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class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Ruxandra Tonea</h3></div><div class="js-work-strip profile--work_container" data-work-id="123793839"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/123793839/Abstract_164_Genetic_elimination_of_%CE%B2_catenin_using_a_doxycycline_regulated_GEM_model_fails_to_restore_checkpoint_blockade_efficacy_due_to_persistent_M2_like_macrophages"><img alt="Research paper thumbnail of Abstract 164: Genetic elimination of β-catenin using a doxycycline-regulated GEM model fails to restore checkpoint blockade efficacy due to persistent M2-like macrophages" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Abstract 164: Genetic elimination of β-catenin using a doxycycline-regulated GEM model fails to restore checkpoint blockade efficacy due to persistent M2-like macrophages</div><div class="wp-workCard_item"><span>Cancer research</span><span>, Mar 22, 2024</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793839"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count 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alt="Research paper thumbnail of Mo1412: SEROTONIN OF THE INTESTINAL MUCOSA ALLEVIATES ANXIETY AND DEPRESSION INDEPENDENTLY OF GASTROINTESTINAL FUNCTION AND THE ENS" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Mo1412: SEROTONIN OF THE INTESTINAL MUCOSA ALLEVIATES ANXIETY AND DEPRESSION INDEPENDENTLY OF GASTROINTESTINAL FUNCTION AND THE ENS</div><div class="wp-workCard_item"><span>Gastroenterology</span><span>, May 1, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793838"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa 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Tonea","url":"https://independent.academia.edu/ToneaR"},"attachments":[],"research_interests":[{"id":3274,"name":"Gastroenterology","url":"https://www.academia.edu/Documents/in/Gastroenterology"},{"id":38676,"name":"Anxiety","url":"https://www.academia.edu/Documents/in/Anxiety"},{"id":51565,"name":"Serotonin","url":"https://www.academia.edu/Documents/in/Serotonin"},{"id":71471,"name":"Intestinal Mucosa","url":"https://www.academia.edu/Documents/in/Intestinal_Mucosa"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences"},{"id":623821,"name":"ANXIETY","url":"https://www.academia.edu/Documents/in/ANXIETY-1"},{"id":1239755,"name":"Neurosciences","url":"https://www.academia.edu/Documents/in/Neurosciences"},{"id":3789883,"name":"Paediatrics and reproductive medicine","url":"https://www.academia.edu/Documents/in/Paediatrics_and_reproductive_medicine"}],"urls":[{"id":44618611,"url":"https://doi.org/10.1016/s0016-5085(22)61803-1"}]}, 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href="https://www.academia.edu/123793837/Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs">Engineered bacterial swarm patterns as spatial records of environmental inputs</a></div><div class="wp-workCard_item"><span>bioRxiv (Cold Spring Harbor Laboratory)</span><span>, Jan 21, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A diverse array of bacteria species naturally self-organize into durable macroscale patterns on s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated, rapid movement of bacteria powered by flagella 1-5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to "write" external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from endpoint images using a segmentation model. This work creates an approach for building a macroscale bacterial recorder and expands the framework for engineering emergent microbial behaviors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7a54e6398c8bdcb69ffa3926e8576b08" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118143857,"asset_id":123793837,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118143857/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793837"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123793837"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123793837; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123793837]").text(description); $(".js-view-count[data-work-id=123793837]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123793837; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123793837']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7a54e6398c8bdcb69ffa3926e8576b08" } } $('.js-work-strip[data-work-id=123793837]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123793837,"title":"Engineered bacterial swarm patterns as spatial records of environmental inputs","translated_title":"","metadata":{"publisher":"Cold Spring Harbor Laboratory","grobid_abstract":"A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated, rapid movement of bacteria powered by flagella 1-5. 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Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring b...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2792d011ec1894cdbce7b6a7bcb14c07" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118143872,"asset_id":123793835,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118143872/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793835"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123793835"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123793835; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123793835]").text(description); $(".js-view-count[data-work-id=123793835]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123793835; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123793835']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2792d011ec1894cdbce7b6a7bcb14c07" } } $('.js-work-strip[data-work-id=123793835]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123793835,"title":"A deep learning pipeline for segmentation of Proteus mirabilis colony patterns","translated_title":"","metadata":{"abstract":"ABSTRACTThe motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. 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Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. 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Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to “write” external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from end...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8faef3aad3ded4718223f679d2dc1448" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118143873,"asset_id":123793833,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118143873/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793833"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123793833"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123793833; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123793833]").text(description); $(".js-view-count[data-work-id=123793833]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123793833; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123793833']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8faef3aad3ded4718223f679d2dc1448" } } $('.js-work-strip[data-work-id=123793833]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123793833,"title":"Engineered bacterial swarm patterns as spatial records of environmental inputs","translated_title":"","metadata":{"abstract":"A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella1–5. 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Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123793837"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123793837/Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs"><img alt="Research paper thumbnail of Engineered bacterial swarm patterns as spatial records of environmental inputs" class="work-thumbnail" src="https://attachments.academia-assets.com/118143857/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123793837/Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs">Engineered bacterial swarm patterns as spatial records of environmental inputs</a></div><div class="wp-workCard_item"><span>bioRxiv (Cold Spring Harbor Laboratory)</span><span>, Jan 21, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A diverse array of bacteria species naturally self-organize into durable macroscale patterns on s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated, rapid movement of bacteria powered by flagella 1-5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to "write" external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from endpoint images using a segmentation model. This work creates an approach for building a macroscale bacterial recorder and expands the framework for engineering emergent microbial behaviors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7a54e6398c8bdcb69ffa3926e8576b08" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118143857,"asset_id":123793837,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118143857/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793837"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123793837"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123793837; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123793837]").text(description); $(".js-view-count[data-work-id=123793837]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123793837; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123793837']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7a54e6398c8bdcb69ffa3926e8576b08" } } $('.js-work-strip[data-work-id=123793837]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123793837,"title":"Engineered bacterial swarm patterns as spatial records of environmental inputs","translated_title":"","metadata":{"publisher":"Cold Spring Harbor Laboratory","grobid_abstract":"A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated, rapid movement of bacteria powered by flagella 1-5. 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This work creates an approach for building a macroscale bacterial recorder and expands the framework for engineering emergent microbial behaviors.","publication_date":{"day":21,"month":1,"year":2022,"errors":{}},"publication_name":"bioRxiv (Cold Spring Harbor Laboratory)","grobid_abstract_attachment_id":118143857},"translated_abstract":null,"internal_url":"https://www.academia.edu/123793837/Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs","translated_internal_url":"","created_at":"2024-09-11T17:46:09.603-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":314912081,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118143857,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118143857/thumbnails/1.jpg","file_name":"2022.01.20.477106.full.pdf","download_url":"https://www.academia.edu/attachments/118143857/download_file","bulk_download_file_name":"Engineered_bacterial_swarm_patterns_as_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118143857/2022.01.20.477106.full-libre.pdf?1726105850=\u0026response-content-disposition=attachment%3B+filename%3DEngineered_bacterial_swarm_patterns_as_s.pdf\u0026Expires=1743087235\u0026Signature=b8ftSEraGhgIY3JlmILcxDuHIR9-gWerdyPO~Ql722aGv~QpbKmbvjm5NQgVZ4YYHF5QJENb5JRJjUlMmaRnNUeRXu596t8D5CP9yN7LXlVKUvDw~2CfTLQhiqUjHb~nB3a1O27sZtAbvk0GXkRMy~No7DJ63c8~2Wqwff7uEy8bcUs7nMYLK5582FfJ6zj-ou6uhOuogFEfDeaCGTK1pQ12BnqiUJ8IJj0Kq2~-YBajqwKexp28uk9cL4PcJoqKnkHVChV~Rz9vwymRs3Cw13AOHERYQZGFzdk4jvkB~LHD5ZQcxaAxZObSHkA3gGv8fenEc25iotGEHlZc82wmZA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs","translated_slug":"","page_count":25,"language":"en","content_type":"Work","summary":"A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated, rapid movement of bacteria powered by flagella 1-5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to \"write\" external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from endpoint images using a segmentation model. This work creates an approach for building a macroscale bacterial recorder and expands the framework for engineering emergent microbial behaviors.","owner":{"id":314912081,"first_name":"Ruxandra","middle_initials":null,"last_name":"Tonea","page_name":"ToneaR","domain_name":"independent","created_at":"2024-05-25T21:30:38.751-07:00","display_name":"Ruxandra Tonea","url":"https://independent.academia.edu/ToneaR"},"attachments":[{"id":118143857,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118143857/thumbnails/1.jpg","file_name":"2022.01.20.477106.full.pdf","download_url":"https://www.academia.edu/attachments/118143857/download_file","bulk_download_file_name":"Engineered_bacterial_swarm_patterns_as_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118143857/2022.01.20.477106.full-libre.pdf?1726105850=\u0026response-content-disposition=attachment%3B+filename%3DEngineered_bacterial_swarm_patterns_as_s.pdf\u0026Expires=1743087235\u0026Signature=b8ftSEraGhgIY3JlmILcxDuHIR9-gWerdyPO~Ql722aGv~QpbKmbvjm5NQgVZ4YYHF5QJENb5JRJjUlMmaRnNUeRXu596t8D5CP9yN7LXlVKUvDw~2CfTLQhiqUjHb~nB3a1O27sZtAbvk0GXkRMy~No7DJ63c8~2Wqwff7uEy8bcUs7nMYLK5582FfJ6zj-ou6uhOuogFEfDeaCGTK1pQ12BnqiUJ8IJj0Kq2~-YBajqwKexp28uk9cL4PcJoqKnkHVChV~Rz9vwymRs3Cw13AOHERYQZGFzdk4jvkB~LHD5ZQcxaAxZObSHkA3gGv8fenEc25iotGEHlZc82wmZA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":118143855,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118143855/thumbnails/1.jpg","file_name":"2022.01.20.477106.full.pdf","download_url":"https://www.academia.edu/attachments/118143855/download_file","bulk_download_file_name":"Engineered_bacterial_swarm_patterns_as_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118143855/2022.01.20.477106.full-libre.pdf?1726105843=\u0026response-content-disposition=attachment%3B+filename%3DEngineered_bacterial_swarm_patterns_as_s.pdf\u0026Expires=1743087235\u0026Signature=TnGfdoaZjLCbipce72k739Hg5G1hCwPd4wUOp1S1q-ZPtla4qNc6Ab9weQtgJL4583Bw5dWzh-nMq6X7ftCkRMSe5hEeqsE4ZDkhDiE-xTFFUohIz28heT1w1jfALwi6xi3MpX5-RwYYC83BwB~W~iJfgfEL7e0rwvG3T2Z7O-PX7IOoc99sHiPlcwLk8cGIp~a7-hLkDEe4mfzft1qX9H7gp4R5h9n0RqXMe13NZLEhIFs8sAV3An6vmtjXhyfoFHy3JxOf74~QfgSkR1NvwU8Acup4eAicRN18dPmDwR3DhMxzdrTJYDD-jxE59QJQ2RoE0htAQ026oRjAeqQbcQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":210122,"name":"Robustness (evolution)","url":"https://www.academia.edu/Documents/in/Robustness_evolution_"},{"id":2752141,"name":"Swarm Behaviour","url":"https://www.academia.edu/Documents/in/Swarm_Behaviour"},{"id":3801335,"name":"swarming motility","url":"https://www.academia.edu/Documents/in/swarming_motility"}],"urls":[{"id":44618610,"url":"https://www.biorxiv.org/content/biorxiv/early/2022/01/21/2022.01.20.477106.full.pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123793835"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123793835/A_deep_learning_pipeline_for_segmentation_of_Proteus_mirabilis_colony_patterns"><img alt="Research paper thumbnail of A deep learning pipeline for segmentation of Proteus mirabilis colony patterns" class="work-thumbnail" src="https://attachments.academia-assets.com/118143872/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123793835/A_deep_learning_pipeline_for_segmentation_of_Proteus_mirabilis_colony_patterns">A deep learning pipeline for segmentation of Proteus mirabilis colony patterns</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACTThe motility mechanisms of microorganisms are critical virulence factors, enabling their ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACTThe motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring b...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2792d011ec1894cdbce7b6a7bcb14c07" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118143872,"asset_id":123793835,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118143872/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793835"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123793835"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123793835; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123793835]").text(description); $(".js-view-count[data-work-id=123793835]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123793835; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123793835']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2792d011ec1894cdbce7b6a7bcb14c07" } } $('.js-work-strip[data-work-id=123793835]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123793835,"title":"A deep learning pipeline for segmentation of Proteus mirabilis colony patterns","translated_title":"","metadata":{"abstract":"ABSTRACTThe motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring b...","publisher":"Cold Spring Harbor Laboratory","ai_title_tag":"Deep Learning for Proteus mirabilis Colony Segmentation","publication_date":{"day":null,"month":null,"year":2022,"errors":{}}},"translated_abstract":"ABSTRACTThe motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring b...","internal_url":"https://www.academia.edu/123793835/A_deep_learning_pipeline_for_segmentation_of_Proteus_mirabilis_colony_patterns","translated_internal_url":"","created_at":"2024-09-11T17:46:08.941-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":314912081,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118143872,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118143872/thumbnails/1.jpg","file_name":"Doshi_biorxiv_2022.pdf","download_url":"https://www.academia.edu/attachments/118143872/download_file","bulk_download_file_name":"A_deep_learning_pipeline_for_segmentatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118143872/Doshi_biorxiv_2022-libre.pdf?1726105798=\u0026response-content-disposition=attachment%3B+filename%3DA_deep_learning_pipeline_for_segmentatio.pdf\u0026Expires=1743087235\u0026Signature=GdXEGvLuceYm3ALqr-I42FQcGWhqIZM-VxgAnaJf4TrQ0kJMZMMekm5IKw5A0PKH9aoXT3IuPdYkvcsExilvyZlJPlMpI3TU9ea9aooE09331KE1t53t9V69ZZjgNLeEYksxj~rKBv3kpYLOnpjLezfpfQJYyENyogCfrqWAXvwDikSftZWyuc1LB98qJknwbT~hWF-q8Jeu0cEzFxdANxWfnPf4ikLr~pVNkjpxyDhQplOWf4ea5RdlsWrfJgKpz~Rv~iCULqSwWUHLFvhrmo2RSU6hi8282Szq4JWOm9Y5r-vkObdfL2UQCGamT5v~~TIVo0ChBoviIXf-4L16Bg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_deep_learning_pipeline_for_segmentation_of_Proteus_mirabilis_colony_patterns","translated_slug":"","page_count":7,"language":"en","content_type":"Work","summary":"ABSTRACTThe motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring b...","owner":{"id":314912081,"first_name":"Ruxandra","middle_initials":null,"last_name":"Tonea","page_name":"ToneaR","domain_name":"independent","created_at":"2024-05-25T21:30:38.751-07:00","display_name":"Ruxandra Tonea","url":"https://independent.academia.edu/ToneaR"},"attachments":[{"id":118143872,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118143872/thumbnails/1.jpg","file_name":"Doshi_biorxiv_2022.pdf","download_url":"https://www.academia.edu/attachments/118143872/download_file","bulk_download_file_name":"A_deep_learning_pipeline_for_segmentatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118143872/Doshi_biorxiv_2022-libre.pdf?1726105798=\u0026response-content-disposition=attachment%3B+filename%3DA_deep_learning_pipeline_for_segmentatio.pdf\u0026Expires=1743087235\u0026Signature=GdXEGvLuceYm3ALqr-I42FQcGWhqIZM-VxgAnaJf4TrQ0kJMZMMekm5IKw5A0PKH9aoXT3IuPdYkvcsExilvyZlJPlMpI3TU9ea9aooE09331KE1t53t9V69ZZjgNLeEYksxj~rKBv3kpYLOnpjLezfpfQJYyENyogCfrqWAXvwDikSftZWyuc1LB98qJknwbT~hWF-q8Jeu0cEzFxdANxWfnPf4ikLr~pVNkjpxyDhQplOWf4ea5RdlsWrfJgKpz~Rv~iCULqSwWUHLFvhrmo2RSU6hi8282Szq4JWOm9Y5r-vkObdfL2UQCGamT5v~~TIVo0ChBoviIXf-4L16Bg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology"},{"id":93217,"name":"Segmentation","url":"https://www.academia.edu/Documents/in/Segmentation"},{"id":1463687,"name":"Proteus Mirabilis","url":"https://www.academia.edu/Documents/in/Proteus_Mirabilis"},{"id":2752141,"name":"Swarm Behaviour","url":"https://www.academia.edu/Documents/in/Swarm_Behaviour"}],"urls":[{"id":44618608,"url":"https://syndication.highwire.org/content/doi/10.1101/2022.01.17.475672"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123793833"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123793833/Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs"><img alt="Research paper thumbnail of Engineered bacterial swarm patterns as spatial records of environmental inputs" class="work-thumbnail" src="https://attachments.academia-assets.com/118143873/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123793833/Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs">Engineered bacterial swarm patterns as spatial records of environmental inputs</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A diverse array of bacteria species naturally self-organize into durable macroscale patterns on s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella1–5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to “write” external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from end...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8faef3aad3ded4718223f679d2dc1448" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118143873,"asset_id":123793833,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118143873/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793833"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123793833"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123793833; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123793833]").text(description); $(".js-view-count[data-work-id=123793833]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123793833; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123793833']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8faef3aad3ded4718223f679d2dc1448" } } $('.js-work-strip[data-work-id=123793833]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123793833,"title":"Engineered bacterial swarm patterns as spatial records of environmental inputs","translated_title":"","metadata":{"abstract":"A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella1–5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to “write” external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from end...","publisher":"Cold Spring Harbor Laboratory","ai_title_tag":"Bacterial Swarm Patterns as Environmental Sensors","publication_date":{"day":null,"month":null,"year":2022,"errors":{}}},"translated_abstract":"A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella1–5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to “write” external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. Lastly, we show a growing colony can record dynamic environmental changes, which can be decoded from end...","internal_url":"https://www.academia.edu/123793833/Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs","translated_internal_url":"","created_at":"2024-09-11T17:46:07.689-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":314912081,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":118143873,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118143873/thumbnails/1.jpg","file_name":"2022.01.20.477106.full.pdf","download_url":"https://www.academia.edu/attachments/118143873/download_file","bulk_download_file_name":"Engineered_bacterial_swarm_patterns_as_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118143873/2022.01.20.477106.full-libre.pdf?1726105839=\u0026response-content-disposition=attachment%3B+filename%3DEngineered_bacterial_swarm_patterns_as_s.pdf\u0026Expires=1743087235\u0026Signature=M79Oo7GIV3sbRweiVCf5gUXNY~4pjWsVlkV3TDSWL13G7ugZb7NLOByXyQwa4T-qYOKLciHc7QXNI2aY3RLYRa38N73-aCwcLywdD-aKXapOeC7OAc4dlMk3gZd-ifTgsCQ0VuQ24ywmMp7ErVw0MfvtwvDWrW67XmP6p2wUttWvlOvAby5-2Hj1LNfPzKEWQMisuW0nt9saBLEx775s2XCI5tbneC5PSRCM5WpK~3LjogF-arm~KYaiCGCopluk0RLn5ZdFkQl2bN-1RGFOG8Lk5L05gyzbrEE~4J5PAEOTLIvDPV3HDYDpY-fC4EnsBVo5t1Ar5ClOJ3zBtWctgQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Engineered_bacterial_swarm_patterns_as_spatial_records_of_environmental_inputs","translated_slug":"","page_count":25,"language":"en","content_type":"Work","summary":"A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated, rapid movement of bacteria powered by flagella1–5. Engineering swarming behaviors is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye patterns on solid agar through swarming, to “write” external inputs into a visible spatial record. Specifically, we engineer tunable expression of swarming-related genes that accordingly modify pattern features, and develop quantitative approaches to decode input conditions. Next, we develop a two-input system that modulates two swarming-related genes simultaneously, and show the resulting patterns can be interpreted using a deep learning classification model. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123793831"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/123793831/Immunotherapy_activated_T_cells_recruit_and_skewlate_stage_activated_M1_like_macrophagesthat_are_critical_for_therapeutic_efficacy"><img alt="Research paper thumbnail of Immunotherapy-activated T cells recruit and skewlate-stage activated M1-like macrophagesthat are critical for therapeutic efficacy" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Immunotherapy-activated T cells recruit and skewlate-stage activated M1-like macrophagesthat are critical for therapeutic efficacy</div><div class="wp-workCard_item"><span>Cancer cell</span><span>, May 1, 2024</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123793831"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123793831"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123793831; 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