{"type": "FeatureCollection", "features": [{"id": "10.1007/s00253-020-10811-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:14:22Z", "type": "Journal Article", "created": "2020-08-13", "title": "Industrial biotechnology of Pseudomonas putida: advances and prospects", "description": "Abstract<p>Pseudomonas putidais a Gram-negative, rod-shaped bacterium that can be encountered in diverse ecological habitats. This ubiquity is traced to its remarkably versatile metabolism, adapted to withstand physicochemical stress, and the capacity to thrive in harsh environments. Owing to these characteristics, there is a growing interest in this microbe for industrial use, and the corresponding research has made rapid progress in recent years. Hereby, strong drivers are the exploitation of cheap renewable feedstocks and waste streams to produce value-added chemicals and the steady progress in genetic strain engineering and systems biology understanding of this bacterium. Here, we summarize the recent advances and prospects in genetic engineering, systems and synthetic biology, and applications ofP. putidaas a cell factory.</p>Key points<p>\uffe2\uff80\uffa2 Pseudomonas putida advances to a global industrial cell factory.</p><p>\uffe2\uff80\uffa2 Novel tools enable system-wide understanding and streamlined genomic engineering.</p><p>\uffe2\uff80\uffa2 Applications of P. putida range from bioeconomy chemicals to biosynthetic drugs.</p>", "keywords": ["0301 basic medicine", "ddc:500", "0303 health sciences", "Pseudomonas putida", "EDEMP cycle", "PHA", "Systems Biology", "500", "Genomics", "Mini-Review", "Bioeconomy", "Bacterial chassis", "Lignin", "03 medical and health sciences", "/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy", "Microbial cell factory", "13. Climate action", "Biocatalysis", "Synthetic Biology", "KT2440", "Metabolic engineering", "Biotransformation", "Synthetic biology", "Biotechnology"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00253-020-10811-9.pdf"}, {"href": "https://doi.org/10.1007/s00253-020-10811-9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Microbiology%20and%20Biotechnology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00253-020-10811-9", "name": "item", "description": "10.1007/s00253-020-10811-9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00253-020-10811-9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-13T00:00:00Z"}}, {"id": "10.1890/02-3005", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:19:42Z", "type": "Journal Article", "created": "2007-06-04", "title": "Co-2 Enrichment Reduces The Energetic Cost Of Biomass Construction In An Invasive Desert Grass", "description": "To examine how global change could influence species invasions, we compared the responses of energetic processes and growth of invasive and native grass species to atmospheric CO2 enrichment in an intact Mojave Desert ecosystem. Combined with its modest influence on photosynthetic activity, elevated atmospheric CO2 was associated with a significant reduction in the energetic cost of aboveground biomass construction in invasive Bromus madritensis spp. rubens (red brome) without a concurrent cost reduction in native Vulpia octoflora (six-weeks fescue). Consequently, the invasive grass species grew faster, grew bigger, and produced more seeds with atmospheric CO2 enrichment than the native grass species. As a physiological mechanism of invasive species success driven by CO2 enrichment, such alterations in biomass construction costs combined with increased photosynthetic activity could trigger a shift in the species composition of this ecosystem, and potentially that of other invaded ecosystems, toward increa...", "keywords": ["0106 biological sciences", "2. Zero hunger", "Bromus madritensis spp. rubens", "Invasive species", "Invasive grasses", "Red brome", "Systems Biology", "Plant Biology", "Native grasses", "NDFF", "Weed Science", "15. Life on land", "01 natural sciences", "Mojave Desert", "Vulpia octoflora", "Relative growth rate", "13. Climate action", "Energetics", "Six-weeks fescue", "Construction cost", "Elevated CO2", "Photosynthesis", "Nevada"]}, "links": [{"href": "https://doi.org/10.1890/02-3005"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1890/02-3005", "name": "item", "description": "10.1890/02-3005", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1890/02-3005"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2004-01-01T00:00:00Z"}}, {"id": "10.31219/osf.io/jfdb9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:26Z", "type": "Journal Article", "created": "2021-03-15", "title": "Overcoming the challenges to enhancing experimental plant biology with computational modeling", "description": "<p>The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.</p>", "keywords": ["Other Physical Sciences and Mathematics", "computational modeling", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "experimental design", "Systems Biology", "Plant Sciences", "Research Methods in Life Sciences", "mathematical modeling", "Life Sciences", "Plant culture", "bioinformatics", "Plant Science", "collaboration", "SB1-1110", "03 medical and health sciences", "Other Life Sciences", "Physical Sciences and Mathematics"]}, "links": [{"href": "https://doi.org/10.31219/osf.io/jfdb9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.31219/osf.io/jfdb9", "name": "item", "description": "10.31219/osf.io/jfdb9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.31219/osf.io/jfdb9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-15T00:00:00Z"}}, {"id": "3186494073", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:48Z", "type": "Journal Article", "created": "2021-03-15", "title": "Overcoming the challenges to enhancing experimental plant biology with computational modeling", "description": "<p>The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. 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Zero hunger", "0301 basic medicine", "0303 health sciences", "experimental design", "Systems Biology", "Plant Sciences", "Research Methods in Life Sciences", "mathematical modeling", "Life Sciences", "Plant culture", "bioinformatics", "Plant Science", "collaboration", "SB1-1110", "03 medical and health sciences", "Other Life Sciences", "Physical Sciences and Mathematics"]}, "links": [{"href": "https://doi.org/3186494073"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3186494073", "name": "item", "description": "3186494073", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3186494073"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-15T00:00:00Z"}}, {"id": "PMC8329482", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:27:44Z", "type": "Journal Article", "created": "2021-03-15", "title": "Overcoming the challenges to enhancing experimental plant biology with computational modeling", "description": "<p>The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.</p>", "keywords": ["Other Physical Sciences and Mathematics", "computational modeling", "0301 basic medicine", "2. 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