1
|
Kawai R, Toya Y, Shimizu H. Metabolic pathway design for growth-associated phenylalanine production using synthetically designed mutualism. Bioprocess Biosyst Eng 2022; 45:1539-1546. [PMID: 35930086 DOI: 10.1007/s00449-022-02762-4] [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: 04/22/2022] [Accepted: 07/21/2022] [Indexed: 11/02/2022]
Abstract
Combination of growth-associated pathway engineering based on flux balance analysis (FBA) and adaptive laboratory evolution (ALE) is a powerful approach to enhance the production of useful compounds. However, the feasibility of such growth-associated pathway designs depends on the type of target compound. In the present study, FBA predicted a set of gene deletions (pykA, pykF, ppc, zwf, and adhE) that leads to growth-associated phenylalanine production in Escherichia coli. The knockout strain is theoretically enforced to produce phenylalanine only at high growth yields, and could not be applied to the ALE experiment because of a severe growth defect. To overcome this challenge, we propose a novel approach for ALE based on mutualistic co-culture for coupling growth and production, regardless of the growth rate. We designed a synthetic mutualism of a phenylalanine-producing leucine-auxotrophic strain (KF strain) and a leucine-producing phenylalanine-auxotrophic strain (KL strain) and performed an ALE experiment for approximately 160 generations. The evolved KF strain (KF-E strain) grew in a synthetic medium (with glucose as the main carbon source) supplemented with leucine, while severe growth defects were observed in the parental KF strain. The phenylalanine yield of the KF-E strain was 2.3 times higher than that of the KF strain.
Collapse
Affiliation(s)
- Ryutaro Kawai
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| |
Collapse
|
2
|
Keller P, Noor E, Meyer F, Reiter MA, Anastassov S, Kiefer P, Vorholt JA. Methanol-dependent Escherichia coli strains with a complete ribulose monophosphate cycle. Nat Commun 2020; 11:5403. [PMID: 33106470 PMCID: PMC7588473 DOI: 10.1038/s41467-020-19235-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022] Open
Abstract
Methanol is a biotechnologically promising substitute for food and feed substrates since it can be produced renewably from electricity, water and CO2. Although progress has been made towards establishing Escherichia coli as a platform organism for methanol conversion via the energy efficient ribulose monophosphate (RuMP) cycle, engineering strains that rely solely on methanol as a carbon source remains challenging. Here, we apply flux balance analysis to comprehensively identify methanol-dependent strains with high potential for adaptive laboratory evolution. We further investigate two out of 1200 candidate strains, one with a deletion of fructose-1,6-bisphosphatase (fbp) and another with triosephosphate isomerase (tpiA) deleted. In contrast to previous reported methanol-dependent strains, both feature a complete RuMP cycle and incorporate methanol to a high degree, with up to 31 and 99% fractional incorporation into RuMP cycle metabolites. These strains represent ideal starting points for evolution towards a fully methylotrophic lifestyle.
Collapse
Affiliation(s)
- Philipp Keller
- Institute of Microbiology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Elad Noor
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Fabian Meyer
- Institute of Microbiology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Michael A Reiter
- Institute of Microbiology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Stanislav Anastassov
- Institute of Microbiology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Patrick Kiefer
- Institute of Microbiology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Julia A Vorholt
- Institute of Microbiology, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland.
| |
Collapse
|
3
|
van Tatenhove-Pel RJ, Hernandez-Valdes JA, Teusink B, Kuipers OP, Fischlechner M, Bachmann H. Microdroplet screening and selection for improved microbial production of extracellular compounds. Curr Opin Biotechnol 2020; 61:72-81. [DOI: 10.1016/j.copbio.2019.10.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 11/26/2022]
|
4
|
Aslan S, Noor E, Benito Vaquerizo S, Lindner SN, Bar-Even A. Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate. Metab Eng 2019; 57:96-109. [PMID: 31491545 DOI: 10.1016/j.ymben.2019.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 08/05/2019] [Accepted: 09/02/2019] [Indexed: 11/16/2022]
Abstract
Microbial biosensors are used to detect the presence of compounds provided externally or produced internally. The latter case is commonly constrained by the need to screen a large library of enzyme or pathway variants to identify those that can efficiently generate the desired compound. To address this limitation, we suggest the use of metabolic sensor strains which can grow only if the relevant compound is present and thus replace screening with direct selection. We used a computational platform to design metabolic sensor strains with varying dependencies on a specific compound. Our method systematically explores combinations of gene deletions and identifies how the growth requirement for a compound changes with the media composition. We demonstrate this approach by constructing a set of E. coli glycerate sensor strains. In each of these strains a different set of enzymes is disrupted such that central metabolism is effectively dissected into multiple segments, each requiring a dedicated carbon source. We find an almost perfect match between the predicted and experimental dependence on glycerate and show that the strains can be used to accurately detect glycerate concentrations across two orders of magnitude. Apart from demonstrating the potential application of metabolic sensor strains, our work reveals key phenomena in central metabolism, including spontaneous degradation of central metabolites and the importance of metabolic sinks for balancing small metabolic networks.
Collapse
Affiliation(s)
- Selçuk Aslan
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Elad Noor
- Institute of Molecular Systems Biology, ETH Zürich, Otto-Stern-Weg 3, 8093, Zürich, Switzerland
| | - Sara Benito Vaquerizo
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Arren Bar-Even
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany.
| |
Collapse
|
5
|
Saleski TE, Kerner AR, Chung MT, Jackman CM, Khasbaatar A, Kurabayashi K, Lin XN. Syntrophic co-culture amplification of production phenotype for high-throughput screening of microbial strain libraries. Metab Eng 2019; 54:232-243. [PMID: 31034921 DOI: 10.1016/j.ymben.2019.04.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 04/17/2019] [Accepted: 04/17/2019] [Indexed: 12/12/2022]
Abstract
Microbes can be engineered to synthesize a wide array of bioproducts, yet production phenotype evaluation remains a frequent bottleneck in the design-build-test cycle where strain development requires iterative rounds of library construction and testing. Here, we present Syntrophic Co-culture Amplification of Production phenotype (SnoCAP). Through a metabolic cross-feeding circuit, the production level of a target molecule is translated into highly distinguishable co-culture growth characteristics, which amplifies differences in production into highly distinguishable growth phenotypes. We demonstrate SnoCAP with the screening of Escherichia coli strains for production of two target molecules: 2-ketoisovalerate, a precursor of the drop-in biofuel isobutanol, and L-tryptophan. The dynamic range of the screening can be tuned by employing an inhibitory analog of the target molecule. Screening based on this framework requires compartmentalization of individual producers with the sensor strain. We explore three formats of implementation with increasing throughput capability: confinement in microtiter plates (102-104 assays/experiment), spatial separation on agar plates (104-105 assays/experiment), and encapsulation in microdroplets (105-107 assays/experiment). Using SnoCAP, we identified an efficient isobutanol production strain from a random mutagenesis library, reaching a final titer that is 5-fold higher than that of the parent strain. The framework can also be extended to screening for secondary metabolite production using a push-pull strategy. We expect that SnoCAP can be readily adapted to the screening of various microbial species, to improve production of a wide range of target molecules.
Collapse
Affiliation(s)
- Tatyana E Saleski
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Alissa R Kerner
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Meng Ting Chung
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Corine M Jackman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Azzaya Khasbaatar
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Katsuo Kurabayashi
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Xiaoxia Nina Lin
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
6
|
Chen X, Gao C, Guo L, Hu G, Luo Q, Liu J, Nielsen J, Chen J, Liu L. DCEO Biotechnology: Tools To Design, Construct, Evaluate, and Optimize the Metabolic Pathway for Biosynthesis of Chemicals. Chem Rev 2017; 118:4-72. [DOI: 10.1021/acs.chemrev.6b00804] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Xiulai Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Cong Gao
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liang Guo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guipeng Hu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Qiuling Luo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jia Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jens Nielsen
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Novo
Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Lyngby, Denmark
| | - Jian Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liming Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| |
Collapse
|
7
|
Boock JT, Gupta A, Prather KLJ. Screening and modular design for metabolic pathway optimization. Curr Opin Biotechnol 2015; 36:189-98. [DOI: 10.1016/j.copbio.2015.08.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 08/07/2015] [Accepted: 08/10/2015] [Indexed: 11/26/2022]
|
8
|
O'Brien EJ, Monk JM, Palsson BO. Using Genome-scale Models to Predict Biological Capabilities. Cell 2015; 161:971-987. [PMID: 26000478 DOI: 10.1016/j.cell.2015.05.019] [Citation(s) in RCA: 438] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Indexed: 11/29/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.
Collapse
Affiliation(s)
- Edward J O'Brien
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of NanoEngineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark.
| |
Collapse
|
9
|
Liu W, Jiang R. Combinatorial and high-throughput screening approaches for strain engineering. Appl Microbiol Biotechnol 2015; 99:2093-104. [DOI: 10.1007/s00253-015-6400-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Revised: 01/09/2015] [Accepted: 01/10/2015] [Indexed: 12/31/2022]
|
10
|
D'Souza G, Waschina S, Pande S, Bohl K, Kaleta C, Kost C. LESS IS MORE: SELECTIVE ADVANTAGES CAN EXPLAIN THE PREVALENT LOSS OF BIOSYNTHETIC GENES IN BACTERIA. Evolution 2014; 68:2559-70. [DOI: 10.1111/evo.12468] [Citation(s) in RCA: 153] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 05/25/2014] [Indexed: 11/29/2022]
Affiliation(s)
- Glen D'Souza
- Experimental Ecology and Evolution Research Group; Department of Bioorganic Chemistry; Max Planck Institute for Chemical Ecology; 07745 Jena Germany
| | - Silvio Waschina
- Experimental Ecology and Evolution Research Group; Department of Bioorganic Chemistry; Max Planck Institute for Chemical Ecology; 07745 Jena Germany
- Research Group Theoretical Systems Biology; Friedrich Schiller University of Jena; 07743 Jena Germany
| | - Samay Pande
- Experimental Ecology and Evolution Research Group; Department of Bioorganic Chemistry; Max Planck Institute for Chemical Ecology; 07745 Jena Germany
| | - Katrin Bohl
- Research Group Theoretical Systems Biology; Friedrich Schiller University of Jena; 07743 Jena Germany
- Department of Bioinformatics; Friedrich Schiller University Jena; Jena Germany
| | - Christoph Kaleta
- Research Group Theoretical Systems Biology; Friedrich Schiller University of Jena; 07743 Jena Germany
| | - Christian Kost
- Experimental Ecology and Evolution Research Group; Department of Bioorganic Chemistry; Max Planck Institute for Chemical Ecology; 07745 Jena Germany
- Institute of Microbiology; Friedrich Schiller University Jena; Germany
| |
Collapse
|
11
|
Novel approach to engineer strains for simultaneous sugar utilization. Metab Eng 2013; 20:63-72. [DOI: 10.1016/j.ymben.2013.08.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Revised: 06/13/2013] [Accepted: 08/14/2013] [Indexed: 01/11/2023]
|
12
|
Bertels F, Merker H, Kost C. Design and characterization of auxotrophy-based amino acid biosensors. PLoS One 2012; 7:e41349. [PMID: 22829942 PMCID: PMC3400592 DOI: 10.1371/journal.pone.0041349] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2012] [Accepted: 06/20/2012] [Indexed: 01/20/2023] Open
Abstract
Efficient and inexpensive methods are required for the high-throughput quantification of amino acids in physiological fluids or microbial cell cultures. Here we develop an array of Escherichia coli biosensors to sensitively quantify eleven different amino acids. By using online databases, genes involved in amino acid biosynthesis were identified that - upon deletion - should render the corresponding mutant auxotrophic for one particular amino acid. This rational design strategy suggested genes involved in the biosynthesis of arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, threonine, tryptophan, and tyrosine as potential genetic targets. A detailed phenotypic characterization of the corresponding single-gene deletion mutants indeed confirmed that these strains could neither grow on a minimal medium lacking amino acids nor transform any other proteinogenic amino acid into the focal one. Site-specific integration of the egfp gene into the chromosome of each biosensor decreased the detection limit of the GFP-labeled cells by 30% relative to turbidometric measurements. Finally, using the biosensors to determine the amino acid concentration in the supernatants of two amino acid overproducing E. coli strains (i.e. ΔhisL and ΔtdcC) both turbidometrically and via GFP fluorescence emission and comparing the results to conventional HPLC measurements confirmed the utility of the developed biosensor system. Taken together, our study provides not only a genotypically and phenotypically well-characterized set of publicly available amino acid biosensors, but also demonstrates the feasibility of the rational design strategy used.
Collapse
Affiliation(s)
- Felix Bertels
- Research Group Experimental Ecology and Evolution, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Holger Merker
- Research Group Experimental Ecology and Evolution, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Christian Kost
- Research Group Experimental Ecology and Evolution, Max Planck Institute for Chemical Ecology, Jena, Germany
| |
Collapse
|
13
|
|
14
|
Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 2012; 10:291-305. [PMID: 22367118 DOI: 10.1038/nrmicro2737] [Citation(s) in RCA: 537] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype-phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour.
Collapse
Affiliation(s)
- Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0412, USA
| | | | | |
Collapse
|
15
|
Michener JK, Thodey K, Liang JC, Smolke CD. Applications of genetically-encoded biosensors for the construction and control of biosynthetic pathways. Metab Eng 2011; 14:212-22. [PMID: 21946159 DOI: 10.1016/j.ymben.2011.09.004] [Citation(s) in RCA: 112] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2011] [Revised: 08/10/2011] [Accepted: 09/09/2011] [Indexed: 01/01/2023]
Abstract
Cells are filled with biosensors, molecular systems that measure the state of the cell and respond by regulating host processes. In much the same way that an engineer would monitor a chemical reactor, the cell uses these sensors to monitor changing intracellular environments and produce consistent behavior despite the variable environment. While natural systems derive a clear benefit from pathway regulation, past research efforts in engineering cellular metabolism have focused on introducing new pathways and removing existing pathway regulation. Synthetic biology is a rapidly growing field that focuses on the development of new tools that support the design, construction, and optimization of biological systems. Recent advances have been made in the design of genetically-encoded biosensors and the application of this class of molecular tools for optimizing and regulating heterologous pathways. Biosensors to cellular metabolites can be taken directly from natural systems, engineered from natural sensors, or constructed entirely in vitro. When linked to reporters, such as antibiotic resistance markers, these metabolite sensors can be used to report on pathway productivity, allowing high-throughput screening for pathway optimization. Future directions will focus on the application of biosensors to introduce feedback control into metabolic pathways, providing dynamic control strategies to increase the efficient use of cellular resources and pathway reliability.
Collapse
Affiliation(s)
- Joshua K Michener
- Department of Bioengineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | | | | |
Collapse
|
16
|
Zhang F, Keasling J. Biosensors and their applications in microbial metabolic engineering. Trends Microbiol 2011; 19:323-9. [PMID: 21664818 DOI: 10.1016/j.tim.2011.05.003] [Citation(s) in RCA: 144] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Revised: 05/05/2011] [Accepted: 05/06/2011] [Indexed: 01/30/2023]
Abstract
Many metabolic pathways in microbial hosts have been created, modified and engineered to produce useful molecules. The titer and yield of a final compound is often limited by the inefficient use of cellular resources and imbalanced metabolism. Engineering sensory-regulation devices that regulate pathway gene expression in response to the environment and metabolic status of the cell have great potential to solve these problems, and enhance product titers and yields. This review will focus on recent developments in biosensor design, and their applications for controlling microbial behavior.
Collapse
Affiliation(s)
- Fuzhong Zhang
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA
| | | |
Collapse
|