1
|
Mahanty B. Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges. Biotechnol Bioeng 2023; 120:2072-2091. [PMID: 37458311 DOI: 10.1002/bit.28503] [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: 05/16/2023] [Revised: 07/09/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.
Collapse
Affiliation(s)
- Biswanath Mahanty
- Department of Biotechnology, Krunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| |
Collapse
|
2
|
Briki A, Olmos E, Delaunay S, Fournier F. Generalized modelling of effect of oxygenation and glucose concentration on Corynebacterium glutamicum growth and production kinetics. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
3
|
Smith DJ, Helmy M, Lindley ND, Selvarajoo K. The transformation of our food system using cellular agriculture: What lies ahead and who will lead it? Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.04.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
4
|
Buldum G, Mantalaris A. Systematic Understanding of Recent Developments in Bacterial Cellulose Biosynthesis at Genetic, Bioprocess and Product Levels. Int J Mol Sci 2021; 22:ijms22137192. [PMID: 34281246 PMCID: PMC8268586 DOI: 10.3390/ijms22137192] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 06/28/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Engineering biological processes has become a standard approach to produce various commercially valuable chemicals, therapeutics, and biomaterials. Among these products, bacterial cellulose represents major advances to biomedical and healthcare applications. In comparison to properties of plant cellulose, bacterial cellulose (BC) shows distinctive characteristics such as a high purity, high water retention, and biocompatibility. However, low product yield and extensive cultivation times have been the main challenges in the large-scale production of BC. For decades, studies focused on optimization of cellulose production through modification of culturing strategies and conditions. With an increasing demand for BC, researchers are now exploring to improve BC production and functionality at different categories: genetic, bioprocess, and product levels as well as model driven approaches targeting each of these categories. This comprehensive review discusses the progress in BC platforms categorizing the most recent advancements under different research focuses and provides systematic understanding of the progress in BC biosynthesis. The aim of this review is to present the potential of ‘modern genetic engineering tools’ and ‘model-driven approaches’ on improving the yield of BC, altering the properties, and adding new functionality. We also provide insights for the future perspectives and potential approaches to promote BC use in biomedical applications.
Collapse
Affiliation(s)
- Gizem Buldum
- Department of Life Sciences, Imperial College London, London SW7 2AZ, UK;
| | - Athanasios Mantalaris
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence:
| |
Collapse
|
5
|
Bio-Electrochemical System Depollution Capabilities and Monitoring Applications: Models, Applicability, Advanced Bio-Based Concept for Predicting Pollutant Degradation and Microbial Growth Kinetics via Gene Regulation Modelling. Processes (Basel) 2021. [DOI: 10.3390/pr9061038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored.
Collapse
|
6
|
Miri S, Davoodi SM, Karimi Darvanjooghi MH, Brar SK, Rouissi T, Martel R. Precision modelling of co-metabolic biodegradation of recalcitrant aromatic hydrocarbons in conjunction with experimental data. Process Biochem 2021. [DOI: 10.1016/j.procbio.2021.03.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
7
|
Martin-Pascual M, Batianis C, Bruinsma L, Asin-Garcia E, Garcia-Morales L, Weusthuis RA, van Kranenburg R, Martins Dos Santos VAP. A navigation guide of synthetic biology tools for Pseudomonas putida. Biotechnol Adv 2021; 49:107732. [PMID: 33785373 DOI: 10.1016/j.biotechadv.2021.107732] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 03/12/2021] [Accepted: 03/18/2021] [Indexed: 12/12/2022]
Abstract
Pseudomonas putida is a microbial chassis of huge potential for industrial and environmental biotechnology, owing to its remarkable metabolic versatility and ability to sustain difficult redox reactions and operational stresses, among other attractive characteristics. A wealth of genetic and in silico tools have been developed to enable the unravelling of its physiology and improvement of its performance. However, the rise of this microbe as a promising platform for biotechnological applications has resulted in diversification of tools and methods rather than standardization and convergence. As a consequence, multiple tools for the same purpose have been generated, whilst most of them have not been embraced by the scientific community, which has led to compartmentalization and inefficient use of resources. Inspired by this and by the substantial increase in popularity of P. putida, we aim herein to bring together and assess all currently available (wet and dry) synthetic biology tools specific for this microbe, focusing on the last 5 years. We provide information on the principles, functionality, advantages and limitations, with special focus on their use in metabolic engineering. Additionally, we compare the tool portfolio for P. putida with those for other bacterial chassis and discuss potential future directions for tool development. Therefore, this review is intended as a reference guide for experts and new 'users' of this promising chassis.
Collapse
Affiliation(s)
- Maria Martin-Pascual
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Christos Batianis
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Lyon Bruinsma
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Enrique Asin-Garcia
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Luis Garcia-Morales
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Ruud A Weusthuis
- Bioprocess Engineering, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Richard van Kranenburg
- Corbion, Gorinchem 4206 AC, The Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen 6708 WE, the Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands; LifeGlimmer GmbH, Berlin 12163, Germany.
| |
Collapse
|
8
|
Hwang J, Hari A, Cheng R, Gardner JG, Lobo D. Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus. Biotechnol Bioeng 2020; 117:3876-3890. [PMID: 32833226 DOI: 10.1002/bit.27544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 07/11/2020] [Accepted: 08/19/2020] [Indexed: 12/12/2022]
Abstract
Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.
Collapse
Affiliation(s)
- Jeanice Hwang
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Raymond Cheng
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Jeffrey G Gardner
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| |
Collapse
|
9
|
A dual-parameter identification approach for data-based predictive modeling of hybrid gene regulatory network-growth kinetics in Pseudomonas putida mt-2. Bioprocess Biosyst Eng 2020; 43:1671-1688. [PMID: 32377941 DOI: 10.1007/s00449-020-02360-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 04/21/2020] [Indexed: 10/24/2022]
Abstract
Data integration to model-based description of biological systems incorporating gene dynamics improves the performance of microbial systems. Bioprocess performance, typically predicted using empirical Monod-type models, is essential for a sustainable bioeconomy. To replace empirical models, we updated a hybrid gene regulatory network-growth kinetic model, predicting aromatic pollutants degradation and biomass growth in Pseudomonas putida mt-2. We modeled a complex biological system including extensive information to understand the role of the regulatory elements in toluene biodegradation and biomass growth. The updated model exhibited extra complications such as the existence of oscillations and discontinuities. As parameter estimation of complex biological models remains a key challenge, we used the updated model to present a dual-parameter identification approach (the 'dual approach') combining two independent methodologies. Approach I handled the complexity by incorporation of demonstrated biological knowledge in the model-development process and combination of global sensitivity analysis and optimisation. Approach II complemented Approach I handling multimodality, ill-conditioning and overfitting through regularisation estimation, global optimisation, and identifiability analysis. To systematically quantify the biological system, we used a vast amount of high-quality time-course data. The dual approach resulted in an accurately calibrated kinetic model (NRMSE: 0.17055) efficiently handling the additional model complexity. We tested model validation using three independent experimental data sets, achieving greater predictive power (NRMSE: 0.18776) than the individual approaches (NRMSE I: 0.25322, II: 0.25227) and increasing model robustness. These results demonstrated data-driven predictive modeling potentially leading to bioprocess' model-based control and optimisation.
Collapse
|
10
|
Parmaki S, Tsipa A, Vasquez MI, Gonçalves JMJ, Hadjiadamou I, Ferreira FC, Afonso CAM, Drouza C, Koutinas M. Resolution of alkaloid racemate: a novel microbial approach for the production of enantiopure lupanine via industrial wastewater valorization. Microb Cell Fact 2020; 19:67. [PMID: 32169079 PMCID: PMC7071741 DOI: 10.1186/s12934-020-01324-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 03/04/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Lupanine is a plant toxin contained in the wastewater of lupine bean processing industries, which could be used for semi-synthesis of various novel high added-value compounds. This paper introduces an environmental friendly process for microbial production of enantiopure lupanine. RESULTS Previously isolated P. putida LPK411, R. rhodochrous LPK211 and Rhodococcus sp. LPK311, holding the capacity to utilize lupanine as single carbon source, were employed as biocatalysts for resolution of racemic lupanine. All strains achieved high enantiomeric excess (ee) of L-(-)-lupanine (> 95%), while with the use of LPK411 53% of the initial racemate content was not removed. LPK411 fed with lupanine enantiomers as single substrates achieved 92% of D-(+)-lupanine biodegradation, whereas L-(-)-lupanine was not metabolized. Monitoring the transcriptional kinetics of the luh gene in cultures supplemented with the racemate as well as each of the enantiomers supported the enantioselectivity of LPK411 for D-(+)-lupanine biotransformation, while (trans)-6-oxooctahydro-1H-quinolizine-3-carboxylic acid was detected as final biodegradation product from D-(+)-lupanine use. Ecotoxicological assessment demonstrated that lupanine enantiomers were less toxic to A. fischeri compared to the racemate exhibiting synergistic interaction. CONCLUSIONS The biological chiral separation process of lupanine presented here constitutes an eco-friendly and low-cost alternative to widely used chemical methods for chiral separation.
Collapse
Affiliation(s)
- Stella Parmaki
- Department of Chemical Engineering, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Limassol, Cyprus
| | - Argyro Tsipa
- Civil and Environmental Engineering, University of Cyprus, 75 Kallipoleos Str., 1678, Nicosia, Cyprus
| | - Marlen I Vasquez
- Department of Chemical Engineering, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Limassol, Cyprus
| | - João M J Gonçalves
- Research Institute for Medicines (iMed. ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003, Lisbon, Portugal
| | - Ioanna Hadjiadamou
- Department of Chemistry, University of Cyprus, P.O. Box 20537, 1678, Nicosia, Cyprus.,Department of Agricultural Sciences, Biotechnology and Food Science, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Limassol, Cyprus
| | - Frederico C Ferreira
- Institute for Bioengineering and Biosciences, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal
| | - Carlos A M Afonso
- Research Institute for Medicines (iMed. ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003, Lisbon, Portugal
| | - Chrysoulla Drouza
- Department of Agricultural Sciences, Biotechnology and Food Science, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Limassol, Cyprus
| | - Michalis Koutinas
- Department of Chemical Engineering, Cyprus University of Technology, 30 Archbishop Kyprianou Str., 3036, Limassol, Cyprus.
| |
Collapse
|
11
|
Bridging substrate intake kinetics and bacterial growth phenotypes with flux balance analysis incorporating proteome allocation. Sci Rep 2020; 10:4283. [PMID: 32152336 PMCID: PMC7062752 DOI: 10.1038/s41598-020-61174-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Accepted: 02/24/2020] [Indexed: 11/08/2022] Open
Abstract
Empirical kinetic models such as the Monod equation have been widely applied to relate the cell growth with substrate availability. The Monod equation shares a similar form with the mechanistically-based Michaelis-Menten kinetics for enzymatic processes, which has provoked long-standing and un-concluded conjectures on their relationship. In this work, we integrated proteome allocation principles into a Flux Balance Analysis (FBA) model of Escherichia coli, which quantitatively revealed potential mechanisms that underpin the phenomenological Monod parameters: the maximum specific growth rate could be dictated by the abundance of growth-controlling proteome and growth-pertinent proteome cost; more importantly, the Monod constant (Ks) was shown to relate to the Michaelis constant for substrate transport (Km,g), with the link being dependent on the cell's metabolic strategy. Besides, the proposed model was able to predict glucose uptake rate at given external glucose concentration through the size of available proteome resource for substrate transport and its enzymatic cost, while growth rate and acetate overflow were accurately simulated for two E. coli strains. Bridging the enzymatic kinetics of substrate intake and overall growth phenotypes, this work offers a mechanistic interpretation to the empirical Monod law, and demonstrates the potential of coupling local and global cellular constrains in predictive modelling.
Collapse
|
12
|
Buldum G, Tsipa A, Mantalaris A. Linking Engineered Gene Circuit Kinetic Modeling to Cellulose Biosynthesis Prediction in Escherichia coli: Toward Bioprocessing of Microbial Cell Factories. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b05847] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Gizem Buldum
- Biological Systems Engineering Laboratory (BSEL), Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Argyro Tsipa
- Biological Systems Engineering Laboratory (BSEL), Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Athanasios Mantalaris
- Biological Systems Engineering Laboratory (BSEL), Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30322, United States
| |
Collapse
|