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        <identifier>oai:figshare.com:article/33967926</identifier>
        <datestamp>2026-09-22T17:39:02Z</datestamp>
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        <oai_dc:dc xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"  xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:title>&lt;p&gt;Environmental and simulation parameters.&lt;/p&gt;</dc:title>
          <dc:creator>David M. Versluis (23672270)</dc:creator>
          <dc:creator>Robert H. Insall (9199791)</dc:creator>
          <dc:subject>Biophysics</dc:subject>
          <dc:subject>Physical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Cell Biology</dc:subject>
          <dc:subject>Molecular Biology</dc:subject>
          <dc:subject>Neuroscience</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Ecology</dc:subject>
          <dc:subject>Immunology</dc:subject>
          <dc:subject>Developmental Biology</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>yet remarkably independent</dc:subject>
          <dc:subject>secondary signalling layer</dc:subject>
          <dc:subject>remain poorly understood</dc:subject>
          <dc:subject>inherent instability rather</dc:subject>
          <dc:subject>inherent chemical instability</dc:subject>
          <dc:subject>excessive signalling drives</dc:subject>
          <dc:subject>optimal lifetimes exist</dc:subject>
          <dc:subject>limit collective migration</dc:subject>
          <dc:subject>directional information cells</dc:subject>
          <dc:subject>primary attractant concentration</dc:subject>
          <dc:subject>based computational model</dc:subject>
          <dc:subject>autoattractants whose removal</dc:subject>
          <dc:subject>autoattractants shape migration</dc:subject>
          <dc:subject>primary attractant</dc:subject>
          <dc:subject>longer lifetimes</dc:subject>
          <dc:subject>collective chemotaxis</dc:subject>
          <dc:subject>immune cells</dc:subject>
          <dc:subject>disperse cells</dc:subject>
          <dc:subject>trigger aggregation</dc:subject>
          <dc:subject>sufficiently short</dc:subject>
          <dc:subject>overall effect</dc:subject>
          <dc:subject>moving aggregates</dc:subject>
          <dc:subject>mediated breakdown</dc:subject>
          <dc:subject>hybrid agent</dc:subject>
          <dc:subject>evolution either</dc:subject>
          <dc:subject>enzymatic breakdown</dc:subject>
          <dc:subject>critical boundary</dc:subject>
          <dc:subject>cellular uptake</dc:subject>
          <dc:subject>autoattractants create</dc:subject>
          <dc:subject>also attracted</dc:subject>
          <dc:subject>accumulation degrades</dc:subject>
          <dc:description>&lt;div&gt;&lt;p&gt;Many eukaryotic cells produce attractant molecules to which they themselves are also attracted. For example, neutrophils produce leukotriene B4 while swarming. These autoattractants create a secondary signalling layer that coordinates collective cell behaviour. The principles by which autoattractants shape migration, however, remain poorly understood. Here we use a hybrid agent-based computational model to dissect the effects of autoattractants on the collective chemotaxis of immune cells. We find that autoattractant signals strongly enhance cells’ responses to primary attractant, but only if the autoattractant is sufficiently short-lived: at longer lifetimes, accumulation degrades the directional information cells can extract. The overall effect of autoattractants therefore depends crucially on the balance between production and efficient removal — whether through cellular uptake, enzymatic breakdown, or inherent chemical instability. Optimal lifetimes exist, determined by cell speed and autoattractant diffusion, yet remarkably independent of cell density and primary attractant concentration. Autoattractants whose removal is governed by inherent instability rather than direct cellular breakdown coordinate migration less efficiently, but work more robustly across different environments. In the absence of cell-mediated breakdown, the model further reveals a characteristic optimal cell–cell distance: too little communication leaves cells uncoordinated, while excessive signalling drives them into slow-moving aggregates. Strikingly, the conditions that produce the most efficient chemotaxis lie close to those that trigger aggregation, suggesting that many autoattractant systems operate near a critical boundary that can be tuned by evolution either to disperse cells or to bring them together.&lt;/p&gt;&lt;/div&gt;</dc:description>
          <dc:date>2026-09-22T17:56:13Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pcbi.1014809.t001</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/_p_Environmental_and_simulation_parameters_p_/33967926</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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