{"id":485,"date":"2020-08-23T18:43:54","date_gmt":"2020-08-23T18:43:54","guid":{"rendered":"https:\/\/arkinlab.org\/?p=485"},"modified":"2021-02-22T08:06:52","modified_gmt":"2021-02-22T08:06:52","slug":"cubes","status":"publish","type":"post","link":"https:\/\/arkinlab.bio\/cubes\/","title":{"rendered":"CUBES"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-e00b4d3e14a5bbeb0b1267d7ce6a0c04\">\n#top .av-special-heading.av-av_heading-e00b4d3e14a5bbeb0b1267d7ce6a0c04{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-e00b4d3e14a5bbeb0b1267d7ce6a0c04 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-e00b4d3e14a5bbeb0b1267d7ce6a0c04 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-e00b4d3e14a5bbeb0b1267d7ce6a0c04 av-special-heading-h2 blockquote modern-quote  avia-builder-el-0  el_before_av_textblock  avia-builder-el-first '><h2 class='av-special-heading-tag '  itemprop=\"headline\"  >CUBES<\/h2><div class='av-subheading av-subheading_below'><p><em>Towards an integrated closed-loop autotrophic biomanufacturing plant for deep space<\/em><\/p>\n<\/div><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-keaaqwfz-7b84e34c5089e475bf6920ec2ffb921c '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>The Center for the Utilization of Biological Engineering in Space (CUBES) was created to develop generalizable approaches to support biomanufacturing for deep space exploration that realizes the inherent mass, power, and volume advantages of space biotechnology over traditional abiotic approaches. We seek to develop nearly closed-loop processes that can use regenerable in situ resources to produce food, pharmaceuticals, and materials to support small colonies of people in resource-poor and extreme environments like deep space. CUBES is specifically focused on developing such a plant for a crewed mission to Mars. However, we are mindful of the applications both for a lunar base and for application right here on earth.<\/p>\n<p>In this center, for which Arkin is the Director and Berliner is the &#8220;majordomo&#8221;, we specifically work (collaboratively) on model-driven mission specification and process optimization, synthetic biology and production engineering of biopolymer and pharmaceutical producing microbes, and microbial community engineering for optimizing plant productivity and environmental resiliency.<\/p>\n<p>For more information visit <a href=\"https:\/\/cubes.space\/\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/cubes.space\/<\/a><\/p>\n<\/div><\/section>\n<div  class='hr av-bejp1-0eafda308a23cbd9d76a4795ad0f6433 hr-default  avia-builder-el-2  el_after_av_textblock  el_before_av_gallery '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-khfsudcb-f6a32ccd459da556f2e92d9ca5493893\">\n#top .avia-gallery.av-khfsudcb-f6a32ccd459da556f2e92d9ca5493893 .avia-gallery-thumb a{\nwidth:20%;\n}\n<\/style>\n<div  class='avia-gallery av-khfsudcb-f6a32ccd459da556f2e92d9ca5493893 avia_animate_when_visible  avia-builder-el-3  el_after_av_hr  el_before_av_hr  avia_lazyload avia-gallery-animate avia-gallery-1'  itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><meta itemprop=\"contentURL\" content=\"https:\/\/arkinlab.bio\/cubes\/\"><div class='avia-gallery-thumb'><a href=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-1030x515.png\" data-srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-1030x515.png 1030w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-300x150.png 300w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-768x384.png 768w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-1536x768.png 1536w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-2048x1024.png 2048w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-1500x750.png 1500w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-705x352.png 705w\" data-sizes=\"(max-width: 1030px) 100vw, 1030px\" data-rel='gallery-1' data-prev-img='https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-495x400.png' class='first_thumb lightbox ' data-onclick='1' title='Mosaic Y4 Fall Retreat low res 2'  itemprop=\"thumbnailUrl\"  ><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-1699 avia-img-lazy-loading-not-1699\"  data-avia-tooltip='Mosaic Y4 Fall Retreat low res 2' src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-495x400.png\" width=\"495\" height=\"400\"  title='Mosaic Y4 Fall Retreat low res 2' alt='Mosaic Y4 Fall Retreat low res 2' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-495x400.png 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-845x684.png 845w\" sizes=\"(max-width: 495px) 100vw, 495px\" \/><div class='big-prev-fake'><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-1699 avia-img-lazy-loading-1699\"  width=\"495\" height=\"400\" src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-495x400.png\" title='Mosaic Y4 Fall Retreat low res 2' alt='Mosaic Y4 Fall Retreat low res 2' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-495x400.png 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Y4-Fall-Retreat-low-res-2-845x684.png 845w\" sizes=\"auto, (max-width: 495px) 100vw, 495px\" \/><\/div><\/a><a href=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-1030x607.png\" data-srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-1030x607.png 1030w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-300x177.png 300w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-768x453.png 768w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-1536x906.png 1536w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-2048x1208.png 2048w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-1500x885.png 1500w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-705x416.png 705w\" data-sizes=\"(max-width: 1030px) 100vw, 1030px\" data-rel='gallery-1' 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srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-495x400.png 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Mosaic-Photo-845x684.png 845w\" sizes=\"auto, (max-width: 495px) 100vw, 495px\" \/><\/div><\/a><a href=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-1030x794.jpg\" data-srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-1030x794.jpg 1030w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-300x231.jpg 300w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-768x592.jpg 768w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-1536x1184.jpg 1536w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-2048x1578.jpg 2048w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-1500x1156.jpg 1500w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-705x543.jpg 705w\" data-sizes=\"(max-width: 1030px) 100vw, 1030px\" data-rel='gallery-1' data-prev-img='https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-495x400.jpg' class='lightbox ' data-onclick='3' title='Sep 18, 2019'  itemprop=\"thumbnailUrl\"  ><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-1518 avia-img-lazy-loading-not-1518\"  data-avia-tooltip='Sep 18, 2019' src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-495x400.jpg\" width=\"495\" height=\"400\"  title='Y2 Retreat Utah State' alt='Y2 Retreat Utah State' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-845x684.jpg 845w\" sizes=\"(max-width: 495px) 100vw, 495px\" \/><div class='big-prev-fake'><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-1518 avia-img-lazy-loading-1518\"  width=\"495\" height=\"400\" src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-495x400.jpg\" title='Y2 Retreat Utah State' alt='Y2 Retreat Utah State' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Y2-Retreat-Utah-CUBE-Group-1-845x684.jpg 845w\" sizes=\"auto, (max-width: 495px) 100vw, 495px\" \/><\/div><\/a><a href=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-1030x773.jpg\" data-srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-1030x773.jpg 1030w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-300x225.jpg 300w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-768x576.jpg 768w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-1536x1152.jpg 1536w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-2048x1536.jpg 2048w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-1500x1125.jpg 1500w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-705x529.jpg 705w\" data-sizes=\"(max-width: 1030px) 100vw, 1030px\" data-rel='gallery-1' data-prev-img='https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-495x400.jpg' class='lightbox ' data-onclick='4' title='May 15, 2019'  itemprop=\"thumbnailUrl\"  ><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-1517 avia-img-lazy-loading-not-1517\"  data-avia-tooltip='May 15, 2019' src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-495x400.jpg\" width=\"495\" height=\"400\"  title='UC Davis Winery Tour - Y2 Spring Review' alt='UC Davis Winery Tour - Y2 Spring Review' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-845x684.jpg 845w\" sizes=\"(max-width: 495px) 100vw, 495px\" \/><div class='big-prev-fake'><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-1517 avia-img-lazy-loading-1517\"  width=\"495\" height=\"400\" src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-495x400.jpg\" title='UC Davis Winery Tour - Y2 Spring Review' alt='UC Davis Winery Tour - Y2 Spring Review' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/Winery-Tour-Y2-Spring-Review-845x684.jpg 845w\" sizes=\"auto, (max-width: 495px) 100vw, 495px\" \/><\/div><\/a><a href=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-1030x673.jpg\" data-srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-1030x673.jpg 1030w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-300x196.jpg 300w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-768x502.jpg 768w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-1536x1004.jpg 1536w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-2048x1339.jpg 2048w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-1500x980.jpg 1500w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-705x461.jpg 705w\" data-sizes=\"(max-width: 1030px) 100vw, 1030px\" data-rel='gallery-1' data-prev-img='https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-495x400.jpg' class='lightbox ' data-onclick='5' title='Oct 20, 2018'  itemprop=\"thumbnailUrl\"  ><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-1515 avia-img-lazy-loading-not-1515\"  data-avia-tooltip='Oct 20, 2018' src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-495x400.jpg\" width=\"495\" height=\"400\"  title='Y2 UC Berkeley CUBES group' alt='Y2 UC Berkeley CUBES group' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-845x684.jpg 845w\" sizes=\"(max-width: 495px) 100vw, 495px\" \/><div class='big-prev-fake'><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-1515 avia-img-lazy-loading-1515\"  width=\"495\" height=\"400\" src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-495x400.jpg\" title='Y2 UC Berkeley CUBES group' alt='Y2 UC Berkeley CUBES group' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/cubes-group-Y2-845x684.jpg 845w\" sizes=\"auto, (max-width: 495px) 100vw, 495px\" \/><\/div><\/a><a href=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-773x1030.jpg\" data-srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-773x1030.jpg 773w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-225x300.jpg 225w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-768x1024.jpg 768w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-1152x1536.jpg 1152w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-1536x2048.jpg 1536w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-1125x1500.jpg 1125w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-529x705.jpg 529w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-scaled.jpg 1920w\" data-sizes=\"(max-width: 773px) 100vw, 773px\" data-rel='gallery-1' data-prev-img='https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-495x400.jpg' class='first_thumb lightbox ' data-onclick='6' title='Oct 19, 2017'  itemprop=\"thumbnailUrl\"  ><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-1516 avia-img-lazy-loading-not-1516\"  data-avia-tooltip='Oct 19, 2017' src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-495x400.jpg\" width=\"495\" height=\"400\"  title='Y1 UC Berkeley CUBES group' alt='Y1 UC Berkeley CUBES group' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-845x684.jpg 845w\" sizes=\"(max-width: 495px) 100vw, 495px\" \/><div class='big-prev-fake'><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-1516 avia-img-lazy-loading-1516\"  width=\"495\" height=\"400\" src=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-495x400.jpg\" title='Y1 UC Berkeley CUBES group' alt='Y1 UC Berkeley CUBES group' srcset=\"https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-495x400.jpg 495w, https:\/\/arkinlab.bio\/wp-content\/uploads\/2020\/08\/CUBES-group-Y1-845x684.jpg 845w\" sizes=\"auto, (max-width: 495px) 100vw, 495px\" \/><\/div><\/a><\/div><\/div>\n<div  class='hr av-bejp1-0eafda308a23cbd9d76a4795ad0f6433 hr-default  avia-builder-el-4  el_after_av_gallery  el_before_av_textblock '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<section  class='av_textblock_section av-keaaqwfz-7b84e34c5089e475bf6920ec2ffb921c '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><h3>Selected Publications<\/h3>\n<div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><\/form><div class=\"teachpress_publication_list\"><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Sander, Kyle;  Abel, Anthony J.;  Friedline, Skyler;  Sharpless, William;  Skerker, Jeffrey;  Deutschbauer, Adam;  Clark, Douglas S.;  Arkin, Adam P.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('444','tp_links')\" style=\"cursor:pointer;\">Eliminating genes for a two\u2010component system increases PHB productivity in <i>Cupriavidus basilensis<\/i> 4G11 under PHB suppressing, nonstress conditions<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Biotech &amp; Bioengineering, <\/span><span class=\"tp_pub_additional_volume\">vol. 121, <\/span><span class=\"tp_pub_additional_number\">no. 1, <\/span><span class=\"tp_pub_additional_pages\">pp. 139\u2013156, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1097-0290<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_444\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('444','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_444\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('444','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_444\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('444','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_444\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Sander2023b,<br \/>\r\ntitle = {Eliminating genes for a two\u2010component system increases PHB productivity in \\textit{Cupriavidus basilensis} 4G11 under PHB suppressing, nonstress conditions},<br \/>\r\nauthor = {Kyle Sander and Anthony J. Abel and Skyler Friedline and William Sharpless and Jeffrey Skerker and Adam Deutschbauer and Douglas S. Clark and Adam P. Arkin},<br \/>\r\ndoi = {10.1002\/bit.28532},<br \/>\r\nissn = {1097-0290},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-00},<br \/>\r\nurldate = {2024-01-00},<br \/>\r\njournal = {Biotech & Bioengineering},<br \/>\r\nvolume = {121},<br \/>\r\nnumber = {1},<br \/>\r\npages = {139--156},<br \/>\r\npublisher = {Wiley},<br \/>\r\nabstract = {&lt;jats:title&gt;Abstract&lt;\/jats:title&gt;&lt;jats:p&gt;Species of bacteria from the genus &lt;jats:italic&gt;Cupriavidus&lt;\/jats:italic&gt; are known, in part, for their ability to produce high amounts of poly\u2010hydroxybutyrate (PHB) making them attractive candidates for bioplastic production. The native synthesis of PHB occurs during periods of metabolic stress, and the process regulating the initiation of PHB accumulation in these organisms is not fully understood. Screening an RB\u2010TnSeq transposon library of &lt;jats:italic&gt;Cupriavidus basilensis&lt;\/jats:italic&gt; 4G11 allowed us to identify two genes of an apparent, uncharacterized two\u2010component system, which when omitted from the genome enable increased PHB productivity in balanced, nonstress growth conditions. We observe average increases in PHB productivity of 56% and 41% relative to the wildtype parent strain upon deleting each gene individually from the genome. The increased PHB phenotype disappears, however, in nitrogen\u2010free unbalanced growth conditions suggesting the phenotype is specific to fast\u2010growing, replete, nonstress growth. Bioproduction modeling suggests this phenotype could be due to a decreased reliance on metabolic stress induced by nitrogen limitation to initiate PHB production in the mutant strains. Due to uncertainty in the two\u2010component system's input signal and regulon, the mechanism by which these genes impart this phenotype remains unclear. Such strains may allow for the use of single\u2010stage, continuous bioreactor systems, which are far simpler than many PHB bioproduction schemes used previously, given a similar product yield to batch systems in such a configuration. Bioproductivity modeling suggests that omitting this regulation in the cells may increase PHB productivity up to 24% relative to the wildtype organism when using single\u2010stage continuous systems. This work expands our understanding of the regulation of PHB accumulation in &lt;jats:italic&gt;Cupriavidus&lt;\/jats:italic&gt;, in particular the initiation of this process upon transition into unbalanced growth regimes.&lt;\/jats:p&gt;},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('444','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_444\" style=\"display:none;\"><div class=\"tp_abstract_entry\">&lt;jats:title&gt;Abstract&lt;\/jats:title&gt;&lt;jats:p&gt;Species of bacteria from the genus &lt;jats:italic&gt;Cupriavidus&lt;\/jats:italic&gt; are known, in part, for their ability to produce high amounts of poly\u2010hydroxybutyrate (PHB) making them attractive candidates for bioplastic production. The native synthesis of PHB occurs during periods of metabolic stress, and the process regulating the initiation of PHB accumulation in these organisms is not fully understood. Screening an RB\u2010TnSeq transposon library of &lt;jats:italic&gt;Cupriavidus basilensis&lt;\/jats:italic&gt; 4G11 allowed us to identify two genes of an apparent, uncharacterized two\u2010component system, which when omitted from the genome enable increased PHB productivity in balanced, nonstress growth conditions. We observe average increases in PHB productivity of 56% and 41% relative to the wildtype parent strain upon deleting each gene individually from the genome. The increased PHB phenotype disappears, however, in nitrogen\u2010free unbalanced growth conditions suggesting the phenotype is specific to fast\u2010growing, replete, nonstress growth. Bioproduction modeling suggests this phenotype could be due to a decreased reliance on metabolic stress induced by nitrogen limitation to initiate PHB production in the mutant strains. Due to uncertainty in the two\u2010component system's input signal and regulon, the mechanism by which these genes impart this phenotype remains unclear. Such strains may allow for the use of single\u2010stage, continuous bioreactor systems, which are far simpler than many PHB bioproduction schemes used previously, given a similar product yield to batch systems in such a configuration. Bioproductivity modeling suggests that omitting this regulation in the cells may increase PHB productivity up to 24% relative to the wildtype organism when using single\u2010stage continuous systems. This work expands our understanding of the regulation of PHB accumulation in &lt;jats:italic&gt;Cupriavidus&lt;\/jats:italic&gt;, in particular the initiation of this process upon transition into unbalanced growth regimes.&lt;\/jats:p&gt;<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('444','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_444\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1002\/bit.28532\" title=\"Follow DOI:10.1002\/bit.28532\" target=\"_blank\">doi:10.1002\/bit.28532<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('444','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Adams, Jeremy David;  Sander, Kyle B.;  Criddle, Craig S.;  Arkin, Adam P.;  Clark, Douglas S.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('413','tp_links')\" style=\"cursor:pointer;\">Engineering osmolysis susceptibility in Cupriavidus necator and Escherichia coli for recovery of intracellular products<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Microb Cell Fact, <\/span><span class=\"tp_pub_additional_volume\">vol. 22, <\/span><span class=\"tp_pub_additional_number\">no. 1, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1475-2859<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_413\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('413','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_413\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('413','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_413\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('413','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_413\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Adams2023,<br \/>\r\ntitle = {Engineering osmolysis susceptibility in Cupriavidus necator and Escherichia coli for recovery of intracellular products},<br \/>\r\nauthor = {Jeremy David Adams and Kyle B. Sander and Craig S. Criddle and Adam P. Arkin and Douglas S. Clark},<br \/>\r\ndoi = {10.1186\/s12934-023-02064-8},<br \/>\r\nissn = {1475-2859},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-12-00},<br \/>\r\njournal = {Microb Cell Fact},<br \/>\r\nvolume = {22},<br \/>\r\nnumber = {1},<br \/>\r\npublisher = {Springer Science and Business Media LLC},<br \/>\r\nabstract = {<jats:title>Abstract<\/jats:title><jats:sec><br \/>\n                <jats:title>Background<\/jats:title><br \/>\n                <jats:p>Intracellular biomacromolecules, such as industrial enzymes and biopolymers, represent an important class of bio-derived products obtained from bacterial hosts. A common key step in the downstream separation of these biomolecules is lysis of the bacterial cell wall to effect release of cytoplasmic contents. Cell lysis is typically achieved either through mechanical disruption or reagent-based methods, which introduce issues of energy demand, material needs, high costs, and scaling problems. Osmolysis, a cell lysis method that relies on hypoosmotic downshock upon resuspension of cells in distilled water, has been applied for bioseparation of intracellular products from extreme halophiles and mammalian cells. However, most industrial bacterial strains are non-halotolerant and relatively resistant to hypoosmotic cell lysis. <\/jats:p><br \/>\n              <\/jats:sec><jats:sec><br \/>\n                <jats:title>Results<\/jats:title><br \/>\n                <jats:p>To overcome this limitation, we developed two strategies to increase the susceptibility of non-halotolerant hosts to osmolysis using <jats:italic>Cupriavidus necator<\/jats:italic>, a strain often used in electromicrobial production, as a prototypical strain. In one strategy, <jats:italic>C. necator<\/jats:italic> was evolved to increase its halotolerance from 1.5% to 3.25% (w\/v) NaCl through adaptive laboratory evolution, and genes potentially responsible for this phenotypic change were identified by whole genome sequencing. The evolved halotolerant strain experienced an osmolytic efficiency of 47% in distilled water following growth in 3% (w\/v) NaCl. In a second strategy, the cells were made susceptible to osmolysis by knocking out the large-conductance mechanosensitive channel (<jats:italic>mscL<\/jats:italic>) gene in <jats:italic>C. necator<\/jats:italic>. When these strategies were combined by knocking out the <jats:italic>mscL<\/jats:italic> gene from the evolved halotolerant strain, greater than 90% osmolytic efficiency was observed upon osmotic downshock. A modified version of this strategy was applied to <jats:italic>E. coli<\/jats:italic> BL21 by deleting the <jats:italic>mscL<\/jats:italic> and <jats:italic>mscS<\/jats:italic> (small-conductance mechanosensitive channel) genes. When grown in medium with 4% NaCl and subsequently resuspended in distilled water, this engineered strain experienced 75% cell lysis, although decreases in cell growth rate due to higher salt concentrations were observed.<\/jats:p><br \/>\n              <\/jats:sec><jats:sec><br \/>\n                <jats:title>Conclusions<\/jats:title><br \/>\n                <jats:p>Our strategy is shown to be a simple and effective way to lyse cells for the purification of intracellular biomacromolecules and may be applicable in many bacteria used for bioproduction.<\/jats:p><br \/>\n              <\/jats:sec>},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('413','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_413\" style=\"display:none;\"><div class=\"tp_abstract_entry\"><jats:title>Abstract<\/jats:title><jats:sec><br \/>\n                <jats:title>Background<\/jats:title><br \/>\n                <jats:p>Intracellular biomacromolecules, such as industrial enzymes and biopolymers, represent an important class of bio-derived products obtained from bacterial hosts. A common key step in the downstream separation of these biomolecules is lysis of the bacterial cell wall to effect release of cytoplasmic contents. Cell lysis is typically achieved either through mechanical disruption or reagent-based methods, which introduce issues of energy demand, material needs, high costs, and scaling problems. Osmolysis, a cell lysis method that relies on hypoosmotic downshock upon resuspension of cells in distilled water, has been applied for bioseparation of intracellular products from extreme halophiles and mammalian cells. However, most industrial bacterial strains are non-halotolerant and relatively resistant to hypoosmotic cell lysis. <\/jats:p><br \/>\n              <\/jats:sec><jats:sec><br \/>\n                <jats:title>Results<\/jats:title><br \/>\n                <jats:p>To overcome this limitation, we developed two strategies to increase the susceptibility of non-halotolerant hosts to osmolysis using <jats:italic>Cupriavidus necator<\/jats:italic>, a strain often used in electromicrobial production, as a prototypical strain. In one strategy, <jats:italic>C. necator<\/jats:italic> was evolved to increase its halotolerance from 1.5% to 3.25% (w\/v) NaCl through adaptive laboratory evolution, and genes potentially responsible for this phenotypic change were identified by whole genome sequencing. The evolved halotolerant strain experienced an osmolytic efficiency of 47% in distilled water following growth in 3% (w\/v) NaCl. In a second strategy, the cells were made susceptible to osmolysis by knocking out the large-conductance mechanosensitive channel (<jats:italic>mscL<\/jats:italic>) gene in <jats:italic>C. necator<\/jats:italic>. When these strategies were combined by knocking out the <jats:italic>mscL<\/jats:italic> gene from the evolved halotolerant strain, greater than 90% osmolytic efficiency was observed upon osmotic downshock. A modified version of this strategy was applied to <jats:italic>E. coli<\/jats:italic> BL21 by deleting the <jats:italic>mscL<\/jats:italic> and <jats:italic>mscS<\/jats:italic> (small-conductance mechanosensitive channel) genes. When grown in medium with 4% NaCl and subsequently resuspended in distilled water, this engineered strain experienced 75% cell lysis, although decreases in cell growth rate due to higher salt concentrations were observed.<\/jats:p><br \/>\n              <\/jats:sec><jats:sec><br \/>\n                <jats:title>Conclusions<\/jats:title><br \/>\n                <jats:p>Our strategy is shown to be a simple and effective way to lyse cells for the purification of intracellular biomacromolecules and may be applicable in many bacteria used for bioproduction.<\/jats:p><br \/>\n              <\/jats:sec><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('413','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_413\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1186\/s12934-023-02064-8\" title=\"Follow DOI:10.1186\/s12934-023-02064-8\" target=\"_blank\">doi:10.1186\/s12934-023-02064-8<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('413','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Averesch, Nils J. H.;  Berliner, Aaron J.;  Nangle, Shannon N.;  Zezulka, Spencer;  Vengerova, Gretchen L.;  Ho, Davian;  Casale, Cameran A.;  Lehner, Benjamin A. E.;  Snyder, Jessica E.;  Clark, Kevin B.;  Dartnell, Lewis R.;  Criddle, Craig S.;  Arkin, Adam P.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('416','tp_links')\" style=\"cursor:pointer;\">Microbial biomanufacturing for space-exploration\u2014what to take and when to make<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Nat Commun, <\/span><span class=\"tp_pub_additional_volume\">vol. 14, <\/span><span class=\"tp_pub_additional_number\">no. 1, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2041-1723<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_416\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('416','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_416\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('416','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_416\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('416','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_416\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Averesch2023,<br \/>\r\ntitle = {Microbial biomanufacturing for space-exploration\u2014what to take and when to make},<br \/>\r\nauthor = {Nils J. H. Averesch and Aaron J. Berliner and Shannon N. Nangle and Spencer Zezulka and Gretchen L. Vengerova and Davian Ho and Cameran A. Casale and Benjamin A. E. Lehner and Jessica E. Snyder and Kevin B. Clark and Lewis R. Dartnell and Craig S. Criddle and Adam P. Arkin},<br \/>\r\ndoi = {10.1038\/s41467-023-37910-1},<br \/>\r\nissn = {2041-1723},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-12-00},<br \/>\r\njournal = {Nat Commun},<br \/>\r\nvolume = {14},<br \/>\r\nnumber = {1},<br \/>\r\npublisher = {Springer Science and Business Media LLC},<br \/>\r\nabstract = {<jats:title>Abstract<\/jats:title><jats:p>As renewed interest in human space-exploration intensifies, a coherent and modernized strategy for mission design and planning has become increasingly crucial. Biotechnology has emerged as a promising approach to increase resilience, flexibility, and efficiency of missions, by virtue of its ability to effectively utilize in situ resources and reclaim resources from waste streams. Here we outline four primary mission-classes on Moon and Mars that drive a staged and accretive biomanufacturing strategy. Each class requires a unique approach to integrate biomanufacturing into the existing mission-architecture and so faces unique challenges in technology development. These challenges stem directly from the resources available in a given mission-class\u2014the degree to which feedstocks are derived from cargo and in situ resources\u2014and the degree to which loop-closure is necessary. As mission duration and distance from Earth increase, the benefits of specialized, sustainable biomanufacturing processes also increase. Consequentially, we define specific design-scenarios and quantify the usefulness of in-space biomanufacturing, to guide techno-economics of space-missions. Especially materials emerged as a potentially pivotal target for biomanufacturing with large impact on up-mass cost. Subsequently, we outline the processes needed for development, testing, and deployment of requisite technologies. As space-related technology development often does, these advancements are likely to have profound implications for the creation of a resilient circular bioeconomy on Earth.<\/jats:p>},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('416','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_416\" style=\"display:none;\"><div class=\"tp_abstract_entry\"><jats:title>Abstract<\/jats:title><jats:p>As renewed interest in human space-exploration intensifies, a coherent and modernized strategy for mission design and planning has become increasingly crucial. Biotechnology has emerged as a promising approach to increase resilience, flexibility, and efficiency of missions, by virtue of its ability to effectively utilize in situ resources and reclaim resources from waste streams. Here we outline four primary mission-classes on Moon and Mars that drive a staged and accretive biomanufacturing strategy. Each class requires a unique approach to integrate biomanufacturing into the existing mission-architecture and so faces unique challenges in technology development. These challenges stem directly from the resources available in a given mission-class\u2014the degree to which feedstocks are derived from cargo and in situ resources\u2014and the degree to which loop-closure is necessary. As mission duration and distance from Earth increase, the benefits of specialized, sustainable biomanufacturing processes also increase. Consequentially, we define specific design-scenarios and quantify the usefulness of in-space biomanufacturing, to guide techno-economics of space-missions. Especially materials emerged as a potentially pivotal target for biomanufacturing with large impact on up-mass cost. Subsequently, we outline the processes needed for development, testing, and deployment of requisite technologies. As space-related technology development often does, these advancements are likely to have profound implications for the creation of a resilient circular bioeconomy on Earth.<\/jats:p><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('416','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_416\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1038\/s41467-023-37910-1\" title=\"Follow DOI:10.1038\/s41467-023-37910-1\" target=\"_blank\">doi:10.1038\/s41467-023-37910-1<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('416','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Makrygiorgos, Georgios;  Berliner, Aaron J.;  Shi, Fengzhe;  Clark, Douglas S.;  Arkin, Adam P.;  Mesbah, Ali<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('422','tp_links')\" style=\"cursor:pointer;\">Data\u2010driven flow\u2010map models for data\u2010efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Biotech &amp; Bioengineering, <\/span><span class=\"tp_pub_additional_volume\">vol. 120, <\/span><span class=\"tp_pub_additional_number\">no. 3, <\/span><span class=\"tp_pub_additional_pages\">pp. 803\u2013818, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1097-0290<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_422\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('422','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_422\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('422','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_422\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('422','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_422\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Makrygiorgos2023,<br \/>\r\ntitle = {Data\u2010driven flow\u2010map models for data\u2010efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems},<br \/>\r\nauthor = {Georgios Makrygiorgos and Aaron J. Berliner and Fengzhe Shi and Douglas S. Clark and Adam P. Arkin and Ali Mesbah},<br \/>\r\ndoi = {10.1002\/bit.28295},<br \/>\r\nissn = {1097-0290},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-03-00},<br \/>\r\njournal = {Biotech & Bioengineering},<br \/>\r\nvolume = {120},<br \/>\r\nnumber = {3},<br \/>\r\npages = {803--818},<br \/>\r\npublisher = {Wiley},<br \/>\r\nabstract = {<jats:title>Abstract<\/jats:title><jats:p>Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow\u2010map (de)compositions to present a framework that can address both of these challenges via learning data\u2010driven models useful for capturing the dynamical behavior of biochemical systems. Data\u2010driven flow\u2010map models seek to directly learn the integration operators of the governing differential equations in a black\u2010box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast\u2010to\u2010evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long\u2010term system dynamics via experimental observations. We present a data\u2010efficient approach to data\u2010driven flow\u2010map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data\u2010driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.<\/jats:p>},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('422','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_422\" style=\"display:none;\"><div class=\"tp_abstract_entry\"><jats:title>Abstract<\/jats:title><jats:p>Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow\u2010map (de)compositions to present a framework that can address both of these challenges via learning data\u2010driven models useful for capturing the dynamical behavior of biochemical systems. Data\u2010driven flow\u2010map models seek to directly learn the integration operators of the governing differential equations in a black\u2010box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast\u2010to\u2010evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long\u2010term system dynamics via experimental observations. We present a data\u2010efficient approach to data\u2010driven flow\u2010map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data\u2010driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.<\/jats:p><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('422','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_422\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1002\/bit.28295\" title=\"Follow DOI:10.1002\/bit.28295\" target=\"_blank\">doi:10.1002\/bit.28295<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('422','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">G. Berliner Makrygiorgos, A. J. Shi<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('398','tp_links')\" style=\"cursor:pointer;\">Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Biotechnol Bioeng, <\/span><span class=\"tp_pub_additional_volume\">vol. 120, <\/span><span class=\"tp_pub_additional_issue\">iss. 3, <\/span><span class=\"tp_pub_additional_pages\">pp. 803-818, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1097-0290 <\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_398\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('398','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_398\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('398','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_398\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('398','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_398\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{nokey,<br \/>\r\ntitle = {Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems},<br \/>\r\nauthor = {Makrygiorgos, G.<br \/>\r\nBerliner, A. J.<br \/>\r\nShi, F.<br \/>\r\nClark, D. S.<br \/>\r\nArkin, A. P.<br \/>\r\nMesbah, A.},<br \/>\r\ndoi = {10.1002\/bit.28295},<br \/>\r\nissn = {1097-0290 },<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-12-02},<br \/>\r\nurldate = {2022-12-02},<br \/>\r\njournal = {Biotechnol Bioeng},<br \/>\r\nvolume = {120},<br \/>\r\nissue = {3},<br \/>\r\npages = {803-818},<br \/>\r\nabstract = {Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('398','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_398\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('398','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_398\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1002\/bit.28295\" title=\"Follow DOI:10.1002\/bit.28295\" target=\"_blank\">doi:10.1002\/bit.28295<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('398','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Berliner, Aaron;  Makrygiorgos, George;  Hill, Avery<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('282','tp_links')\" style=\"cursor:pointer;\">Extension of Equivalent System Mass for Human Exploration Missions on Mars<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">preprints.org, <\/span><span class=\"tp_pub_additional_year\">2021<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_282\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('282','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_282\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('282','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_282\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Berliner2021,<br \/>\r\ntitle = {Extension of Equivalent System Mass for Human Exploration Missions on Mars},<br \/>\r\nauthor = {Aaron Berliner and George Makrygiorgos and Avery Hill},<br \/>\r\ndoi = {doi: 10.20944\/preprints202101.0363.v1},<br \/>\r\nyear  = {2021},<br \/>\r\ndate = {2021-01-01},<br \/>\r\njournal = {preprints.org},<br \/>\r\npublisher = {Preprints},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('282','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_282\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/doi: 10.20944\/preprints202101.0363.v1\" title=\"Follow DOI:doi: 10.20944\/preprints202101.0363.v1\" target=\"_blank\">doi:doi: 10.20944\/preprints202101.0363.v1<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('282','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yongao Xiong Matthew J. McNulty, Kevin Yates<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('269','tp_links')\" style=\"cursor:pointer;\">Molecular Pharming to Support Human Life on the Moon, Mars, and Beyond<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Preprints, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_269\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('269','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_269\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('269','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_269\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{McNulty2020,<br \/>\r\ntitle = {Molecular Pharming to Support Human Life on the Moon, Mars, and Beyond},<br \/>\r\nauthor = {Matthew J. McNulty , Yongao Xiong , Kevin Yates , Kalimuthu Karuppanan , Jacob M. Hilzinger , Aaron J. Berliner , Jesse Delzio , Adam P. Arkin , Nancy E. Lane , Somen Nandi , Karen A. McDonald },<br \/>\r\ndoi = {10.20944\/preprints202009.0086.v1},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-09-03},<br \/>\r\njournal = {Preprints},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('269','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_269\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.20944\/preprints202009.0086.v1\" title=\"Follow DOI:10.20944\/preprints202009.0086.v1\" target=\"_blank\">doi:10.20944\/preprints202009.0086.v1<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('269','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Berliner, Aaron;  Hilzinger, Jacob M;  Abel, Anthony J;  McNulty, Matthew;  Makrygiorgos, George;  Averesch, Nils J H;  Gupta, Soumyajit Sen;  Benvenuti, Alexander;  Caddell, Daniel;  Cestellos-Blanco, Stefano<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('283','tp_links')\" style=\"cursor:pointer;\">Towards a Biomanufactory on Mars<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">preprints.org, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_283\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('283','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_283\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('283','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_283\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Berliner2020,<br \/>\r\ntitle = {Towards a Biomanufactory on Mars},<br \/>\r\nauthor = {Aaron Berliner and Jacob M Hilzinger and Anthony J Abel and Matthew McNulty and George Makrygiorgos and Nils J H Averesch and Soumyajit Sen Gupta and Alexander Benvenuti and Daniel Caddell and Stefano Cestellos-Blanco},<br \/>\r\ndoi = {doi: 10.20944\/preprints202012.0714.v1},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\njournal = {preprints.org},<br \/>\r\npublisher = {Preprints},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('283','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_283\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/doi: 10.20944\/preprints202012.0714.v1\" title=\"Follow DOI:doi: 10.20944\/preprints202012.0714.v1\" target=\"_blank\">doi:doi: 10.20944\/preprints202012.0714.v1<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('283','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Abel, Anthony J;  Hilzinger, Jacob M;  Arkin, Adam P;  Clark, Douglas S<\/p><p class=\"tp_pub_title\">Systems-informed genome mining for electroautotrophic microbial production <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">bioRxiv, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_284\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('284','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_284\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Abel2020,<br \/>\r\ntitle = {Systems-informed genome mining for electroautotrophic microbial production},<br \/>\r\nauthor = {Anthony J Abel and Jacob M Hilzinger and Adam P Arkin and Douglas S Clark},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\njournal = {bioRxiv},<br \/>\r\npublisher = {Cold Spring Harbor Laboratory},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('284','tp_bibtex')\">Close<\/a><\/p><\/div><\/div><\/div><\/div><\/div>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"<p>The Center for the Utilization of Biological Engineering in Space (CUBES) was created to develop generalizable approaches to support biomanufacturing for deep space exploration that realizes the inherent mass, power, and volume advantages of space biotechnology over traditional abiotic approaches.<\/p>\n","protected":false},"author":1,"featured_media":1376,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[],"class_list":["post-485","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research"],"_links":{"self":[{"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/posts\/485","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/comments?post=485"}],"version-history":[{"count":17,"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/posts\/485\/revisions"}],"predecessor-version":[{"id":2451,"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/posts\/485\/revisions\/2451"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/media\/1376"}],"wp:attachment":[{"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/media?parent=485"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/categories?post=485"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/arkinlab.bio\/wp-json\/wp\/v2\/tags?post=485"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}