1
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Hernández-Magaña A, Bensussen A, Martínez-García JC, Álvarez-Buylla ER. Engineering principles for rationally design therapeutic strategies against hepatocellular carcinoma. Front Mol Biosci 2024; 11:1404319. [PMID: 38939509 PMCID: PMC11208463 DOI: 10.3389/fmolb.2024.1404319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/23/2024] [Indexed: 06/29/2024] Open
Abstract
The search for new therapeutic strategies against cancer has favored the emergence of rationally designed treatments. These treatments have focused on attacking cell plasticity mechanisms to block the transformation of epithelial cells into cancerous cells. The aim of these approaches was to control particularly lethal cancers such as hepatocellular carcinoma. However, they have not been able to control the progression of cancer for unknown reasons. Facing this scenario, emerging areas such as systems biology propose using engineering principles to design and optimize cancer treatments. Beyond the possibilities that this approach might offer, it is necessary to know whether its implementation at a clinical level is viable or not. Therefore, in this paper, we will review the engineering principles that could be applied to rationally design strategies against hepatocellular carcinoma, and discuss whether the necessary elements exist to implement them. In particular, we will emphasize whether these engineering principles could be applied to fight hepatocellular carcinoma.
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Affiliation(s)
| | - Antonio Bensussen
- Departamento de Control Automático, Cinvestav-IPN, Ciudad de México, Mexico
| | | | - Elena R. Álvarez-Buylla
- Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, Mexico
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2
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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3
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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4
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Tan C, Xu P, Tao F. Harnessing Interactional Sensory Genes for Rationally Reprogramming Chaotic Metabolism. RESEARCH (WASHINGTON, D.C.) 2022; 2022:0017. [PMID: 39290971 PMCID: PMC11407584 DOI: 10.34133/research.0017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/07/2022] [Indexed: 09/19/2024]
Abstract
Rationally controlling cellular metabolism is of great importance but challenging owing to its highly complex and chaotic nature. Natural existing sensory proteins like histidine kinases (HKs) are understood as "sensitive nodes" of biological networks that can trigger disruptive metabolic reprogramming (MRP) upon perceiving environmental fluctuation. Here, the "sensitive node" genes were adopted to devise a global MRP platform consisting of a CRISPR interference-mediated dual-gene combinational knockdown toolbox and survivorship-based metabolic interaction decoding algorithm. The platform allows users to decode the interfering effects of n × n gene pairs while only requiring the synthesis of n pairs of primers. A total of 35 HK genes and 24 glycine metabolic genes were selected as the targets to determine the effectiveness of our platform in a Vibrio sp. FA2. The platform was applied to decode the interfering impact of HKs on antibiotic resistance in strain FA2. A pattern of combined knockdown of HK genes (sasA_8 and 04288) was demonstrated to be capable of reducing antibiotic resistance of Vibrio by 108-fold. Patterns of combined knockdown of glycine pathway genes (e.g., gcvT and ltaE) and several HK genes (e.g., cpxA and btsS) were also revealed to increase glycine production. Our platform may enable an efficient and rational approach for global MRP based on the elucidation of high-order gene interactions. A web-based 1-stop service (https://smrp.sjtu.edu.cn) is also provided to simplify the implementation of this smart strategy in a broad range of cells.
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Affiliation(s)
- Chunlin Tan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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5
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Moškon M, Mraz M. Programmable evolution of computing circuits in cellular populations. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07532-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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6
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Foo M, Dony L, He F. Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network: A comparative analysis. Biosystems 2022; 219:104732. [PMID: 35781035 DOI: 10.1016/j.biosystems.2022.104732] [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: 02/25/2022] [Revised: 05/30/2022] [Accepted: 06/22/2022] [Indexed: 11/02/2022]
Abstract
Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback control circuits, an accurate model that is able to capture the vital dynamical behaviour of the pathogen-infected plant is required. In this study, using a data-driven modelling approach, we develop and compare four dynamical models (i.e. linear, Michaelis-Menten with Hill coefficient (Hill Function), standard S-System and extended S-System) of a pathogen-infected plant gene regulatory network (GRN). These models are then assessed across several criteria, i.e. ease of identifying the type of gene regulation, the predictive capability, Akaike Information Criterion (AIC) and the robustness to parameter uncertainty to determine its viability of balancing between biological complexity and accuracy when modelling the pathogen-infected plant GRN. Using our defined ranking score, we obtain the following insights to the modelling of GRN. Our analyses show that despite commonly used and provide biological relevance, the Hill Function model ranks the lowest while the extended S-System model ranks highest in the overall comparison. Interestingly, the performance of the linear model is more consistent throughout the comparison, making it the preferred model for this pathogen-infected plant GRN when considering data-driven modelling approach.
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Affiliation(s)
- Mathias Foo
- School of Engineering, University of Warwick, CV4 7AL, Coventry, UK.
| | - Leander Dony
- Institute of Computational Biology, Helmholtz Munich, 85764, Neuherberg, Germany; Department of Translational Psychiatry, Max Planck Institute of Psychiatry, International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany.
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, CV1 2JH, Coventry, UK.
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7
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Quantitative metabolic fluxes regulated by trans-omic networks. Biochem J 2022; 479:787-804. [PMID: 35356967 PMCID: PMC9022981 DOI: 10.1042/bcj20210596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 12/21/2022]
Abstract
Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
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8
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Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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9
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Sharp JA, Burrage K, Simpson MJ. Implementation and acceleration of optimal control for systems biology. J R Soc Interface 2021; 18:20210241. [PMID: 34428951 PMCID: PMC8385371 DOI: 10.1098/rsif.2021.0241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Optimal control theory provides insight into complex resource allocation decisions. The forward–backward sweep method (FBSM) is an iterative technique commonly implemented to solve two-point boundary value problems arising from the application of Pontryagin’s maximum principle (PMP) in optimal control. The FBSM is popular in systems biology as it scales well with system size and is straightforward to implement. In this review, we discuss the PMP approach to optimal control and the implementation of the FBSM. By conceptualizing the FBSM as a fixed point iteration process, we leverage and adapt existing acceleration techniques to improve its rate of convergence. We show that convergence improvement is attainable without prohibitively costly tuning of the acceleration techniques. Furthermore, we demonstrate that these methods can induce convergence where the underlying FBSM fails to converge. All code used in this work to implement the FBSM and acceleration techniques is available on GitHub at https://github.com/Jesse-Sharp/Sharp2021.
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Affiliation(s)
- Jesse A Sharp
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Kevin Burrage
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia.,Department of Computer Science, University of Oxford, Oxford OX2 6GG, UK
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
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10
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Wells PK, Smutok O, Melman A, Katz E. Switchable Biocatalytic Reactions Controlled by Interfacial pH Changes Produced by Orthogonal Biocatalytic Processes. ACS APPLIED MATERIALS & INTERFACES 2021; 13:33830-33839. [PMID: 34264645 DOI: 10.1021/acsami.1c07393] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Enzymes immobilized on a nano-structured surface were used to switch the activity of one enzyme by a local pH change produced by another enzyme. Immobilized amyloglucosidase (AMG) and trypsin were studied as examples of the pH-dependent switchable "target enzymes." The reactions catalyzed by co-immobilized urease or esterase were increasing or decreasing the local pH, respectively, thus operating as "actuator enzymes." Both kinds of the enzymes, producing local pH changes and changing biocatalytic activity with the pH variation, were orthogonal in terms of the biocatalytic reactions; however, their operation was coupled with the local pH produced near the surface with the immobilized enzymes. The "target enzymes" (AMG and trypsin) were changed reversibly between the active and inactive states by applying input signals (urea or ester, substrates for the urease or esterase operating as the "actuator enzymes") and washing them out with a new portion of the background solution. The developed approach can potentially lead to switchable operation of several enzymes, while some of them are inhibited when the others are activated upon receiving external signals processed by the "actuator enzymes." More complex systems with branched biocatalytic cascades can be controlled by orthogonal biocatalytic reactions activating selected pathways and changing the final output.
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Affiliation(s)
- Paulina K Wells
- Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, New York 13699, United States
| | - Oleh Smutok
- Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, New York 13699, United States
| | - Artem Melman
- Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, New York 13699, United States
| | - Evgeny Katz
- Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, New York 13699, United States
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11
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Szekeres K, Bollella P, Kim Y, Minko S, Melman A, Katz E. Magneto-Controlled Enzyme Activity with Locally Produced pH Changes. J Phys Chem Lett 2021; 12:2523-2527. [PMID: 33682408 DOI: 10.1021/acs.jpclett.1c00036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Biocatalytic activity of amyloglucosidase (AMG), immobilized on superparamagnetic nanoparticles, is dynamically and reversibly activated or inhibited by applying a magnetic field. The magnetic field triggers aggregation/deaggregation of magnetic particles that are also functionalized with urease or esterase enzymes. These enzymes produce a local pH change in the vicinity of the particles changing the AMG activity.
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Affiliation(s)
- Krisztina Szekeres
- Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, New York 13699, United States
- Electrochemistry and Electroanalytical Chemistry, Eötvös Loránd University, Budapest 1117, Hungary
| | - Paolo Bollella
- Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, New York 13699, United States
- Department of Chemistry, University of Bari A. Moro, Via E. Orabona 4, 70125 Bari, Italy
| | - Yongwook Kim
- Nanostructured Materials Lab, University of Georgia, Athens, Georgia 30602, United States
| | - Sergiy Minko
- Nanostructured Materials Lab, University of Georgia, Athens, Georgia 30602, United States
| | - Artem Melman
- Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, New York 13699, United States
| | - Evgeny Katz
- Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, New York 13699, United States
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12
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Wang T, Tague N, Whelan SA, Dunlop MJ. Programmable gene regulation for metabolic engineering using decoy transcription factor binding sites. Nucleic Acids Res 2021; 49:1163-1172. [PMID: 33367820 PMCID: PMC7826281 DOI: 10.1093/nar/gkaa1234] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 11/12/2020] [Accepted: 12/08/2020] [Indexed: 12/30/2022] Open
Abstract
Transcription factor decoy binding sites are short DNA sequences that can titrate a transcription factor away from its natural binding site, therefore regulating gene expression. In this study, we harness synthetic transcription factor decoy systems to regulate gene expression for metabolic pathways in Escherichia coli. We show that transcription factor decoys can effectively regulate expression of native and heterologous genes. Tunability of the decoy can be engineered via changes in copy number or modifications to the DNA decoy site sequence. Using arginine biosynthesis as a showcase, we observed a 16-fold increase in arginine production when we introduced the decoy system to steer metabolic flux towards increased arginine biosynthesis, with negligible growth differences compared to the wild type strain. The decoy-based production strain retains high genetic integrity; in contrast to a gene knock-out approach where mutations were common, we detected no mutations in the production system using the decoy-based strain. We further show that transcription factor decoys are amenable to multiplexed library screening by demonstrating enhanced tolerance to pinene with a combinatorial decoy library. Our study shows that transcription factor decoy binding sites are a powerful and compact tool for metabolic engineering.
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Affiliation(s)
- Tiebin Wang
- Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA 02215, USA.,Biological Design Center, Boston University, Boston, MA 02215, USA
| | - Nathan Tague
- Biological Design Center, Boston University, Boston, MA 02215, USA.,Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | | | - Mary J Dunlop
- Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA 02215, USA.,Biological Design Center, Boston University, Boston, MA 02215, USA.,Biomedical Engineering, Boston University, Boston, MA 02215, USA
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13
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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: 79] [Impact Index Per Article: 26.3] [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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14
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N Kolodkin A, Sharma RP, Colangelo AM, Ignatenko A, Martorana F, Jennen D, Briedé JJ, Brady N, Barberis M, Mondeel TDGA, Papa M, Kumar V, Peters B, Skupin A, Alberghina L, Balling R, Westerhoff HV. ROS networks: designs, aging, Parkinson's disease and precision therapies. NPJ Syst Biol Appl 2020; 6:34. [PMID: 33106503 PMCID: PMC7589522 DOI: 10.1038/s41540-020-00150-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 08/28/2020] [Indexed: 12/11/2022] Open
Abstract
How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.
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Affiliation(s)
- Alexey N Kolodkin
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Raju Prasad Sharma
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
| | - Anna Maria Colangelo
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Andrew Ignatenko
- Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
| | - Francesca Martorana
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Jacco J Briedé
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Nathan Brady
- Department of Molecular Microbiology & Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Thierry D G A Mondeel
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Michele Papa
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Infrastructure for Systems Biology Europe (ISBE.IT), Naples, Italy
- Department of Mental and Physical Health, University of Campania-L. Vanvitelli, Napoli, Italia
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
- IISPV, Hospital Universitari Sant Joan de Reus, Universitat Rovira I Virgili, Reus, Spain
| | - Bernhard Peters
- Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lilia Alberghina
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Hans V Westerhoff
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
- Manchester Centre for Integrative Systems Biology, School for Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK.
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15
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Tsiantis N, Banga JR. Using optimal control to understand complex metabolic pathways. BMC Bioinformatics 2020; 21:472. [PMID: 33087041 PMCID: PMC7579911 DOI: 10.1186/s12859-020-03808-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 10/13/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria. RESULTS Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments. CONCLUSIONS We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.
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Affiliation(s)
- Nikolaos Tsiantis
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain
- Department of Chemical Engineering, University of Vigo, 36310 Vigo, Spain
| | - Julio R. Banga
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain
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16
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Bankefa OE, Oladeji SJ, Ayilara-Akande SO, Lasisi MM. Microbial redemption of "evil" days: a global appraisal to food security. Journal of Food Science and Technology 2020; 58:2041-2053. [PMID: 33967303 DOI: 10.1007/s13197-020-04725-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 08/07/2020] [Accepted: 08/13/2020] [Indexed: 11/29/2022]
Abstract
Without refute, a sustainable global food security can only be achieved when all folks have physical, social and economic access to safe, nutritious, and sufficient supply of food to meet their dietary needs and food preferences for healthy life. To this end, quest to achieve this dream has been on course since 1970s as evident by the establishment of a committee on food security in 1975 by the UN World Food Conference to oversee and make developmental difference in food security. Interestingly, 2019 Global Hunger Index revealed transition in global hunger from serious to moderate with 31% decline in global hunger since 2000, and hence depicting enhanced food security. Despite this achievement, many countries are still battling with hunger and under-nutrition. Moreover, if the ''zero hunger'' goal envisaged by World Food Program is to be actualized by 2030, then it is crucial to pool efforts toward the provision of suggestive approach(es) for mitigating global hunger and under-nutrition while averting the "evils days" of food scarcity, starvation, food borne illnesses, wastage, malnutrition and death. On this note, microorganisms have revolutionized from the era of only being known as food spoilers and disease-causing agents to useful resources with the capability to improve food supply, food safety and food production through bio-preservation, bio-based production, bio-fertilization among others. Therefore, the exploration of microbes in redeeming the "evils" associated with food insecurity cannot but be appraised. To this end, this review proposes optimization of different microbial processes as food security enhancing agents.
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Affiliation(s)
- Olufemi Emmanuel Bankefa
- Department of Microbiology, Federal University Oye-Ekiti, P.M.B. 373, Oye-Ekiti, Ekiti-State Nigeria
| | - Seye Julius Oladeji
- Department of Microbiology, Federal University Oye-Ekiti, P.M.B. 373, Oye-Ekiti, Ekiti-State Nigeria
| | | | - Modupe Mariam Lasisi
- Department of Microbiology, Federal University Oye-Ekiti, P.M.B. 373, Oye-Ekiti, Ekiti-State Nigeria
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17
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Volk MJ, Lourentzou I, Mishra S, Vo LT, Zhai C, Zhao H. Biosystems Design by Machine Learning. ACS Synth Biol 2020; 9:1514-1533. [PMID: 32485108 DOI: 10.1021/acssynbio.0c00129] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
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18
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VanArsdale E, Hörnström D, Sjöberg G, Järbur I, Pitzer J, Payne GF, van Maris AJA, Bentley WE. A Coculture Based Tyrosine-Tyrosinase Electrochemical Gene Circuit for Connecting Cellular Communication with Electronic Networks. ACS Synth Biol 2020; 9:1117-1128. [PMID: 32208720 DOI: 10.1021/acssynbio.9b00469] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
There is a growing interest in mediating information transfer between biology and electronics. By the addition of redox mediators to various samples and cells, one can both electronically obtain a redox "portrait" of a biological system and, conversely, program gene expression. Here, we have created a cell-based synthetic biology-electrochemical axis in which engineered cells process molecular cues, producing an output that can be directly recorded via electronics-but without the need for added redox mediators. The process is robust; two key components must act together to provide a valid signal. The system builds on the tyrosinase-mediated conversion of tyrosine to L-DOPA and L-DOPAquinone, which are both redox active. "Catalytic" transducer cells provide for signal-mediated surface expression of tyrosinase. Additionally, "reagent" transducer cells synthesize and export tyrosine, a substrate for tyrosinase. In cocultures, this system enables real-time electrochemical transduction of cell activating molecular cues. To demonstrate, we eavesdrop on quorum sensing signaling molecules that are secreted by Pseudomonas aeruginosa, N-(3-oxododecanoyl)-l-homoserine lactone and pyocyanin.
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Affiliation(s)
- Eric VanArsdale
- Fischell Department of Bioengineering, University of Maryland, 3102 A. James Clark Hall, 8278 Paint Branch Drive, College Park, Maryland 20742, United States
| | - David Hörnström
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, AlbaNova University Center, SE 10691 Stockholm, Sweden
| | - Gustav Sjöberg
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, AlbaNova University Center, SE 10691 Stockholm, Sweden
| | - Ida Järbur
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, AlbaNova University Center, SE 10691 Stockholm, Sweden
| | - Juliana Pitzer
- Fischell Department of Bioengineering, University of Maryland, 3102 A. James Clark Hall, 8278 Paint Branch Drive, College Park, Maryland 20742, United States
| | - Gregory F. Payne
- Fischell Department of Bioengineering, University of Maryland, 3102 A. James Clark Hall, 8278 Paint Branch Drive, College Park, Maryland 20742, United States
| | - Antonius J. A. van Maris
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, AlbaNova University Center, SE 10691 Stockholm, Sweden
| | - William E. Bentley
- Fischell Department of Bioengineering, University of Maryland, 3102 A. James Clark Hall, 8278 Paint Branch Drive, College Park, Maryland 20742, United States
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19
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Mahajan D, Sengupta S, Sen S. Strategies to improve microbial lipid production: Optimization techniques. BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2019. [DOI: 10.1016/j.bcab.2019.101321] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Sander T, Wang CY, Glatter T, Link H. CRISPRi-Based Downregulation of Transcriptional Feedback Improves Growth and Metabolism of Arginine Overproducing E. coli. ACS Synth Biol 2019; 8:1983-1990. [PMID: 31429546 DOI: 10.1021/acssynbio.9b00183] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Removing transcriptional feedback regulation of metabolic pathways is a classical approach to enhance overproduction of chemicals in microbes. However, disrupting transcriptional regulation can have broad physiological consequences that decrease cellular growth and productivity. Here, we compared downregulation and deletion of the transcriptional repressor ArgR in arginine overproducing Escherichia coli. Different levels of ArgR downregulation were achieved with CRISPR interference (CRISPRi) and resulted in 2-times higher growth rates compared to deletion of ArgR, while specific arginine production was similar (∼2 mmol gDW-1 h-1). Metabolomics and proteomics data revealed that poor growth of the ArgR deletion strain was caused by a limitation of pyrimidine nucleotide biosynthesis, because a 17-fold overexpression of ornithine carbamoyltransferase (ArgI) perturbed the arginine-pyrimidine branch point. These results demonstrate that overexpression of enzymes in an engineered pathway can impair metabolism of the host, especially in the case of branch-point enzymes. Thus, balancing enzyme levels is important to optimize industrial microbes, and CRISPRi of a transcription factor is a versatile tool for this purpose.
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Affiliation(s)
- Timur Sander
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Chun Ying Wang
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Timo Glatter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
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21
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He F, Stumpf MPH. Quantifying Dynamic Regulation in Metabolic Pathways with Nonparametric Flux Inference. Biophys J 2019; 116:2035-2046. [PMID: 31076100 DOI: 10.1016/j.bpj.2019.04.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 04/08/2019] [Indexed: 02/06/2023] Open
Abstract
One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis is a powerful tool to study this regulation at the metabolic, gene-expression, and signaling levels. It has been widely applied to study steady-state regulation, but analysis of the metabolic dynamics remains challenging because it is difficult to measure time-dependent metabolic flux. Here, we develop a nonparametric method that uses Gaussian processes to accurately infer the dynamics of a metabolic pathway based only on metabolite measurements; from this, we then go on to obtain a dynamical view of the hierarchical regulation processes invoked over time to control the activity in a pathway. Our approach allows us to use hierarchical regulation analysis in a dynamic setting but without the need for explicitly time-dependent flux measurements.
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Affiliation(s)
- Fei He
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom; School of Computing, Electronics, and Mathematics, Coventry University, Coventry, United Kingdom
| | - Michael P H Stumpf
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom; Melbourne Integrative Genomics, School of BioScience and School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.
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22
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Krishnan J, Floros I. Adaptive information processing of network modules to dynamic and spatial stimuli. BMC SYSTEMS BIOLOGY 2019; 13:32. [PMID: 30866946 PMCID: PMC6417070 DOI: 10.1186/s12918-019-0703-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 02/08/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND Adaptation and homeostasis are basic features of information processing in cells and seen in a broad range of contexts. Much of the current understanding of adaptation in network modules/motifs is based on their response to simple stimuli. Recently, there have also been studies of adaptation in dynamic stimuli. However a broader synthesis of how different circuits of adaptation function, and which circuits enable a broader adaptive behaviour in classes of more complex and spatial stimuli is largely missing. RESULTS We study the response of a variety of adaptive circuits to time-varying stimuli such as ramps, periodic stimuli and static and dynamic spatial stimuli. We find that a variety of responses can be seen in ramp stimuli, making this a basis for discriminating between even similar circuits. We also find that a number of circuits adapt exactly to ramp stimuli, and dissect these circuits to pinpoint what characteristics (architecture, feedback, biochemical aspects, information processing ingredients) allow for this. These circuits include incoherent feedforward motifs, inflow-outflow motifs and transcritical circuits. We find that changes in location in such circuits where a signal acts can result in non-adaptive behaviour in ramps, even though the location was associated with exact adaptation in step stimuli. We also demonstrate that certain augmentations of basic inflow-outflow motifs can alter the behaviour of the circuit from exact adaptation to non-adaptive behaviour. When subject to periodic stimuli, some circuits (inflow-outflow motifs and transcritical circuits) are able to maintain an average output independent of the characteristics of the input. We build on this to examine the response of adaptive circuits to static and dynamic spatial stimuli. We demonstrate how certain circuits can exhibit a graded response in spatial static stimuli with an exact maintenance of the spatial mean-value. Distinct features which emerge from the consideration of dynamic spatial stimuli are also discussed. Finally, we also build on these results to show how different circuits which show any combination of presence or absence of exact adaptation in ramps, exact mainenance of time average output in periodic stimuli and exact maintenance of spatial average of output in static spatial stimuli may be realized. CONCLUSIONS By studying a range of network circuits/motifs on one hand and a range of stimuli on the other, we isolate characteristics of these circuits (structural) which enable different degrees of exact adaptive and homeostatic behaviour in such stimuli, how they may be combined, and also identify cases associated with non-homeostatic behaviour. We also reveal constraints associated with locations where signals may act to enable homeostatic behaviour and constraints associated with augmentations of circuits. This consideration of multiple experimentally/naturally relevant stimuli along with circuits of adaptation of relevance in natural and engineered biology, provides a platform for deepening our understanding of adaptive and homeostatic behaviour in natural systems, bridging the gap between models of adaptation and experiments and in engineering homeostatic synthetic circuits.
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Affiliation(s)
- J Krishnan
- Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK.
| | - Ioannis Floros
- Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK.,National Centre of Scientific Research "Demokritos", Athens, Greece
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23
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Abstract
Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.
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Affiliation(s)
- Stephen G Oliver
- Department of Biochemistry, University of Cambridge, Cambridge, UK.
- Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.
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24
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Lalwani MA, Zhao EM, Avalos JL. Current and future modalities of dynamic control in metabolic engineering. Curr Opin Biotechnol 2018; 52:56-65. [DOI: 10.1016/j.copbio.2018.02.007] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Accepted: 02/11/2018] [Indexed: 12/30/2022]
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25
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Reznik E, Christodoulou D, Goldford JE, Briars E, Sauer U, Segrè D, Noor E. Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity. Cell Rep 2018; 20:2666-2677. [PMID: 28903046 DOI: 10.1016/j.celrep.2017.08.066] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 08/05/2017] [Accepted: 08/19/2017] [Indexed: 12/21/2022] Open
Abstract
Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.
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Affiliation(s)
- Ed Reznik
- Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA; Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Dimitris Christodoulou
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Systems Biology Graduate School, Zurich 8057, Switzerland
| | | | - Emma Briars
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Daniel Segrè
- Department of Biomedical Engineering, Boston University, Boston, MA, USA; Bioinformatics Program, Boston University, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA
| | - Elad Noor
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
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26
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Tsipa A, Koutinas M, Usaku C, Mantalaris A. Optimal bioprocess design through a gene regulatory network - Growth kinetic hybrid model: Towards replacing Monod kinetics. Metab Eng 2018; 48:129-137. [PMID: 29729316 DOI: 10.1016/j.ymben.2018.04.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 03/14/2018] [Accepted: 04/30/2018] [Indexed: 01/01/2023]
Abstract
Currently, design and optimisation of biotechnological bioprocesses is performed either through exhaustive experimentation and/or with the use of empirical, unstructured growth kinetics models. Whereas, elaborate systems biology approaches have been recently explored, mixed-substrate utilisation is predominantly ignored despite its significance in enhancing bioprocess performance. Herein, bioprocess optimisation for an industrially-relevant bioremediation process involving a mixture of highly toxic substrates, m-xylene and toluene, was achieved through application of a novel experimental-modelling gene regulatory network - growth kinetic (GRN-GK) hybrid framework. The GRN model described the TOL and ortho-cleavage pathways in Pseudomonas putida mt-2 and captured the transcriptional kinetics expression patterns of the promoters. The GRN model informed the formulation of the growth kinetics model replacing the empirical and unstructured Monod kinetics. The GRN-GK framework's predictive capability and potential as a systematic optimal bioprocess design tool, was demonstrated by effectively predicting bioprocess performance, which was in agreement with experimental values, when compared to four commonly used models that deviated significantly from the experimental values. Significantly, a fed-batch biodegradation process was designed and optimised through the model-based control of TOL Pr promoter expression resulting in 61% and 60% enhanced pollutant removal and biomass formation, respectively, compared to the batch process. This provides strong evidence of model-based bioprocess optimisation at the gene level, rendering the GRN-GK framework as a novel and applicable approach to optimal bioprocess design. Finally, model analysis using global sensitivity analysis (GSA) suggests an alternative, systematic approach for model-driven strain modification for synthetic biology and metabolic engineering applications.
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Affiliation(s)
- Argyro Tsipa
- Department of Chemical Engineering, South Kensington Campus, Imperial College London, London, United Kingdom
| | - Michalis Koutinas
- Department of Environmental Science and Technology, Cyprus University of Technology, 30 Archbishop Kuprianou Str., Limassol, Cyprus
| | - Chonlatep Usaku
- Department of Chemical Engineering, South Kensington Campus, Imperial College London, London, United Kingdom; Department of Biotechnology, Silpakorn University, Nakorn Pathom 73000, Thailand
| | - Athanasios Mantalaris
- Department of Chemical Engineering, South Kensington Campus, Imperial College London, London, United Kingdom.
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27
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Tsiantis N, Balsa-Canto E, Banga JR. Optimality and identification of dynamic models in systems biology: an inverse optimal control framework. Bioinformatics 2018. [DOI: 10.1093/bioinformatics/bty139] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Nikolaos Tsiantis
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
- Department of Chemical Engineering, University of Vigo Vigo, Spain
| | - Eva Balsa-Canto
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
| | - Julio R Banga
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
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28
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29
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Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. Biotechnol Adv 2017; 35:981-1003. [PMID: 28916392 DOI: 10.1016/j.biotechadv.2017.09.005] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 08/30/2017] [Accepted: 09/10/2017] [Indexed: 12/13/2022]
Abstract
Kinetic models are critical to predict the dynamic behaviour of metabolic networks. Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting their parameters. Recent modelling frameworks promise new ways to overcome this obstacle while retaining predictive capabilities. In this review, we present an overview of the relevant mathematical frameworks for kinetic formulation, construction and analysis. Starting with kinetic formalisms, we next review statistical methods for parameter inference, as well as recent computational frameworks applied to the construction and analysis of kinetic models. Finally, we discuss opportunities and limitations hindering the development of larger kinetic reconstructions.
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30
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Chen PW, Theisen MK, Liao JC. Metabolic systems modeling for cell factories improvement. Curr Opin Biotechnol 2017; 46:114-119. [PMID: 28388485 DOI: 10.1016/j.copbio.2017.02.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 02/10/2017] [Accepted: 02/13/2017] [Indexed: 12/23/2022]
Abstract
Techniques for modeling microbial bioproduction systems have evolved over many decades. Here, we survey recent literature and focus on modeling approaches for improving bioproduction. These techniques from systems biology are based on different methodologies, starting from stoichiometry only to various stoichiometry with kinetics approaches that address different issues in metabolic systems. Techniques to overcome unknown kinetic parameters using random sampling have emerged to address meaningful questions. Among those questions, pathway robustness seems to be an important issue for metabolic engineering. We also discuss the increasing significance of databases in biology and their potential impact for biotechnology.
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Affiliation(s)
- Po-Wei Chen
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - Matthew K Theisen
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - James C Liao
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States; Academia Sinica, Taipei 11529, Taiwan.
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31
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Martín-Jiménez CA, Salazar-Barreto D, Barreto GE, González J. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network. Front Aging Neurosci 2017; 9:23. [PMID: 28243200 PMCID: PMC5303712 DOI: 10.3389/fnagi.2017.00023] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 01/27/2017] [Indexed: 12/22/2022] Open
Abstract
Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.
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Affiliation(s)
- Cynthia A Martín-Jiménez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - Diego Salazar-Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - George E Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad JaverianaBogotá, Colombia; Instituto de Ciencias Biomédicas, Universidad Autónoma de ChileSantiago, Chile
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
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Del Vecchio D, Dy AJ, Qian Y. Control theory meets synthetic biology. J R Soc Interface 2016; 13:rsif.2016.0380. [PMID: 27440256 DOI: 10.1098/rsif.2016.0380] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 06/20/2016] [Indexed: 12/15/2022] Open
Abstract
The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology.
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Affiliation(s)
- Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Aaron J Dy
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yili Qian
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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