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Gómez-Schiavon M, Montejano-Montelongo I, Orozco-Ruiz FS, Sotomayor-Vivas C. The art of modeling gene regulatory circuits. NPJ Syst Biol Appl 2024; 10:60. [PMID: 38811585 PMCID: PMC11137155 DOI: 10.1038/s41540-024-00380-2] [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: 11/16/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The amazing complexity of gene regulatory circuits, and biological systems in general, makes mathematical modeling an essential tool to frame and develop our understanding of their properties. Here, we present some fundamental considerations to develop and analyze a model of a gene regulatory circuit of interest, either representing a natural, synthetic, or theoretical system. A mathematical model allows us to effectively evaluate the logical implications of our hypotheses. Using our models to systematically perform in silico experiments, we can then propose specific follow-up assessments of the biological system as well as to reformulate the original assumptions, enriching both our knowledge and our understanding of the system. We want to invite the community working on different aspects of gene regulatory circuits to explore the power and benefits of mathematical modeling in their system.
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Affiliation(s)
- Mariana Gómez-Schiavon
- International Laboratory for Human Genome Research, Universidad Nacional Autónoma de México, Queretaro, 76230, Mexico.
- ANID-Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, 8331150, Chile.
| | - Isabel Montejano-Montelongo
- International Laboratory for Human Genome Research, Universidad Nacional Autónoma de México, Queretaro, 76230, Mexico
- ANID-Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, 8331150, Chile
| | - F Sophia Orozco-Ruiz
- International Laboratory for Human Genome Research, Universidad Nacional Autónoma de México, Queretaro, 76230, Mexico
- ANID-Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, 8331150, Chile
| | - Cristina Sotomayor-Vivas
- International Laboratory for Human Genome Research, Universidad Nacional Autónoma de México, Queretaro, 76230, Mexico
- ANID-Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, 8331150, Chile
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2
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Wong A, Bi C, Chi W, Hu N, Gehring C. Amino acid motifs for the identification of novel protein interactants. Comput Struct Biotechnol J 2022; 21:326-334. [PMID: 36582434 PMCID: PMC9791077 DOI: 10.1016/j.csbj.2022.12.012] [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: 11/08/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Biological systems consist of multiple components of different physical and chemical properties that require complex and dynamic regulatory loops to function efficiently. The discovery of ever more novel interacting sites in complex proteins suggests that we are only beginning to understand how cellular and biological functions are integrated and tuned at the molecular and systems levels. Here we review recently discovered interacting sites which have been identified through rationally designed amino acid motifs diagnostic for specific molecular functions, including enzymatic activities and ligand-binding properties. We specifically discuss the nature of the latter using as examples, novel hormone recognition and gas sensing sites that occur in moonlighting protein complexes. Drawing evidence from the current literature, we discuss the potential implications at the cellular, tissue, and/or organismal levels of such non-catalytic interacting sites and provide several promising avenues for the expansion of amino acid motif searches to discover hitherto unknown protein interactants and interaction networks. We believe this knowledge will unearth unexpected functions in both new and well-characterized proteins, thus filling existing conceptual gaps or opening new avenues for applications either as drug targets or tools in pharmacology, cell biology and bio-catalysis. Beyond this, motif searches may also support the design of novel, effective and sustainable approaches to crop improvements and the development of new therapeutics.
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Affiliation(s)
- Aloysius Wong
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China,Wenzhou Municipal Key Lab for Applied Biomedical and Biopharmaceutical Informatics, Ouhai, Wenzhou, Zhejiang Province 325060, China,Zhejiang Bioinformatics International Science and Technology Cooperation Center, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Chuyun Bi
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China,Wenzhou Municipal Key Lab for Applied Biomedical and Biopharmaceutical Informatics, Ouhai, Wenzhou, Zhejiang Province 325060, China,Zhejiang Bioinformatics International Science and Technology Cooperation Center, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Wei Chi
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Ningxin Hu
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Chris Gehring
- Department of Chemistry, Biology & Biotechnology, University of Perugia, Perugia 06121, Italy,Corresponding author.
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Foo M, Dony L, He F. Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network: A comparative analysis. Biosystems 2022; 219:104732. [PMID: 35781035 DOI: 10.1016/j.biosystems.2022.104732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/30/2022] [Accepted: 06/22/2022] [Indexed: 11/02/2022]
Abstract
Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback control circuits, an accurate model that is able to capture the vital dynamical behaviour of the pathogen-infected plant is required. In this study, using a data-driven modelling approach, we develop and compare four dynamical models (i.e. linear, Michaelis-Menten with Hill coefficient (Hill Function), standard S-System and extended S-System) of a pathogen-infected plant gene regulatory network (GRN). These models are then assessed across several criteria, i.e. ease of identifying the type of gene regulation, the predictive capability, Akaike Information Criterion (AIC) and the robustness to parameter uncertainty to determine its viability of balancing between biological complexity and accuracy when modelling the pathogen-infected plant GRN. Using our defined ranking score, we obtain the following insights to the modelling of GRN. Our analyses show that despite commonly used and provide biological relevance, the Hill Function model ranks the lowest while the extended S-System model ranks highest in the overall comparison. Interestingly, the performance of the linear model is more consistent throughout the comparison, making it the preferred model for this pathogen-infected plant GRN when considering data-driven modelling approach.
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Affiliation(s)
- Mathias Foo
- School of Engineering, University of Warwick, CV4 7AL, Coventry, UK.
| | - Leander Dony
- Institute of Computational Biology, Helmholtz Munich, 85764, Neuherberg, Germany; Department of Translational Psychiatry, Max Planck Institute of Psychiatry, International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany.
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, CV1 2JH, Coventry, UK.
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Mehta G, Cornell SE, Krief A, Hopf H, Matlin SA. A shared future: chemistry's engagement is essential for resilience of people and planet. ROYAL SOCIETY OPEN SCIENCE 2022; 9:212004. [PMID: 35601450 PMCID: PMC9039782 DOI: 10.1098/rsos.212004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/14/2022] [Indexed: 05/03/2023]
Abstract
Strengthening resilience-elasticity or adaptive capacity-is essential in responding to the wide range of natural hazards and anthropogenic changes humanity faces. Chemistry's roles in resilience are explored for the first time, with its technical capacities set in the wider contexts of cross-disciplinary working and the intersecting worlds of science, society and policy. The roles are framed by chemistry's contributions to the sustainability of people and planet, examined via the human security framework's four material aspects of food, health, economic and environmental security. As the science of transformation of matter, chemistry is deeply involved in these material aspects and in their interfacing with human security's three societal and governance aspects of personal, community and political security. Ultimately, strengthening resilience requires making choices about the present use of resources as a hedge against future hazards and adverse events, with these choices being co-determined by technical capacities and social and political will. It is argued that, to intensify its contributions to resilience, chemistry needs to take action along at least three major lines: (i) taking an integrative approach to the field of 'chemistry and resilience'; (ii) rethinking how the chemical industry operates; and (iii) engaging more with society and policy-makers.
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Affiliation(s)
- Goverdhan Mehta
- School of Chemistry, University of Hyderabad, Gachibowli, Hyderabad, Telangana 500046, India
- International Organization for Chemical Sciences in Development, 61 rue de Bruxelles, 5000 Namur, Belgium
| | - Sarah E. Cornell
- Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, 10691 Stockholm, Sweden
| | - Alain Krief
- International Organization for Chemical Sciences in Development, 61 rue de Bruxelles, 5000 Namur, Belgium
- Department of Chemistry, University of Namur, 61 rue de Bruxelles, 5000 Namur, Belgium
| | - Henning Hopf
- International Organization for Chemical Sciences in Development, 61 rue de Bruxelles, 5000 Namur, Belgium
- Institute of Organic Chemistry, Technical University of Braunschweig, Hagenring 30, 38106 Braunschweig, Germany
| | - Stephen A. Matlin
- International Organization for Chemical Sciences in Development, 61 rue de Bruxelles, 5000 Namur, Belgium
- Institute of Global Health Innovation, Imperial College London, South Kensington, London SW7 2AZ, UK
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Foo M, Akman OE, Bates DG. Restoring circadian gene profiles in clock networks using synthetic feedback control. NPJ Syst Biol Appl 2022; 8:7. [PMID: 35169147 PMCID: PMC8847486 DOI: 10.1038/s41540-022-00216-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 01/24/2022] [Indexed: 11/29/2022] Open
Abstract
The circadian system—an organism’s built-in biological clock—is responsible for orchestrating biological processes to adapt to diurnal and seasonal variations. Perturbations to the circadian system (e.g., pathogen attack, sudden environmental change) often result in pathophysiological responses (e.g., jetlag in humans, stunted growth in plants, etc.) In view of this, synthetic biologists are progressively adapting the idea of employing synthetic feedback control circuits to alleviate the effects of perturbations on circadian systems. To facilitate the design of such controllers, suitable models are required. Here, we extend our recently developed model for the plant circadian clock—termed the extended S-System model—to model circadian systems across different kingdoms of life. We then use this modeling strategy to develop a design framework, based on an antithetic integral feedback (AIF) controller, to restore a gene’s circadian profile when it is subject to loss-of-function due to external perturbations. The use of the AIF controller is motivated by its recent successful experimental implementation. Our findings provide circadian biologists with a systematic and general modeling and design approach for implementing synthetic feedback control of circadian systems.
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Affiliation(s)
- Mathias Foo
- School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry, CV1 5FB, UK.,School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - Ozgur E Akman
- College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, EX4 4QF, UK
| | - Declan G Bates
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
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Gupta C, Ramegowda V, Basu S, Pereira A. Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance. Front Genet 2021; 12:652189. [PMID: 34249082 PMCID: PMC8264776 DOI: 10.3389/fgene.2021.652189] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 05/13/2021] [Indexed: 12/13/2022] Open
Abstract
Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.
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Affiliation(s)
- Chirag Gupta
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Venkategowda Ramegowda
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Supratim Basu
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Andy Pereira
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
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Gupta C, Ramegowda V, Basu S, Pereira A. Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance. Front Genet 2021. [PMID: 34249082 DOI: 10.1101/2020.04.29.068379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.
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Affiliation(s)
- Chirag Gupta
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Venkategowda Ramegowda
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Supratim Basu
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Andy Pereira
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
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de Vries FT, Griffiths RI, Knight CG, Nicolitch O, Williams A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 2020; 368:270-274. [PMID: 32299947 DOI: 10.1126/science.aaz5192] [Citation(s) in RCA: 273] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Root-associated microbes can improve plant growth, and they offer the potential to increase crop resilience to future drought. Although our understanding of the complex feedbacks between plant and microbial responses to drought is advancing, most of our knowledge comes from non-crop plants in controlled experiments. We propose that future research efforts should attempt to quantify relationships between plant and microbial traits, explicitly focus on food crops, and include longer-term experiments under field conditions. Overall, we highlight the need for improved mechanistic understanding of the complex feedbacks between plants and microbes during, and particularly after, drought. This requires integrating ecology with plant, microbiome, and molecular approaches and is central to making crop production more resilient to our future climate.
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Affiliation(s)
- Franciska T de Vries
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PT, UK. .,Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1090 GE Amsterdam, Netherlands
| | | | - Christopher G Knight
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PT, UK
| | - Oceane Nicolitch
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PT, UK
| | - Alex Williams
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PT, UK
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Marshall-Colón A, Kliebenstein DJ. Plant Networks as Traits and Hypotheses: Moving Beyond Description. TRENDS IN PLANT SCIENCE 2019; 24:840-852. [PMID: 31300195 DOI: 10.1016/j.tplants.2019.06.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 05/04/2023]
Abstract
Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.
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Affiliation(s)
- Amy Marshall-Colón
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Daniel J Kliebenstein
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA; DynaMo Center of Excellence, University of Copenhagen, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark.
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Mochida K, Koda S, Inoue K, Nishii R. Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets. FRONTIERS IN PLANT SCIENCE 2018; 9:1770. [PMID: 30555503 PMCID: PMC6281826 DOI: 10.3389/fpls.2018.01770] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Accepted: 11/14/2018] [Indexed: 05/20/2023]
Abstract
Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.
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Affiliation(s)
- Keiichi Mochida
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, Japan
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
- Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
| | - Satoru Koda
- Graduate School of Mathematics, Kyushu University, Fukuoka, Japan
| | - Komaki Inoue
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
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