1
|
Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
Collapse
Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
| |
Collapse
|
2
|
Rao X, Barros J. Modeling lignin biosynthesis: a pathway to renewable chemicals. TRENDS IN PLANT SCIENCE 2024; 29:546-559. [PMID: 37802691 DOI: 10.1016/j.tplants.2023.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/01/2023] [Accepted: 09/18/2023] [Indexed: 10/08/2023]
Abstract
Plant biomass contains lignin that can be converted into high-value-added chemicals, fuels, and materials. The precise genetic manipulation of lignin content and composition in plant cells offers substantial environmental and economic benefits. However, the intricate regulatory mechanisms governing lignin formation challenge the development of crops with specific lignin profiles. Mathematical models and computational simulations have recently been employed to gain fundamental understanding of the metabolism of lignin and related phenolic compounds. This review article discusses the strategies used for modeling plant metabolic networks, focusing on the application of mathematical modeling for flux network analysis in monolignol biosynthesis. Furthermore, we highlight how current challenges might be overcome to optimize the use of metabolic modeling approaches for developing lignin-engineered plants.
Collapse
Affiliation(s)
- Xiaolan Rao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Jaime Barros
- Division of Plant Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, MO 65211, USA.
| |
Collapse
|
3
|
Krix S, Wilczynski E, Falgàs N, Sánchez-Valle R, Yoles E, Nevo U, Baruch K, Fröhlich H. Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches. Front Immunol 2024; 15:1343900. [PMID: 38720902 PMCID: PMC11078023 DOI: 10.3389/fimmu.2024.1343900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.
Collapse
Affiliation(s)
- Sophia Krix
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
| | - Ella Wilczynski
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Neus Falgàs
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Eti Yoles
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Uri Nevo
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Kuti Baruch
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
| |
Collapse
|
4
|
Pandey AK, Nayak SC, Kim SH. Functional link hybrid artificial neural network for predicting continuous biohydrogen production in dynamic membrane bioreactor. BIORESOURCE TECHNOLOGY 2024; 397:130496. [PMID: 38408499 DOI: 10.1016/j.biortech.2024.130496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/30/2024] [Accepted: 02/23/2024] [Indexed: 02/28/2024]
Abstract
Conventional machine learning approaches have shown limited predictive power when applied to continuous biohydrogen production due to nonlinearity and instability. This study was aimed at forecasting the dynamic membrane reactor performance in terms of the hydrogen production rate (HPR) and hydrogen yield (HY) using laboratory-based daily operation datapoints for twelve input variables. Hybrid algorithms were developed by integrating particle swarm optimized with functional link artificial neural network (PSO-FLN) which outperformed other hybrid algorithms for both HPR and HY, with determination coefficients (R2) of 0.97 and 0.80 and mean absolute percentage errors of 0.014 % and 0.023 %, respectively. Shapley additive explanations (SHAP) explained the two positive-influencing parameters, OLR_added (1.1-1.3 mol/L/d) and butyric acid (7.5-16.5 g COD/L) supports the highest HPR (40-60 L/L/d). This research indicates that PSO-FLN model are capable of handling complicated datasets with high precision in less computational timeat 9.8 sec for HPR and 10.0 sec for HY prediction.
Collapse
Affiliation(s)
- Ashutosh Kumar Pandey
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Sarat Chandra Nayak
- Department of Computer Science and Engineering, GITAM University, Hyderabad, India
| | - Sang-Hyoun Kim
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
| |
Collapse
|
5
|
Dodia H, Mishra V, Nakrani P, Muddana C, Kedia A, Rana S, Sahasrabuddhe D, Wangikar PP. Dynamic flux balance analysis of high cell density fed-batch culture of Escherichia coli BL21 (DE3) with mass spectrometry-based spent media analysis. Biotechnol Bioeng 2024; 121:1394-1406. [PMID: 38214104 DOI: 10.1002/bit.28654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/08/2023] [Accepted: 12/29/2023] [Indexed: 01/13/2024]
Abstract
Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome-scale metabolic models (GSMM) and by assuming instantaneous pseudo-steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed-batch, high-cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri-carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.
Collapse
Affiliation(s)
- Hardik Dodia
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Vivek Mishra
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| | | | | | - Anant Kedia
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| | - Sneha Rana
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Deepti Sahasrabuddhe
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Pramod P Wangikar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| |
Collapse
|
6
|
Hu M, Suthers PF, Maranas CD. KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states. Metab Eng 2024; 82:123-133. [PMID: 38336004 DOI: 10.1016/j.ymben.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/24/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.
Collapse
Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
| |
Collapse
|
7
|
Narayanan B, Weilandt D, Masid M, Miskovic L, Hatzimanikatis V. Rational strain design with minimal phenotype perturbation. Nat Commun 2024; 15:723. [PMID: 38267425 PMCID: PMC10808392 DOI: 10.1038/s41467-024-44831-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/08/2024] [Indexed: 01/26/2024] Open
Abstract
Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.
Collapse
Affiliation(s)
- Bharath Narayanan
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Department of Oncology, University of Cambridge, Cambridge, CB2 0XZ, UK
| | - Daniel Weilandt
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Maria Masid
- Ludwig Institute for Cancer Research, Department of Oncology, University of Lausanne, and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| |
Collapse
|
8
|
Ghadermazi P, Chan SHJ. Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution. Bioinformatics 2024; 40:btae003. [PMID: 38212999 PMCID: PMC10799744 DOI: 10.1093/bioinformatics/btae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/24/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024] Open
Abstract
MOTIVATION Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities. RESULTS Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions. AVAILABILITY AND IMPLEMENTATION The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.
Collapse
Affiliation(s)
- Parsa Ghadermazi
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80521, United States
| | - Siu Hung Joshua Chan
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80521, United States
| |
Collapse
|
9
|
Kulakowski S, Banerjee D, Scown CD, Mukhopadhyay A. Improving microbial bioproduction under low-oxygen conditions. Curr Opin Biotechnol 2023; 84:103016. [PMID: 37924688 DOI: 10.1016/j.copbio.2023.103016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/17/2023] [Accepted: 10/07/2023] [Indexed: 11/06/2023]
Abstract
Microbial bioconversion provides access to a wide range of sustainably produced chemicals and commodities. However, industrial-scale bioproduction process operations are preferred to be anaerobic due to the cost associated with oxygen transfer. Anaerobic bioconversion generally offers limited substrate utilization profiles, lower product yields, and reduced final product diversity compared with aerobic processes. Bioproduction under conditions of reduced oxygen can overcome the limitations of fully aerobic and anaerobic bioprocesses, but many microbial hosts are not developed for low-oxygen bioproduction. Here, we describe advances in microbial strain engineering involving the use of redox cofactor engineering, genome-scale metabolic modeling, and functional genomics to enable improved bioproduction processes under low oxygen and provide a viable path for scaling these bioproduction systems to industrial scales.
Collapse
Affiliation(s)
- Shawn Kulakowski
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Corinne D Scown
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Energy Analysis and Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics & Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
| |
Collapse
|
10
|
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.
Collapse
Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
| | | |
Collapse
|
11
|
Georgouli K, Yeom JS, Blake RC, Navid A. Multi-scale models of whole cells: progress and challenges. Front Cell Dev Biol 2023; 11:1260507. [PMID: 38020904 PMCID: PMC10661945 DOI: 10.3389/fcell.2023.1260507] [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: 07/18/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Whole-cell modeling is "the ultimate goal" of computational systems biology and "a grand challenge for 21st century" (Tomita, Trends in Biotechnology, 2001, 19(6), 205-10). These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of "system" models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.
Collapse
Affiliation(s)
- Konstantia Georgouli
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Jae-Seung Yeom
- Center for Applied Scientific Computing, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Robert C. Blake
- Center for Applied Scientific Computing, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Ali Navid
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| |
Collapse
|
12
|
van Lent P, Schmitz J, Abeel T. Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering. ACS Synth Biol 2023; 12:2588-2599. [PMID: 37616156 PMCID: PMC10510747 DOI: 10.1021/acssynbio.3c00186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Indexed: 08/25/2023]
Abstract
Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.
Collapse
Affiliation(s)
- Paul van Lent
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
| | - Joep Schmitz
- Department
of Science and Research, Joep Schmitz -
dsm-firmenich, Science & Research, P.O. Box 1, 2600
MA Delft, The Netherlands
| | - Thomas Abeel
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
- Infectious
Disease and Microbiome Program, Broad Institute
of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| |
Collapse
|
13
|
Li G, Liu L, Du W, Cao H. Local flux coordination and global gene expression regulation in metabolic modeling. Nat Commun 2023; 14:5700. [PMID: 37709734 PMCID: PMC10502109 DOI: 10.1038/s41467-023-41392-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.
Collapse
Affiliation(s)
- Gaoyang Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Li Liu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
| | - Huansheng Cao
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China.
| |
Collapse
|
14
|
Fan X, Cao L, Yan X. Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models. Biotechnol Bioeng 2023. [PMID: 37448239 DOI: 10.1002/bit.28493] [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: 11/29/2022] [Revised: 04/04/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( k cat ${k}_{\mathrm{cat}}$ ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 k cat ${k}_{\mathrm{cat}}$ c), highly sensitive (1759 k cat ${k}_{\mathrm{cat}}$ ), and nonsensitive (2502 k cat ${k}_{\mathrm{cat}}$ ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.
Collapse
Affiliation(s)
- Xingcun Fan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Lingfeng Cao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xuefeng Yan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
| |
Collapse
|
15
|
Kroll A, Rousset Y, Hu XP, Liebrand NA, Lercher MJ. Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nat Commun 2023; 14:4139. [PMID: 37438349 DOI: 10.1038/s41467-023-39840-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/27/2023] [Indexed: 07/14/2023] Open
Abstract
The turnover number kcat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental kcat estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly desirable. However, existing machine learning models are limited to a single, well-studied organism, or they provide inaccurate predictions except for enzymes that are highly similar to proteins in the training set. Here, we present TurNuP, a general and organism-independent model that successfully predicts turnover numbers for natural reactions of wild-type enzymes. We constructed model inputs by representing complete chemical reactions through differential reaction fingerprints and by representing enzymes through a modified and re-trained Transformer Network model for protein sequences. TurNuP outperforms previous models and generalizes well even to enzymes that are not similar to proteins in the training set. Parameterizing metabolic models with TurNuP-predicted kcat values leads to improved proteome allocation predictions. To provide a powerful and convenient tool for the study of molecular biochemistry and physiology, we implemented a TurNuP web server.
Collapse
Affiliation(s)
- Alexander Kroll
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Yvan Rousset
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Xiao-Pan Hu
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Nina A Liebrand
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
| |
Collapse
|
16
|
Karlsen ST, Rau MH, Sánchez BJ, Jensen K, Zeidan AA. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiol Rev 2023; 47:fuad030. [PMID: 37286882 PMCID: PMC10337747 DOI: 10.1093/femsre/fuad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
Collapse
Affiliation(s)
- Signe T Karlsen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Martin H Rau
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Benjamín J Sánchez
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Kristian Jensen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| |
Collapse
|
17
|
de Leeuw M, Matos MRA, Nielsen LK. Omics data for sampling thermodynamically feasible kinetic models. Metab Eng 2023; 78:41-47. [PMID: 37209863 DOI: 10.1016/j.ymben.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/03/2023] [Accepted: 05/07/2023] [Indexed: 05/22/2023]
Abstract
Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.
Collapse
Affiliation(s)
- Marina de Leeuw
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Marta R A Matos
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Lars Keld Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark; Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, 4072, Brisbane QLD, Australia.
| |
Collapse
|
18
|
Wang X, Mohsin A, Sun Y, Li C, Zhuang Y, Wang G. From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology. Bioengineering (Basel) 2023; 10:744. [PMID: 37370675 DOI: 10.3390/bioengineering10060744] [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: 05/15/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.
Collapse
Affiliation(s)
- Xueting Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Ali Mohsin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yifei Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Chao Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| |
Collapse
|
19
|
Sahin A, Weilandt DR, Hatzimanikatis V. Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations. Nat Commun 2023; 14:2618. [PMID: 37147292 PMCID: PMC10162984 DOI: 10.1038/s41467-023-38159-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/19/2023] [Indexed: 05/07/2023] Open
Abstract
Deciphering the metabolic functions of organisms requires understanding the dynamic responses of living cells upon genetic and environmental perturbations, which in turn can be inferred from enzymatic activity. In this work, we investigate the optimal modes of operation for enzymes in terms of the evolutionary pressure driving them toward increased catalytic efficiency. We develop a framework using a mixed-integer formulation to assess the distribution of thermodynamic forces and enzyme states, providing detailed insights into the enzymatic mode of operation. We use this framework to explore Michaelis-Menten and random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative operating modes dependent on reactant concentrations. We find that in a bimolecular enzyme reaction, the random mechanism is optimal over any other ordered mechanism under physiological conditions. Our framework can investigate the optimal catalytic properties of complex enzyme mechanisms. It can further guide the directed evolution of enzymes and fill in the knowledge gaps in enzyme kinetics.
Collapse
Affiliation(s)
- Asli Sahin
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Daniel R Weilandt
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland
- Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
| |
Collapse
|
20
|
Wendering P, Arend M, Razaghi-Moghadam Z, Nikoloski Z. Data integration across conditions improves turnover number estimates and metabolic predictions. Nat Commun 2023; 14:1485. [PMID: 36932067 PMCID: PMC10023748 DOI: 10.1038/s41467-023-37151-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 03/03/2023] [Indexed: 03/19/2023] Open
Abstract
Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.
Collapse
Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Marius Arend
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
- Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
| |
Collapse
|
21
|
Kim GB, Choi SY, Cho IJ, Ahn DH, Lee SY. Metabolic engineering for sustainability and health. Trends Biotechnol 2023; 41:425-451. [PMID: 36635195 DOI: 10.1016/j.tibtech.2022.12.014] [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: 11/09/2022] [Revised: 12/17/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023]
Abstract
Bio-based production of chemicals and materials has attracted much attention due to the urgent need to establish sustainability and enhance human health. Metabolic engineering (ME) allows purposeful modification of cellular metabolic, regulatory, and signaling networks to achieve enhanced production of desired chemicals and degradation of environmentally harmful chemicals. ME has significantly progressed over the past 30 years through further integration of the strategies of synthetic biology, systems biology, evolutionary engineering, and data science aided by artificial intelligence. Here we review the field of ME from its emergence to the current state-of-the-art, highlighting its contribution to sustainable production of chemicals, health, and the environment through representative examples. Future challenges of ME and perspectives are also discussed.
Collapse
Affiliation(s)
- Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - So Young Choi
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - In Jin Cho
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Da-Hee Ahn
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
| |
Collapse
|
22
|
Hu M, Dinh HV, Shen Y, Suthers PF, Foster CJ, Call CM, Ye X, Pratas J, Fatma Z, Zhao H, Rabinowitz JD, Maranas CD. Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale. Metab Eng 2023; 76:1-17. [PMID: 36603705 DOI: 10.1016/j.ymben.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/22/2022] [Accepted: 01/01/2023] [Indexed: 01/04/2023]
Abstract
The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.
Collapse
Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Catherine M Call
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Xuanjia Ye
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Jimmy Pratas
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Zia Fatma
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
| |
Collapse
|
23
|
Martin JP, Rasor BJ, DeBonis J, Karim AS, Jewett MC, Tyo KEJ, Broadbelt LJ. A dynamic kinetic model captures cell-free metabolism for improved butanol production. Metab Eng 2023; 76:133-145. [PMID: 36724840 DOI: 10.1016/j.ymben.2023.01.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/30/2022] [Accepted: 01/25/2023] [Indexed: 01/30/2023]
Abstract
Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.
Collapse
Affiliation(s)
- Jacob P Martin
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Blake J Rasor
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Jonathon DeBonis
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Ashty S Karim
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Michael C Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA.
| |
Collapse
|
24
|
Kang CK, Shin J, Cha Y, Kim MS, Choi MS, Kim T, Park YK, Choi YJ. Machine learning-guided prediction of potential engineering targets for microbial production of lycopene. BIORESOURCE TECHNOLOGY 2023; 369:128455. [PMID: 36503092 DOI: 10.1016/j.biortech.2022.128455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
The process of designing streamlined workflows for developing microbial strains using classical methods from vast amounts of biological big data has reached its limits. With the continuous increase in the amount of biological big data, data-driven machine learning approaches are being used to overcome the limits of classical approaches for strain development. Here, machine learning-guided engineering of Deinococcus radiodurans R1 for high-yield production of lycopene was demonstrated. The multilayer perceptron models were first trained using the mRNA expression levels of the key genes along with lycopene titers and yields obtained from 17 strains. Then, the potential overexpression targets from 2,047 possible combinations were predicted by the multilayer perceptron combined with a genetic algorithm. Through the machine learning-aided fine-tuning of the predicted genes, the final-engineered LY04 strain resulted in an 8-fold increase in the lycopene production, up to 1.25 g/L from glycerol, and a 6-fold increase in the lycopene yield.
Collapse
Affiliation(s)
- Chang Keun Kang
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Jihoon Shin
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - YoonKyung Cha
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - TaeHo Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Young-Kwon Park
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Yong Jun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.
| |
Collapse
|
25
|
V. K P, Sinha S. A systems level approach to study metabolic networks in prokaryotes with the aromatic amino acid biosynthesis pathway. Front Genet 2023; 13:1084727. [PMID: 36726720 PMCID: PMC9885046 DOI: 10.3389/fgene.2022.1084727] [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: 10/30/2022] [Accepted: 12/30/2022] [Indexed: 01/18/2023] Open
Abstract
Metabolism of an organism underlies its phenotype, which depends on many factors, such as the genetic makeup, habitat, and stresses to which it is exposed. This is particularly important for the prokaryotes, which undergo significant vertical and horizontal gene transfers. In this study we have used the energy-intensive Aromatic Amino Acid (Tryptophan, Tyrosine and Phenylalanine, TTP) biosynthesis pathway, in a large number of prokaryotes, as a model system to query the different levels of organization of metabolism in the whole intracellular biochemical network, and to understand how perturbations, such as mutations, affects the metabolic flux through the pathway - in isolation and in the context of other pathways connected to it. Using an agglomerative approach involving complex network analysis and Flux Balance Analyses (FBA), of the Tryptophan, Tyrosine and Phenylalanine and other pathways connected to it, we identify several novel results. Using the reaction network analysis and Flux Balance Analyses of the Tryptophan, Tyrosine and Phenylalanine and the genome-scale reconstructed metabolic pathways, many common hubs between the connected networks and the whole genome network are identified. The results show that the connected pathway network can act as a proxy for the whole genome network in Prokaryotes. This systems level analysis also points towards designing functional smaller synthetic pathways based on the reaction network and Flux Balance Analyses analysis.
Collapse
Affiliation(s)
- Priya V. K
- National Institute of Technology Calicut, Kattangal, Kerala, India,*Correspondence: Priya V. K, ; Somdatta Sinha,
| | - Somdatta Sinha
- Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, India,*Correspondence: Priya V. K, ; Somdatta Sinha,
| |
Collapse
|
26
|
Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, Kontoravdi C, Lewis NE. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng 2023; 75:181-191. [PMID: 36566974 PMCID: PMC10258867 DOI: 10.1016/j.ymben.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
Collapse
Affiliation(s)
| | - Chintan J Joshi
- Department of Pediatrics, University of California San Diego, United States
| | | | - Elcin Icten
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - William Johnson
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, United States; Department of Bioengineering, University of California San Diego, United States.
| |
Collapse
|
27
|
Sieow BFL, De Sotto R, Seet ZRD, Hwang IY, Chang MW. Synthetic Biology Meets Machine Learning. Methods Mol Biol 2023; 2553:21-39. [PMID: 36227537 DOI: 10.1007/978-1-0716-2617-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
Collapse
Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| |
Collapse
|
28
|
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: 24] [Impact Index Per Article: 12.0] [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.
Collapse
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
| |
Collapse
|
29
|
Kenefake D, Armingol E, Lewis NE, Pistikopoulos EN. An improved algorithm for flux variability analysis. BMC Bioinformatics 2022; 23:550. [PMID: 36536290 PMCID: PMC9761963 DOI: 10.1186/s12859-022-05089-9] [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: 09/27/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving [Formula: see text] linear programs (LPs), with n being the number of reactions in the metabolic network. However, FVA can be solved with less than [Formula: see text] LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. In this work, a new algorithm is proposed to solve FVA that requires less than [Formula: see text] LPs. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem.
Collapse
Affiliation(s)
- Dustin Kenefake
- grid.264756.40000 0004 4687 2082Texas A &M Energy Institute, Texas A &M University, College Station, TX 77843 USA ,grid.264756.40000 0004 4687 2082Department of Chemical Engineering, Texas A &M University, College Station, TX 77843 USA
| | - Erick Armingol
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093 USA ,grid.266100.30000 0001 2107 4242Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093 USA
| | - Nathan E. Lewis
- grid.266100.30000 0001 2107 4242Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093 USA ,grid.266100.30000 0001 2107 4242Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093 USA
| | - Efstratios N. Pistikopoulos
- grid.264756.40000 0004 4687 2082Texas A &M Energy Institute, Texas A &M University, College Station, TX 77843 USA ,grid.264756.40000 0004 4687 2082Department of Chemical Engineering, Texas A &M University, College Station, TX 77843 USA
| |
Collapse
|
30
|
Weilandt DR, Salvy P, Masid M, Fengos G, Denhardt-Erikson R, Hosseini Z, Hatzimanikatis V. Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models. Bioinformatics 2022; 39:6887139. [PMID: 36495209 PMCID: PMC9825757 DOI: 10.1093/bioinformatics/btac787] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 11/22/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parameterize and analyze large-scale kinetic models intuitively and efficiently. RESULTS We present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can implement multispecies bioreactor simulations to assess biotechnological processes. AVAILABILITY AND IMPLEMENTATION The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
|
31
|
Li F, Chen Y, Anton M, Nielsen J. GotEnzymes: an extensive database of enzyme parameter predictions. Nucleic Acids Res 2022; 51:D583-D586. [PMID: 36169223 PMCID: PMC9825421 DOI: 10.1093/nar/gkac831] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/08/2022] [Accepted: 09/26/2022] [Indexed: 01/29/2023] Open
Abstract
Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (AI) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by AI approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.
Collapse
Affiliation(s)
- Feiran Li
- Correspondence may also be addressed to Feiran Li.
| | | | | | - Jens Nielsen
- To whom correspondence should be addressed. Tel: +46 31 772 3804;
| |
Collapse
|
32
|
Choudhury S, Moret M, Salvy P, Weilandt D, Hatzimanikatis V, Miskovic L. Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks. NAT MACH INTELL 2022; 4:710-719. [PMID: 37790987 PMCID: PMC10543203 DOI: 10.1038/s42256-022-00519-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/11/2022] [Indexed: 11/09/2022]
Abstract
Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE's capabilities to navigate through the physiological states of metabolism using small numbers of data with significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.
Collapse
Affiliation(s)
- Subham Choudhury
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael Moret
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pierre Salvy
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Present Address: Cambrium GmBH, Berlin, Germany
| | - Daniel Weilandt
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Present Address: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| |
Collapse
|
33
|
Ye C, Wei X, Shi T, Sun X, Xu N, Gao C, Zou W. Genome-scale metabolic network models: from first-generation to next-generation. Appl Microbiol Biotechnol 2022; 106:4907-4920. [PMID: 35829788 DOI: 10.1007/s00253-022-12066-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 06/24/2022] [Accepted: 07/02/2022] [Indexed: 11/26/2022]
Abstract
Over the last two decades, thousands of genome-scale metabolic network models (GSMMs) have been constructed. These GSMMs have been widely applied in various fields, ranging from network interaction analysis, to cell phenotype prediction. However, due to the lack of constraints, the prediction accuracy of first-generation GSMMs was limited. To overcome these limitations, the next-generation GSMMs were developed by integrating omics data, adding constrain condition, integrating different biological models, and constructing whole-cell models. Here, we review recent advances of GSMMs from the first generation to the next generation. Then, we discuss the major application of GSMMs in industrial biotechnology, such as predicting phenotypes and guiding metabolic engineering. In addition, human health applications, including understanding biological mechanisms, discovering biomarkers and drug targets, are also summarized. Finally, we address the challenges and propose new trend of GSMMs. KEY POINTS: •This mini-review updates the literature on almost all published GSMMs since 1999. •Detailed insights into the development of the first- and next-generation GSMMs. •The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized.
Collapse
Affiliation(s)
- Chao Ye
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China.
| | - Xinyu Wei
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Tianqiong Shi
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Xiaoman Sun
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Nan Xu
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, China
| | - Cong Gao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, China
| | - Wei Zou
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin, 644005, China.
| |
Collapse
|
34
|
Zomorrodi AR, Hemez C, Arranz-Gibert P, Wu T, Isaacs FJ, Segrè D. Computational design and engineering of an Escherichia coli strain producing the nonstandard amino acid para-aminophenylalanine. iScience 2022; 25:104562. [PMID: 35789833 PMCID: PMC9249619 DOI: 10.1016/j.isci.2022.104562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/16/2022] [Accepted: 06/02/2022] [Indexed: 11/16/2022] Open
Abstract
Introducing heterologous pathways into host cells constitutes a promising strategy for synthesizing nonstandard amino acids (nsAAs) to enable the production of proteins with expanded chemistries. However, this strategy has proven challenging, as the expression of heterologous pathways can disrupt cellular homeostasis of the host cell. Here, we sought to optimize the heterologous production of the nsAA para-aminophenylalanine (pAF) in Escherichia coli. First, we incorporated a heterologous pAF biosynthesis pathway into a genome-scale model of E. coli metabolism and computationally identified metabolic interventions in the host’s native metabolism to improve pAF production. Next, we explored different approaches of imposing these flux interventions experimentally and found that the upregulation of flux in the chorismate biosynthesis pathway through the elimination of feedback inhibition mechanisms could significantly raise pAF titers (∼20-fold) while maintaining a reasonable pAF production-growth rate trade-off. Overall, this study provides a promising strategy for the biosynthesis of nsAAs in engineered cells. Sought to optimize para-aminophenylalanine (pAF) production and growth in E. coli Identified interventions in the host native metabolism using genome-scale models Constructed multiple mutant strains involving gene knockouts and/or overexpressions Flux modification in chorismate biosynthesis pathway significantly raised pAF titer
Collapse
Affiliation(s)
- Ali R. Zomorrodi
- Mucosal Immunology and Biology Research Center, Pediatrics Department, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA
| | - Colin Hemez
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Pol Arranz-Gibert
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
| | - Terrence Wu
- Yale West Campus Analytical Core, 600 West Campus Drive, West Haven, USA
| | - Farren J. Isaacs
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
- Systems Biology Institute, Yale University, West Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Corresponding author
| | - Daniel Segrè
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA
- Department of Biology, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Corresponding author
| |
Collapse
|
35
|
Hartmann FSF, Udugama IA, Seibold GM, Sugiyama H, Gernaey KV. Digital models in biotechnology: Towards multi-scale integration and implementation. Biotechnol Adv 2022; 60:108015. [PMID: 35781047 DOI: 10.1016/j.biotechadv.2022.108015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/03/2022] [Accepted: 06/27/2022] [Indexed: 12/28/2022]
Abstract
Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.
Collapse
Affiliation(s)
- Fabian S F Hartmann
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Isuru A Udugama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan; Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
| | - Gerd M Seibold
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
| |
Collapse
|
36
|
Sabzevari M, Szedmak S, Penttilä M, Jouhten P, Rousu J. Strain design optimization using reinforcement learning. PLoS Comput Biol 2022; 18:e1010177. [PMID: 35658018 PMCID: PMC9200333 DOI: 10.1371/journal.pcbi.1010177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 06/15/2022] [Accepted: 05/06/2022] [Indexed: 11/18/2022] Open
Abstract
Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization.
In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. Our method is model-free and does not assume prior knowledge of the microbe’s metabolic network or its regulation. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains.
We demonstrate the method’s capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. We investigate the method’s performance relevant for practical applicability in strain engineering i.e. the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library.
Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels.
Collapse
Affiliation(s)
- Maryam Sabzevari
- Department of Computer Science, Aalto University, Espoo, Finland
- * E-mail: ,
| | - Sandor Szedmak
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Merja Penttilä
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Paula Jouhten
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, Finland
| |
Collapse
|
37
|
Bi X, Liu Y, Li J, Du G, Lv X, Liu L. Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges. Biomolecules 2022; 12:biom12050721. [PMID: 35625648 PMCID: PMC9139095 DOI: 10.3390/biom12050721] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 12/04/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.
Collapse
Affiliation(s)
- Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Correspondence: ; Tel.: +86-0510-8591-8312; Fax: +86-0510-8591-8309
| |
Collapse
|
38
|
Lv X, Hueso-Gil A, Bi X, Wu Y, Liu Y, Liu L, Ledesma-Amaro R. New synthetic biology tools for metabolic control. Curr Opin Biotechnol 2022; 76:102724. [PMID: 35489308 DOI: 10.1016/j.copbio.2022.102724] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/28/2022] [Accepted: 03/20/2022] [Indexed: 11/29/2022]
Abstract
In industrial bioprocesses, microbial metabolism dictates the product yields, and therefore, our capacity to control it has an enormous potential to help us move towards a bio-based economy. The rapid development of multiomics data has accelerated our systematic understanding of complex metabolic regulatory mechanisms, which allow us to develop tools to manipulate them. In the last few years, machine learning-based metabolic modeling, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) derived synthetic biology tools, and synthetic genetic circuits have been widely used to control the metabolism of microorganisms, manipulate gene expression, and build synthetic pathways for bioproduction. This review describes the latest developments for metabolic control, and focuses on the trends and challenges of metabolic engineering strategies.
Collapse
Affiliation(s)
- Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Angeles Hueso-Gil
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW72AZ, UK
| | - Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yaokang Wu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW72AZ, UK.
| |
Collapse
|
39
|
Lee JY, Styczynski MP. Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework. Sci Rep 2022; 12:762. [PMID: 35031616 PMCID: PMC8760257 DOI: 10.1038/s41598-021-03934-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/08/2021] [Indexed: 11/29/2022] Open
Abstract
Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework's success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.
Collapse
Affiliation(s)
- Justin Y. Lee
- grid.213917.f0000 0001 2097 4943School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA USA
| | - Mark P. Styczynski
- grid.213917.f0000 0001 2097 4943School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA USA
| |
Collapse
|
40
|
Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model. Metab Eng 2022; 69:286-301. [PMID: 34982997 DOI: 10.1016/j.ymben.2021.12.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/16/2021] [Accepted: 12/29/2021] [Indexed: 11/20/2022]
Abstract
Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via 13C-metabolic flux analysis (13C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated 13C labeling data were used with 13C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.
Collapse
|
41
|
Sun G, Ahn-Horst TA, Covert MW. The E. coli Whole-Cell Modeling Project. EcoSal Plus 2021; 9:eESP00012020. [PMID: 34242084 PMCID: PMC11163835 DOI: 10.1128/ecosalplus.esp-0001-2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 05/26/2021] [Indexed: 12/22/2022]
Abstract
The Escherichia coli whole-cell modeling project seeks to create the most detailed computational model of an E. coli cell in order to better understand and predict the behavior of this model organism. Details about the approach, framework, and current version of the model are discussed. Currently, the model includes the functions of 43% of characterized genes, with ongoing efforts to include additional data and mechanisms. As additional information is incorporated in the model, its utility and predictive power will continue to increase, which means that discovery efforts can be accelerated by community involvement in the generation and inclusion of data. This project will be an invaluable resource to the E. coli community that could be used to verify expected physiological behavior, to predict new outcomes and testable hypotheses for more efficient experimental design iterations, and to evaluate heterogeneous data sets in the context of each other through deep curation.
Collapse
Affiliation(s)
- Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Travis A. Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Markus W. Covert
- Department of Bioengineering, Stanford University, Stanford, California, USA
| |
Collapse
|
42
|
Liebermeister W, Noor E. Model Balancing: A Search for In-Vivo Kinetic Constants and Consistent Metabolic States. Metabolites 2021; 11:749. [PMID: 34822407 PMCID: PMC8621975 DOI: 10.3390/metabo11110749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/08/2021] [Accepted: 10/09/2021] [Indexed: 11/16/2022] Open
Abstract
Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. Given measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, these constants may be inferred by model fitting, but the estimation problems are hard to solve if models are large. Here we show how consistent kinetic constants, metabolite concentrations, and enzyme concentrations can be determined from data if metabolic fluxes are known. The estimation method, called model balancing, can handle models with a wide range of rate laws and accounts for thermodynamic constraints between fluxes, kinetic constants, and metabolite concentrations. It can be used to estimate in-vivo kinetic constants, to complete and adjust available data, and to construct plausible metabolic states with predefined flux distributions. By omitting one term from the log posterior-a term for penalising low enzyme concentrations-we obtain a convex optimality problem with a unique local optimum. As a demonstrative case, we balance a model of E. coli central metabolism with artificial or experimental data and obtain a physically and biologically plausible parameterisation of reaction kinetics in E. coli central metabolism. The example shows what information about kinetic constants can be obtained from omics data and reveals practical limits to estimating in-vivo kinetic constants. While noise-free omics data allow for a reasonable reconstruction of in-vivo kcat and KM values, prediction from noisy omics data are worse. Hence, adjusting kinetic constants and omics data to obtain consistent metabolic models is the main application of model balancing.
Collapse
Affiliation(s)
| | - Elad Noor
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel;
| |
Collapse
|
43
|
Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021; 478:3685-3721. [PMID: 34673920 PMCID: PMC8589332 DOI: 10.1042/bcj20210535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
Collapse
|
44
|
Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLoS Biol 2021; 19:e3001402. [PMID: 34665809 PMCID: PMC8525774 DOI: 10.1371/journal.pbio.3001402] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 08/26/2021] [Indexed: 01/09/2023] Open
Abstract
The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism. To understand the action of an enzyme, we need to know its affinity for its substrates, quantified by Michaelis constants, but these are difficult to measure experimentally. This study shows that a deep learning model that can predict them from structural features of the enzyme and substrate, providing KM predictions for all enzymes across 47 model organisms.
Collapse
|
45
|
Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J 2021; 19:5762-5790. [PMID: 34765093 PMCID: PMC8566780 DOI: 10.1016/j.csbj.2021.10.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/13/2022] Open
Abstract
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.
Collapse
Affiliation(s)
- Claudia Caudai
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Antonella Galizia
- CNR, Institute of Applied Mathematics and Information Technologies (IMATI), Genoa, Italy
| | - Filippo Geraci
- CNR, Institute for Informatics and Telematics (IIT), Pisa, Italy
| | - Loredana Le Pera
- CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Veronica Morea
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Emanuele Salerno
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Allegra Via
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Teresa Colombo
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| |
Collapse
|
46
|
Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics. Processes (Basel) 2021. [DOI: 10.3390/pr9101701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods produce useful predictions on pathway operations. In this review, we present both modelling techniques and we show how they bring complementary views of metabolism. In particular, we show on a simple example how both approaches can be used in conjunction to shed some light on the dynamics of metabolic networks.
Collapse
|
47
|
Küken A, Wendering P, Langary D, Nikoloski Z. A structural property for reduction of biochemical networks. Sci Rep 2021; 11:17415. [PMID: 34465818 PMCID: PMC8408245 DOI: 10.1038/s41598-021-96835-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/19/2021] [Indexed: 11/28/2022] Open
Abstract
Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.
Collapse
Affiliation(s)
- Anika Küken
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Damoun Langary
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
| |
Collapse
|
48
|
Xu R, Razaghi-Moghadam Z, Nikoloski Z. Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli. Bioinformatics 2021; 37:3848-3855. [PMID: 34358300 DOI: 10.1093/bioinformatics/btab575] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 07/19/2021] [Accepted: 08/04/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms. RESULTS Here we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes. AVAILABILITY https://github.com/Rudan-X/NIDLE-flux-code. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Rudan Xu
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany
| | - Zahra Razaghi-Moghadam
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany.,Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, 14476, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany.,Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, 14476, Germany
| |
Collapse
|
49
|
Lu H, Chen Y, Nielsen J, Kerkhoven EJ. Kinetic Models of Metabolism. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
50
|
Alsiyabi A, Chowdhury NB, Long D, Saha R. Enhancing in silico strain design predictions through next generation metabolic modeling approaches. Biotechnol Adv 2021; 54:107806. [PMID: 34298108 DOI: 10.1016/j.biotechadv.2021.107806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/22/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023]
Abstract
The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.
Collapse
Affiliation(s)
- Adil Alsiyabi
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, United States of America
| | - Niaz Bahar Chowdhury
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, United States of America
| | - Dianna Long
- Complex Biosystems, University of Nebraska-Lincoln, United States of America
| | - Rajib Saha
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, United States of America; Complex Biosystems, University of Nebraska-Lincoln, United States of America.
| |
Collapse
|