Activation in Vesicle-Mediated Signaling Shaped by Batch Arrival Statistics

[2605.06456] Activation in Vesicle-Mediated Signaling Shaped by Batch Arrival Statistics Skip to main content Search Submit Donate Log in Condensed Matter > Statistical Mechanics arXiv:2605.06456 (cond-mat) [Submitted on 7 May 2026 ( v1 ), last revised 8 Jul 2026 (this version, v2)] Title: Activation in Vesicle-Mediated Signaling Shaped by Batch Arrival Statistics Authors: Jan Hauke , Julian B. Voits , Ulrich S. Schwarz (Heidelberg University) View PDF HTML (experimental) Abstract: Vesicle-mediated secretion of ions or molecules is a central mechanism of cellular communication, for example in processes such as neurotransmission or hormone release. These events are inherently stochastic: vesicle fusions lead to bursts of variable sizes, releasing discrete packets of transmitters that are subsequently cleared or degraded. The dynamics are intrinsically time-directed due to the interplay of spontaneous bursts and continuous degradation. Using generating functions and a recursion relation, we derive an exact solution for the full time-dependent probability distribution of a general batch arrival degradation model. This framework also enables a full analysis of first-passage times to a concentration threshold representing downstream activation. We show that activation kinetics are not determined by mean dynamics alone, but depend sensitively on the temporal statistics of arrival events, batch-size variability, and degradation. In particular, different arrival processes with identical mean rates can lead to qualitatively distinct first passage behavior, reflecting the role of time-asymmetric fluctuations. We also discuss extensions incorporating vesicle depletion. Our results provide a transparent link between stochastic release dynamics and activation timing in vesicle-mediated signaling. Comments: 16 pages, 7 figures, supplement with 17 pages and 1 figure Subjects: Statistical Mechanics (cond-mat.stat-mech) ; Molecular Networks (q-bio.MN); Subcellular Processes (q-bio.SC) Cite as: arXiv:2605.06456 [cond-mat.stat-mech] (or arXiv:2605.06456v2 [cond-mat.stat-mech] for this version) https://doi.org/10.48550/arXiv.2605.06456 Focus to learn more arXiv-issued DOI via DataCite Journal reference: New J. Phys. 28 (2026) 083301 Related DOI : https://doi.org/10.1088/1367-2630/ae98a6 Focus to learn more DOI(s) linking to related resources Submission history From: Ulrich S. Schwarz [ view email ] [v1] Thu, 7 May 2026 15:50:13 UTC (1,767 KB) [v2] Wed, 8 Jul 2026 13:37:24 UTC (2,025 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cond-mat.stat-mech < prev | next > new | recent | 2026-05 Change to browse by: cond-mat q-bio q-bio.MN q-bio.SC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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