1
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Li G, Liu L, Du W, Cao H. Local flux coordination and global gene expression regulation in metabolic modeling. Nat Commun 2023; 14:5700. [PMID: 37709734 PMCID: PMC10502109 DOI: 10.1038/s41467-023-41392-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.
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Affiliation(s)
- Gaoyang Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Li Liu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Huansheng Cao
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China.
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2
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Merzbacher C, Mac Aodha O, Oyarzún DA. Bayesian Optimization for Design of Multiscale Biological Circuits. ACS Synth Biol 2023. [PMID: 37339382 PMCID: PMC10367132 DOI: 10.1021/acssynbio.3c00120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.
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Affiliation(s)
| | - Oisin Mac Aodha
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London NW1 2DB, U.K
| | - Diego A Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London NW1 2DB, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, U.K
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3
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Zhou GJ, Zhang F. Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors. BIOSENSORS 2023; 13:428. [PMID: 37185503 PMCID: PMC10136082 DOI: 10.3390/bios13040428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 05/17/2023]
Abstract
Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.
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Affiliation(s)
- Gloria J. Zhou
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA;
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA;
- Division of Biology & Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
- Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
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4
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Mu X, Zhang F. Diverse mechanisms of bioproduction heterogeneity in fermentation and their control strategies. J Ind Microbiol Biotechnol 2023; 50:kuad033. [PMID: 37791393 PMCID: PMC10583207 DOI: 10.1093/jimb/kuad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023]
Abstract
Microbial bioproduction often faces challenges related to populational heterogeneity, where cells exhibit varying biosynthesis capabilities. Bioproduction heterogeneity can stem from genetic and non-genetic factors, resulting in decreased titer, yield, stability, and reproducibility. Consequently, understanding and controlling bioproduction heterogeneity are crucial for enhancing the economic competitiveness of large-scale biomanufacturing. In this review, we provide a comprehensive overview of current understandings of the various mechanisms underlying bioproduction heterogeneity. Additionally, we examine common strategies for controlling bioproduction heterogeneity based on these mechanisms. By implementing more robust measures to mitigate heterogeneity, we anticipate substantial enhancements in the scalability and stability of bioproduction processes. ONE-SENTENCE SUMMARY This review summarizes current understandings of different mechanisms of bioproduction heterogeneity and common control strategies based on these mechanisms.
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Affiliation(s)
- Xinyue Mu
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Division of Biological & Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
- Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
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5
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Hartline CJ, Zhang F. The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors. ACS Synth Biol 2022; 11:2247-2258. [PMID: 35700119 PMCID: PMC9994378 DOI: 10.1021/acssynbio.2c00143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.
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Affiliation(s)
- Christopher J Hartline
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States.,Division of Biology & Biomedical Sciences, Washington University in St. Louis, Saint Louis, Missouri 63130, United States.,Institute of Materials Science & Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States
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6
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Hartline CJ, Zhang R, Zhang F. Transient Antibiotic Tolerance Triggered by Nutrient Shifts From Gluconeogenic Carbon Sources to Fatty Acid. Front Microbiol 2022; 13:854272. [PMID: 35359720 PMCID: PMC8963472 DOI: 10.3389/fmicb.2022.854272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 02/22/2022] [Indexed: 12/04/2022] Open
Abstract
Nutrient shifts from glycolytic-to-gluconeogenic carbon sources can create large sub-populations of extremely antibiotic tolerant bacteria, called persisters. Positive feedback in Escherichia coli central metabolism was believed to play a key role in the formation of persister cells. To examine whether positive feedback in nutrient transport can also support high persistence to β-lactams, we performed nutrient shifts for E. coli from gluconeogenic carbon sources to fatty acid (FA). We observed tri-phasic antibiotic killing kinetics characterized by a transient period of high antibiotic tolerance, followed by rapid killing then a slower persister-killing phase. The duration of transient tolerance (3-44 h) varies with pre-shift carbon source and correlates strongly with the time needed to accumulate the FA degradation enzyme FadD after the shift. Additionally, FadD accumulation time and thus transient tolerance time can be reduced by induction of the glyoxylate bypass prior to switching, highlighting that two interacting feedback loops simultaneously control the length of transient tolerance. Our results demonstrate that nutrient switches along with positive feedback are not sufficient to trigger persistence in a majority of the population but instead triggers only a temporary tolerance. Additionally, our results demonstrate that the pre-shift metabolic state determines the duration of transient tolerance and that supplying glyoxylate can facilitate antibiotic killing of bacteria.
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Affiliation(s)
- Christopher J. Hartline
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, United States
| | - Ruixue Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, United States
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, United States
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, Saint Louis, MO, United States
- Institute of Materials Science and Engineering, Washington University in St. Louis, Saint Louis, MO, United States
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7
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Mannan AA, Bates DG. Designing an irreversible metabolic switch for scalable induction of microbial chemical production. Nat Commun 2021; 12:3419. [PMID: 34103495 PMCID: PMC8187666 DOI: 10.1038/s41467-021-23606-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 05/07/2021] [Indexed: 01/05/2023] Open
Abstract
Bacteria can be harnessed to synthesise high-value chemicals. A promising strategy for increasing productivity uses inducible control systems to switch metabolism from growth to chemical synthesis once a large population of cell factories are generated. However, use of expensive chemical inducers limits scalability of this approach for biotechnological applications. Switching using cheap nutrients is an appealing alternative, but their tightly regulated uptake and consumption again limits scalability. Here, using mathematical models of fatty acid uptake in E. coli as an exemplary case study, we unravel how the cell's native regulation and program of induction can be engineered to minimise inducer usage. We show that integrating positive feedback loops into the circuitry creates an irreversible metabolic switch, which, requiring only temporary induction, drastically reduces inducer usage. Our proposed switch should be widely applicable, irrespective of the product of interest, and brings closer the realization of scalable and sustainable microbial chemical production.
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Affiliation(s)
- Ahmad A Mannan
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Declan G Bates
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom.
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8
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Dusad V, Thiel D, Barahona M, Keun HC, Oyarzún DA. Opportunities at the Interface of Network Science and Metabolic Modeling. Front Bioeng Biotechnol 2021; 8:591049. [PMID: 33569373 PMCID: PMC7868444 DOI: 10.3389/fbioe.2020.591049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
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Affiliation(s)
- Varshit Dusad
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Denise Thiel
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Hector C. Keun
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Diego A. Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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9
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Abstract
Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.
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10
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Hartline CJ, Schmitz AC, Han Y, Zhang F. Dynamic control in metabolic engineering: Theories, tools, and applications. Metab Eng 2021; 63:126-140. [PMID: 32927059 PMCID: PMC8015268 DOI: 10.1016/j.ymben.2020.08.015] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/15/2020] [Accepted: 08/26/2020] [Indexed: 12/12/2022]
Abstract
Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.
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Affiliation(s)
- Christopher J Hartline
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA
| | - Alexander C Schmitz
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA
| | - Yichao Han
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA; Division of Biological & Biomedical Sciences, Washington University in St. Louis, Saint Louis, MO, 63130, USA; Institute of Materials Science & Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA.
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11
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Cronan JE. The Escherichia coli FadR transcription factor: Too much of a good thing? Mol Microbiol 2020; 115:1080-1085. [PMID: 33283913 DOI: 10.1111/mmi.14663] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 11/29/2020] [Accepted: 12/01/2020] [Indexed: 12/19/2022]
Abstract
Escherichia coli FadR is a transcription factor regulated by acyl-CoA thioester binding that optimizes fatty acid (FA) metabolism in response to environmental FAs. FadR represses the fad genes of FA degradation (β-oxidation) and activates the fab genes of FA synthesis thereby allowing E. coli to have its cake (acyl chains for phospholipid synthesis) and eat it (degrade acyl chains to acetyl-CoA). Acyl-CoA binding of FadR derepresses the transcription of the fad genes and cancels fab gene transcriptional activation. Activation of fab genes was thought restricted to the fabA and fabB genes of unsaturated FA synthesis, but FadR overproduction markedly increases yields of all FA acyl chains. Subsequently, almost all of the remaining fab genes were shown to be transcriptionally activated by FadR binding, but binding was very weak. Why are the low-affinity sites retained? What effects on cell physiology would result from their conversion to high-affinity sites (thereby mimicking FadR overproduction)? Investigations of E. coli cell size determinants showed that FA synthesis primarily determines E. coli cell size. Upon modest induction of FadR, cell size increases, but at the cost of growth rate and accumulation of intracellular membranes. Greater induction resulted in further growth rate decreases and abnormal cells. Hence, too much FadR is bad. FadR is extraordinarily conserved in γ-proteobacteria but has migrated. Mycobacterium tuberculosis encodes FadR orthologs one of which is functional in E. coli. Strikingly, the FadR theme of acyl-CoA-dependent transcriptional regulation is found in a different transcription factor family where two Bacillus species plus bacterial and archaeal thermophiles contain related proteins of similar function.
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Affiliation(s)
- John E Cronan
- Departments of Microbiology and Biochemistry, University of Illinois, Urbana, IL, USA
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12
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Lv Y, Gu Y, Xu J, Zhou J, Xu P. Coupling metabolic addiction with negative autoregulation to improve strain stability and pathway yield. Metab Eng 2020; 61:79-88. [PMID: 32445959 PMCID: PMC7510839 DOI: 10.1016/j.ymben.2020.05.005] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 05/14/2020] [Indexed: 11/23/2022]
Abstract
Metabolic addiction, an organism that is metabolically addicted with a compound to maintain its growth fitness, is an underexplored area in metabolic engineering. Microbes with heavily engineered pathways or genetic circuits tend to experience metabolic burden leading to degenerated or abortive production phenotype during long-term cultivation or scale-up. A promising solution to combat metabolic instability is to tie up the end-product with an intermediary metabolite that is essential to the growth of the producing host. Here we present a simple strategy to improve both metabolic stability and pathway yield by coupling chemical addiction with negative autoregulatory genetic circuits. Naringenin and lipids compete for the same precursor malonyl-CoA with inversed pathway yield in oleaginous yeast. Negative autoregulation of the lipogenic pathways, enabled by CRISPRi and fatty acid-inducible promoters, repartitions malonyl-CoA to favor flavonoid synthesis and increased naringenin production by 74.8%. With flavonoid-sensing transcriptional activator FdeR and yeast hybrid promoters to control leucine synthesis and cell grwoth fitness, this amino acid feedforward metabolic circuit confers a flavonoid addiction phenotype that selectively enrich the naringenin-producing pupulation in the leucine auxotrophic yeast. The engineered yeast persisted 90.9% of naringenin titer up to 324 generations. Cells without flavonoid addiction regained growth fitness but lost 94.5% of the naringenin titer after cell passage beyond 300 generations. Metabolic addiction and negative autoregulation may be generalized as basic tools to eliminate metabolic heterogeneity, improve strain stability and pathway yield in long-term and large-scale bioproduction.
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Affiliation(s)
- Yongkun Lv
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA; School of Chemical Engineering, Zhengzhou University, Zhengzhou, Henan, 450001, China; National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yang Gu
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA; National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Jingliang Xu
- School of Chemical Engineering, Zhengzhou University, Zhengzhou, Henan, 450001, China.
| | - Jingwen Zhou
- National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China.
| | - Peng Xu
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA.
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13
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Han Y, Zhang F. Control strategies to manage trade-offs during microbial production. Curr Opin Biotechnol 2020; 66:158-164. [PMID: 32810759 PMCID: PMC8021483 DOI: 10.1016/j.copbio.2020.07.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 07/04/2020] [Accepted: 07/05/2020] [Indexed: 12/31/2022]
Abstract
When engineering microbes to overproduce a target molecule, engineers face multiple layers of trade-offs to allocate limited cellular resources between the target pathway and native cellular systems. These trade-offs arise from limited free ribosomes during translation, competition for metabolic precursors, as well as the negative relationship between production and growth rate. To achieve high production performance, microbes need to spontaneously make decisions in the dynamic and heterogeneous fermentation environment. In this review, we discuss recent advances in microbial control strategies that are used to manage these trade-offs and to improve microbial production. This review focuses on design principles and compares different implementations, with the hope to provide guidelines to future microbial engineering.
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Affiliation(s)
- Yichao Han
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Division of Biological & Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA; Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.
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