Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks
Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on...
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| Veröffentlicht in: | Canadian journal of forest research 2013-11, Vol.43 (11), p.985-992 |
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| creator | Vicente, Henrique José C. Roseiro José M. Arteiro José Neves A. Teresa Caldeira |
| description | Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria isolated from Quercus suber L. Artificial neural networks were used to maximize the percentage of inhibition triggered by the antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of 15 fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi, and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R² values varying between 0.9965â0.9971 and 0.9974â0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely, 0.25 hâ»Â¹ for surface contaminant fungi, 0.45 hâ»Â¹ for blue stain fungi, and between 0.30 and 0.40 hâ»Â¹ for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses. |
| doi_str_mv | 10.1139/cjfr-2013-0142 |
| format | Article |
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Roseiro ; José M. Arteiro ; José Neves ; A. Teresa Caldeira</creator><creatorcontrib>Vicente, Henrique ; José C. Roseiro ; José M. Arteiro ; José Neves ; A. Teresa Caldeira</creatorcontrib><description>Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria isolated from Quercus suber L. Artificial neural networks were used to maximize the percentage of inhibition triggered by the antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of 15 fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi, and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R² values varying between 0.9965â0.9971 and 0.9974â0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely, 0.25 hâ»Â¹ for surface contaminant fungi, 0.45 hâ»Â¹ for blue stain fungi, and between 0.30 and 0.40 hâ»Â¹ for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses.</description><identifier>ISSN: 1208-6037</identifier><identifier>ISSN: 0045-5067</identifier><identifier>EISSN: 1208-6037</identifier><identifier>DOI: 10.1139/cjfr-2013-0142</identifier><identifier>CODEN: CJFRAR</identifier><language>eng</language><publisher>Ottawa, ON: NRC Research Press</publisher><subject>active ingredients ; antifungal properties ; Bacillus ; Bacillus (Bacteria) ; Bacillus amyloliquefaciens ; Bacteria ; Bioactive compounds ; Biological and medical sciences ; Biopesticides ; blue-stain fungi ; Chemical compounds ; Chemical properties ; Chemical treatment ; Contaminants ; decay fungi ; endophytes ; Environmental aspects ; Forestry ; Fundamental and applied biological sciences. Psychology ; Fungi ; growth retardation ; lipopeptides ; Metabolites ; Microbiological chemistry ; Neural networks ; Optimization ; Pesticides ; Physiological aspects ; Plant diseases ; plant diseases and disorders ; plant pathogenic fungi ; prediction ; Production processes ; Quercus suber ; Wood</subject><ispartof>Canadian journal of forest research, 2013-11, Vol.43 (11), p.985-992</ispartof><rights>2015 INIST-CNRS</rights><rights>COPYRIGHT 2013 NRC Research Press</rights><rights>Copyright Canadian Science Publishing NRC Research Press Nov 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c636t-b666e406201adf86e39a7be5db90df17d00328abaf8c1e1d31cf99ac9c8d02b3</citedby><cites>FETCH-LOGICAL-c636t-b666e406201adf86e39a7be5db90df17d00328abaf8c1e1d31cf99ac9c8d02b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28130777$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Vicente, Henrique</creatorcontrib><creatorcontrib>José C. Roseiro</creatorcontrib><creatorcontrib>José M. Arteiro</creatorcontrib><creatorcontrib>José Neves</creatorcontrib><creatorcontrib>A. Teresa Caldeira</creatorcontrib><title>Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks</title><title>Canadian journal of forest research</title><description>Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria isolated from Quercus suber L. Artificial neural networks were used to maximize the percentage of inhibition triggered by the antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of 15 fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi, and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R² values varying between 0.9965â0.9971 and 0.9974â0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely, 0.25 hâ»Â¹ for surface contaminant fungi, 0.45 hâ»Â¹ for blue stain fungi, and between 0.30 and 0.40 hâ»Â¹ for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses.</description><subject>active ingredients</subject><subject>antifungal properties</subject><subject>Bacillus</subject><subject>Bacillus (Bacteria)</subject><subject>Bacillus amyloliquefaciens</subject><subject>Bacteria</subject><subject>Bioactive compounds</subject><subject>Biological and medical sciences</subject><subject>Biopesticides</subject><subject>blue-stain fungi</subject><subject>Chemical compounds</subject><subject>Chemical properties</subject><subject>Chemical treatment</subject><subject>Contaminants</subject><subject>decay fungi</subject><subject>endophytes</subject><subject>Environmental aspects</subject><subject>Forestry</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Fungi</subject><subject>growth retardation</subject><subject>lipopeptides</subject><subject>Metabolites</subject><subject>Microbiological chemistry</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Pesticides</subject><subject>Physiological aspects</subject><subject>Plant diseases</subject><subject>plant diseases and disorders</subject><subject>plant pathogenic fungi</subject><subject>prediction</subject><subject>Production processes</subject><subject>Quercus suber</subject><subject>Wood</subject><issn>1208-6037</issn><issn>0045-5067</issn><issn>1208-6037</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqVkk1v1DAQhiMEEqVw5YoFQiqHFDtOnPhYVRQqVYBoOVsTfwQvib21HUr_PU53BV20EkI-jMfzzKvxzBTFc4KPCaH8rVyZUFaY0BKTunpQHJAKdyXDtH147_64eBLjCmNMGcUHhf0ctLIyWe-QN6i3HrLzQyPpp7WfnUJ3vk23CAawLiZ0473KYZdgsg5cQmZ2g0VztG5AEJI1VloYkdNzuDPpxofv8WnxyMAY9bOtPSyuzt5dnX4oLz69Pz89uSgloyyVPWNM15jlj4AyHdOUQ9vrRvUcK0NalSuvOujBdJJooiiRhnOQXHYKVz09LI42suvgr2cdk5hslHocwWk_R0FqVje4IazN6Ku_0JWfg8vFZarpWk6apvlDDTBqYZ3xKYBcRMUJbeqq5nXLMlXuoQbtdO6Bd9rY_LzDv9zDy7W9Fveh4z1QPkpPVu5VfbOTsExJ_0wDzDGK88sv_8F-3GW3hcjgYwzaiHWwE4RbQbBYlk8syyeW5RPL8uWE19vWQpQwmgBO2vg7q-oIxW27jIBsOBdk0FFDkN_-rf1ik2PACxhC1v16mcMMY8I5bwn9BX778K8</recordid><startdate>20131101</startdate><enddate>20131101</enddate><creator>Vicente, Henrique</creator><creator>José C. Roseiro</creator><creator>José M. Arteiro</creator><creator>José Neves</creator><creator>A. Teresa Caldeira</creator><general>NRC Research Press</general><general>National Research Council of Canada</general><general>Canadian Science Publishing NRC Research Press</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>U9A</scope><scope>M7N</scope></search><sort><creationdate>20131101</creationdate><title>Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks</title><author>Vicente, Henrique ; José C. Roseiro ; José M. Arteiro ; José Neves ; A. Teresa Caldeira</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c636t-b666e406201adf86e39a7be5db90df17d00328abaf8c1e1d31cf99ac9c8d02b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>active ingredients</topic><topic>antifungal properties</topic><topic>Bacillus</topic><topic>Bacillus (Bacteria)</topic><topic>Bacillus amyloliquefaciens</topic><topic>Bacteria</topic><topic>Bioactive compounds</topic><topic>Biological and medical sciences</topic><topic>Biopesticides</topic><topic>blue-stain fungi</topic><topic>Chemical compounds</topic><topic>Chemical properties</topic><topic>Chemical treatment</topic><topic>Contaminants</topic><topic>decay fungi</topic><topic>endophytes</topic><topic>Environmental aspects</topic><topic>Forestry</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Fungi</topic><topic>growth retardation</topic><topic>lipopeptides</topic><topic>Metabolites</topic><topic>Microbiological chemistry</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Pesticides</topic><topic>Physiological aspects</topic><topic>Plant diseases</topic><topic>plant diseases and disorders</topic><topic>plant pathogenic fungi</topic><topic>prediction</topic><topic>Production processes</topic><topic>Quercus suber</topic><topic>Wood</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vicente, Henrique</creatorcontrib><creatorcontrib>José C. Roseiro</creatorcontrib><creatorcontrib>José M. Arteiro</creatorcontrib><creatorcontrib>José Neves</creatorcontrib><creatorcontrib>A. Teresa Caldeira</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><jtitle>Canadian journal of forest research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vicente, Henrique</au><au>José C. Roseiro</au><au>José M. Arteiro</au><au>José Neves</au><au>A. Teresa Caldeira</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks</atitle><jtitle>Canadian journal of forest research</jtitle><date>2013-11-01</date><risdate>2013</risdate><volume>43</volume><issue>11</issue><spage>985</spage><epage>992</epage><pages>985-992</pages><issn>1208-6037</issn><issn>0045-5067</issn><eissn>1208-6037</eissn><coden>CJFRAR</coden><abstract>Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria isolated from Quercus suber L. Artificial neural networks were used to maximize the percentage of inhibition triggered by the antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of 15 fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi, and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R² values varying between 0.9965â0.9971 and 0.9974â0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely, 0.25 hâ»Â¹ for surface contaminant fungi, 0.45 hâ»Â¹ for blue stain fungi, and between 0.30 and 0.40 hâ»Â¹ for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses.</abstract><cop>Ottawa, ON</cop><pub>NRC Research Press</pub><doi>10.1139/cjfr-2013-0142</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Canadian journal of forest research, 2013-11, Vol.43 (11), p.985-992 |
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| language | eng |
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| subjects | active ingredients antifungal properties Bacillus Bacillus (Bacteria) Bacillus amyloliquefaciens Bacteria Bioactive compounds Biological and medical sciences Biopesticides blue-stain fungi Chemical compounds Chemical properties Chemical treatment Contaminants decay fungi endophytes Environmental aspects Forestry Fundamental and applied biological sciences. Psychology Fungi growth retardation lipopeptides Metabolites Microbiological chemistry Neural networks Optimization Pesticides Physiological aspects Plant diseases plant diseases and disorders plant pathogenic fungi prediction Production processes Quercus suber Wood |
| title | Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks |
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