{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T19:06:55Z","timestamp":1788894415127,"version":"build-2803163510"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014608","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000}}],"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2023\/51\/B\/NZ6\/02951"],"award-info":[{"award-number":["2023\/51\/B\/NZ6\/02951"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000152","name":"Division of Molecular and Cellular Biosciences","doi-asserted-by":"publisher","award":["MCB-2231765"],"award-info":[{"award-number":["MCB-2231765"]}],"id":[{"id":"10.13039\/100000152","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01 GM123011"],"award-info":[{"award-number":["R01 GM123011"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Cells make hard calls under noise. When signaling is abnormal, those calls can go wrong and drive pathological conditions and diseases. In this research, we develop a Neyman\u2013Pearson (NP) detection-theory framework that maximizes probability of detection (\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    ) for a chosen false alarm probability (\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>FA<\/jats:sub>\n                    ), without requiring prior probabilities, using experimental single-cell measurements of NF-\u03baB responses to tumor necrosis factor (TNF), a critical pathway involved in cell survival, apoptosis, immune signaling, and stress response, in wild-type and A20-deficient fibroblasts. We model log-responses as (multi)variate Gaussian and compute optimal thresholds,\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    \u2013\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>FA<\/jats:sub>\n                    trade-offs, and ROC curves at 30 minutes and 4 hours. The NP framework captures expected biology:\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    increases with TNF dose; wild-type cells outperform A20\n                    <jats:sup>-\/-<\/jats:sup>\n                    at matched conditions; and combining two time points (bivariate analysis) improves detection (e.g., for 0.0052 vs. 0.2 ng\/mL,\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    rises from 0.71 (30 minutes) and 0.42 (4 hours) to 0.80 at\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>FA<\/jats:sub>\n                    \u2009=\u20090.1). The analysis recovers expected biology (higher TNF causes higher detectability; negative feedback lowers late responses) and flags cases where decision quality degrades (e.g., perturbations that blunt separation between conditions). The same recipe extends to multivariate readouts without changing the logic. A non-parametric (kernel-density) detector applied to the raw single-cell data reproduces these detection probabilities (|\u0394\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    |\u2009\u2264\u20090.092), confirming the conclusions do not rely on the Gaussian approximation. Overall, the NP detection framework provides a compact, quantitative score of pathway performance and failure. It turns noisy single-cell readouts into actionable decision metrics that compare doses, time points, and perturbations, and ultimately, can help explain when and how cellular decisions drift toward pathology.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014608","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T17:40:44Z","timestamp":1786556444000},"page":"e1014608","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the reliability of cellular decision making from noisy, multidimensional single-cell TNF\u2013NF-\u03baB signaling data"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2224-8854","authenticated-orcid":true,"given":"Ali","family":"Emadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3488-2561","authenticated-orcid":true,"given":"Tomasz","family":"Lipniacki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6262-1222","authenticated-orcid":true,"given":"Andre","family":"Levchenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Abdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"issue":"5","key":"pcbi.1014608.ref001","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1093\/intbio\/zyac011","article-title":"A hybrid model to study how late long-term potentiation is affected by faulty molecules in an intraneuronal signaling network regulating transcription factor CREB","volume":"14","author":"A Emadi","year":"2022","journal-title":"Integr Biol (Camb)"},{"key":"pcbi.1014608.ref002","doi-asserted-by":"crossref","unstructured":"Emadi A, Abdi A. 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