Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels
•Compartmentalized genome scale metabolic model was reconstructed for Chlorella variabilis.•The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes.•Growth rates predicted by the model were in fair agreement with experimental results.•Red and blue light wavelengths w...
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description | •Compartmentalized genome scale metabolic model was reconstructed for Chlorella variabilis.•The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes.•Growth rates predicted by the model were in fair agreement with experimental results.•Red and blue light wavelengths were found to be essential and useful for high algal growth.
A compartmentalized genome scale metabolic network was reconstructed for Chlorella variabilis to offer insight into various metabolic potentials from this alga. The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes. 21% of the reactions were transport reactions and about 81% of the total reactions were associated with enzymes. Along with gap filling reactions, 2 major sub-pathways were added to the model, chitosan synthesis and rhamnose metabolism. The reconstructed model had reaction participation of 4.3 metabolites per reaction and average lethality fraction of 0.21. The model was effective in capturing the growth of C. variabilis under three light conditions (white, red and red+blue light) with fair agreement. This reconstructed metabolic network will serve an important role in systems biology for further exploration of metabolism for specific target metabolites and enable improved characteristics in the strain through metabolic engineering. |
doi_str_mv | 10.1016/j.biortech.2016.02.118 |
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A compartmentalized genome scale metabolic network was reconstructed for Chlorella variabilis to offer insight into various metabolic potentials from this alga. The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes. 21% of the reactions were transport reactions and about 81% of the total reactions were associated with enzymes. Along with gap filling reactions, 2 major sub-pathways were added to the model, chitosan synthesis and rhamnose metabolism. The reconstructed model had reaction participation of 4.3 metabolites per reaction and average lethality fraction of 0.21. The model was effective in capturing the growth of C. variabilis under three light conditions (white, red and red+blue light) with fair agreement. This reconstructed metabolic network will serve an important role in systems biology for further exploration of metabolism for specific target metabolites and enable improved characteristics in the strain through metabolic engineering.</description><identifier>ISSN: 0960-8524</identifier><identifier>EISSN: 1873-2976</identifier><identifier>DOI: 10.1016/j.biortech.2016.02.118</identifier><identifier>PMID: 26995318</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Algae ; biochemical pathways ; Biofuels ; Biomass ; chitosan ; Chlorella - cytology ; Chlorella - genetics ; Chlorella - growth & development ; Chlorella - metabolism ; Chlorella variabilis ; death ; Flux balance analysis ; Genome ; Genome scale reconstruction ; Metabolic Engineering - methods ; Metabolic Flux Analysis ; Metabolic Networks and Pathways ; Metabolic reconstruction ; metabolism ; metabolites ; Models, Theoretical ; Phenotype ; rhamnose ; technology</subject><ispartof>Bioresource technology, 2016-08, Vol.213, p.103-110</ispartof><rights>2016 Elsevier Ltd</rights><rights>Copyright © 2016 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-840810f76641958af32f70c2e35025ffb1f2ece1c8e3668883c4fd4038c91c503</citedby><cites>FETCH-LOGICAL-c438t-840810f76641958af32f70c2e35025ffb1f2ece1c8e3668883c4fd4038c91c503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.biortech.2016.02.118$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26995318$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Juneja, Ankita</creatorcontrib><creatorcontrib>Chaplen, Frank W.R.</creatorcontrib><creatorcontrib>Murthy, Ganti S.</creatorcontrib><title>Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels</title><title>Bioresource technology</title><addtitle>Bioresour Technol</addtitle><description>•Compartmentalized genome scale metabolic model was reconstructed for Chlorella variabilis.•The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes.•Growth rates predicted by the model were in fair agreement with experimental results.•Red and blue light wavelengths were found to be essential and useful for high algal growth.
A compartmentalized genome scale metabolic network was reconstructed for Chlorella variabilis to offer insight into various metabolic potentials from this alga. The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes. 21% of the reactions were transport reactions and about 81% of the total reactions were associated with enzymes. Along with gap filling reactions, 2 major sub-pathways were added to the model, chitosan synthesis and rhamnose metabolism. The reconstructed model had reaction participation of 4.3 metabolites per reaction and average lethality fraction of 0.21. The model was effective in capturing the growth of C. variabilis under three light conditions (white, red and red+blue light) with fair agreement. This reconstructed metabolic network will serve an important role in systems biology for further exploration of metabolism for specific target metabolites and enable improved characteristics in the strain through metabolic engineering.</description><subject>Algae</subject><subject>biochemical pathways</subject><subject>Biofuels</subject><subject>Biomass</subject><subject>chitosan</subject><subject>Chlorella - cytology</subject><subject>Chlorella - genetics</subject><subject>Chlorella - growth & development</subject><subject>Chlorella - metabolism</subject><subject>Chlorella variabilis</subject><subject>death</subject><subject>Flux balance analysis</subject><subject>Genome</subject><subject>Genome scale reconstruction</subject><subject>Metabolic Engineering - methods</subject><subject>Metabolic Flux Analysis</subject><subject>Metabolic Networks and Pathways</subject><subject>Metabolic reconstruction</subject><subject>metabolism</subject><subject>metabolites</subject><subject>Models, Theoretical</subject><subject>Phenotype</subject><subject>rhamnose</subject><subject>technology</subject><issn>0960-8524</issn><issn>1873-2976</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1v1DAQhi0EotvCX6h85JLUH_FHbqAVFKRKXOBsOd4x9cqJg-1U8O_xsi3i1tPImuedGflB6JqSnhIqb479FFKu4O571t49YT2l-gXaUa14x0YlX6IdGSXptGDDBbos5UgI4VSx1-iCyXEUnOodWm9hSTPg4mwEPEO1U4rB4QwuLaXmzdWQFpw83t_HlCFGix9sDnYKMRTsU8bwa22dsPzAoZb_RqypwlKDjX-pdq7fIJY36JW3scDbx3qFvn_6-G3_ubv7evtl_-GucwPXtdMD0ZR4JeVAR6Gt58wr4hhwQZjwfqKegQPqNHAptdbcDf4wEK7dSJ0g_Aq9O89dc_q5QalmDsWd7l8gbcVQ3XJCUS6eR9WotBJqoA2VZ9TlVEoGb9YcZpt_G0rMSYw5micx5iTGEGaamBa8ftyxTTMc_sWeTDTg_RlofwQPAbIpLsDi4BCai2oOKTy34w8UUaPf</recordid><startdate>20160801</startdate><enddate>20160801</enddate><creator>Juneja, Ankita</creator><creator>Chaplen, Frank W.R.</creator><creator>Murthy, Ganti S.</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20160801</creationdate><title>Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels</title><author>Juneja, Ankita ; Chaplen, Frank W.R. ; Murthy, Ganti S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c438t-840810f76641958af32f70c2e35025ffb1f2ece1c8e3668883c4fd4038c91c503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algae</topic><topic>biochemical pathways</topic><topic>Biofuels</topic><topic>Biomass</topic><topic>chitosan</topic><topic>Chlorella - cytology</topic><topic>Chlorella - genetics</topic><topic>Chlorella - growth & development</topic><topic>Chlorella - metabolism</topic><topic>Chlorella variabilis</topic><topic>death</topic><topic>Flux balance analysis</topic><topic>Genome</topic><topic>Genome scale reconstruction</topic><topic>Metabolic Engineering - methods</topic><topic>Metabolic Flux Analysis</topic><topic>Metabolic Networks and Pathways</topic><topic>Metabolic reconstruction</topic><topic>metabolism</topic><topic>metabolites</topic><topic>Models, Theoretical</topic><topic>Phenotype</topic><topic>rhamnose</topic><topic>technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Juneja, Ankita</creatorcontrib><creatorcontrib>Chaplen, Frank W.R.</creatorcontrib><creatorcontrib>Murthy, Ganti S.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Bioresource technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Juneja, Ankita</au><au>Chaplen, Frank W.R.</au><au>Murthy, Ganti S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels</atitle><jtitle>Bioresource technology</jtitle><addtitle>Bioresour Technol</addtitle><date>2016-08-01</date><risdate>2016</risdate><volume>213</volume><spage>103</spage><epage>110</epage><pages>103-110</pages><issn>0960-8524</issn><eissn>1873-2976</eissn><abstract>•Compartmentalized genome scale metabolic model was reconstructed for Chlorella variabilis.•The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes.•Growth rates predicted by the model were in fair agreement with experimental results.•Red and blue light wavelengths were found to be essential and useful for high algal growth.
A compartmentalized genome scale metabolic network was reconstructed for Chlorella variabilis to offer insight into various metabolic potentials from this alga. The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes. 21% of the reactions were transport reactions and about 81% of the total reactions were associated with enzymes. Along with gap filling reactions, 2 major sub-pathways were added to the model, chitosan synthesis and rhamnose metabolism. The reconstructed model had reaction participation of 4.3 metabolites per reaction and average lethality fraction of 0.21. The model was effective in capturing the growth of C. variabilis under three light conditions (white, red and red+blue light) with fair agreement. This reconstructed metabolic network will serve an important role in systems biology for further exploration of metabolism for specific target metabolites and enable improved characteristics in the strain through metabolic engineering.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>26995318</pmid><doi>10.1016/j.biortech.2016.02.118</doi><tpages>8</tpages></addata></record> |
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subjects | Algae biochemical pathways Biofuels Biomass chitosan Chlorella - cytology Chlorella - genetics Chlorella - growth & development Chlorella - metabolism Chlorella variabilis death Flux balance analysis Genome Genome scale reconstruction Metabolic Engineering - methods Metabolic Flux Analysis Metabolic Networks and Pathways Metabolic reconstruction metabolism metabolites Models, Theoretical Phenotype rhamnose technology |
title | Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels |
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