corynebacterium glutamicum improves malonyl-CoA availability and increases plant polyphenol synthesis. Biotechnol Bioeng 2019; 116:1380-1391. [PMID: 30684355 DOI: 10.1002/bit.26939] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 12/10/2018] [Accepted: 01/22/2019] [Indexed: 12/21/2022]
Abstract
In recent years microorganisms have been engineered towards synthesizing interesting plant polyphenols such as flavonoids and stilbenes from glucose. Currently, the low endogenous supply of malonyl-CoA, indispensable for plant polyphenol synthesis, impedes high product titers. Usually, limited malonyl-CoA availability during plant polyphenol production is avoided by supplementing fatty acid synthesis-inhibiting antibiotics such as cerulenin, which are known to increase the intracellular malonyl-CoA pool as a side effect. Motivated by the goal of microbial polyphenol synthesis being independent of such expensive additives, we used rational metabolic engineering approaches to modulate regulation of fatty acid synthesis and flux into the tricarboxylic acid cycle (TCA cycle) in Corynebacterium glutamicum strains capable of flavonoid and stilbene synthesis. Initial experiments showed that sole overexpression of genes coding for the native malonyl-CoA-forming acetyl-CoA carboxylase is not sufficient for increasing polyphenol production in C. glutamicum. Hence, the intracellular acetyl-CoA availability was also increased by reducing the flux into the TCA cycle through reduction of citrate synthase activity. In defined cultivation medium, the constructed C. glutamicum strains accumulated 24 mg·L -1 (0.088 mM) naringenin or 112 mg·L -1 (0.49 mM) resveratrol from glucose without supplementation of phenylpropanoid precursor molecules or any inhibitors of fatty acid synthesis.
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Affiliation(s)
- Lars Milke
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
| | - Patrícia Ferreira
- Biotempo, Guimarães, Portugal.,Centre of Biological Engineering, University of Minho-Campus de Gualtar, Braga, Portugal
| | - Nicolai Kallscheuer
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
| | - Adelaide Braga
- Biotempo, Guimarães, Portugal.,Centre of Biological Engineering, University of Minho-Campus de Gualtar, Braga, Portugal
| | - Michael Vogt
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
| | - Jannick Kappelmann
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
| | - Joana Oliveira
- Biotempo, Guimarães, Portugal.,Centre of Biological Engineering, University of Minho-Campus de Gualtar, Braga, Portugal
| | - Ana Rita Silva
- Biotempo, Guimarães, Portugal.,Centre of Biological Engineering, University of Minho-Campus de Gualtar, Braga, Portugal
| | - Isabel Rocha
- Biotempo, Guimarães, Portugal.,Centre of Biological Engineering, University of Minho-Campus de Gualtar, Braga, Portugal.,Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Michael Bott
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
| | - Nuno Faria
- Biotempo, Guimarães, Portugal.,Centre of Biological Engineering, University of Minho-Campus de Gualtar, Braga, Portugal
| | - Jan Marienhagen
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany
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Costa RS, Vinga S. Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: Validation against experimental data. Biotechnol Prog 2018; 34:1344-1354. [PMID: 30294889 DOI: 10.1002/btpr.2700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/14/2018] [Indexed: 11/11/2022]
Abstract
Over the last years, several genome-scale metabolic models (GEMs) and kinetic models of Escherichia coli were published. Their predictive performance varies according to the evaluation metric considered, the computational simulation methods used, and the type/quality of experimental data available. However, the GEM approach is often not compared with the kinetic modeling framework. Also, the different genome-scale reconstruction versions and simulation methods of mutant phenotypes are usually not validated to predict intracellular fluxes using large experimental datasets. Here, we intended to (i) systematically evaluate the prediction performance of three E. coli GEMs (iJR904, iAF1260, and iJO1366) available in the literature according to predictive growth metrics (intracellular flux distribution); (ii) assess the reliability of a E. coli GEM in the prediction of gene knockout phenotypes when different simulation methods (parsimonious flux balance analysis, Minimization of Metabolic Adjustment, linear version of MoMA, Regulatory on/off minimization, and Minimization of Metabolites Balance) are used; and finally (iii) investigate the flux distribution predictive power of the constrained-based modeling approach (selected stoichiometric GEM) and compare it with the kinetic modeling approach (two published kinetic models) for E. coli central metabolism, in order to assess their accuracy. Results show that the phenotype predictions were not significantly sensitive to the metabolic models, although the GEM iAF1260 was more accurate in the prediction of central carbon fluxes at low dilution rates. Furthermore, we observed that the choice of the appropriate simulation method of mutant phenotypes depends on the biological question to be addressed. In terms of the two modeling approaches, none outperformed the other for all the tested scenarios. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1344-1354, 2018.
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Affiliation(s)
- Rafael S Costa
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal.,INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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