---
title: "PINN-iPRC: Inferring infinitesimal phase response curves from finite-pulse circadian data"
id: "biorxiv-3-a-physics-informed-neural-network-framework-for-inferring-infinitesimal-phase"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-3-a-physics-informed-neural-network-framework-for-inferring-infinitesimal-phase"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.20.760317v1?rss=1"
published_at: "2026-10-24T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# PINN-iPRC: Inferring infinitesimal phase response curves from finite-pulse circadian data
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-3-a-physics-informed-neural-network-framework-for-inferring-infinitesimal-phase
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.20.760317v1?rss=1)
- **Published At:** 2026-10-24T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors introduce **PINN-iPRC**, a physics-informed neural network framework that infers the **infinitesimal phase response curve** (iPRC) from standard finite-pulse stimulus-response recordings without requiring specialized population-level resetting experiments. - PINN-iPRC treats the unknown iPRC as a neural network and embeds the phase evolution equation into the loss function; the network is trained to identify the iPRC that produces the observed transient phase trajectory under a known stimulus. - In synthetic oscillator tests, investigators reported accurate recovery of iPRCs, including complex shapes that simpler parametric methods struggle to capture. - Applied to published Arabidopsis circadian recordings, the study team observed iPRCs comparable to those derived from specialized experiments, while using only conventional stimulus-response trajectories. - The inferred iPRCs were used to predict finite-duration PRCs and circadian peak phases across different photoperiods; predictions included near-invariance of the prr7 peak phase across 8–16 h darkness, consistent with an independent published dataset. - The authors propose applications in experimental design, entrainment prediction, and chronotherapeutic or pharmacological schedule optimization, but the source did not report details such as neural network architecture, hyperparameters, or training regimen. - Competing interests were declared as none; funders included the Institute for Basic Science, Samsung Science and Technology Foundation, and the National Research Foundation of Korea.
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Dongwoo Won 1 Department of Mathematical Sciences, KAIST; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Dongwoo%2BWon%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Won%20D&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ADongwoo%2BWon%2B) Gyuyoung Hwang 2 Biomedical Mathematics Group, Institute for Basic Science * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Gyuyoung%2BHwang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Hwang%20G&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AGyuyoung%2BHwang%2B) * [ORCID record for Gyuyoung Hwang](http://orcid.org/0009-0008-7552-6636 "Open in new tab") Jae Kyoung Kim 1 Department of Mathematical Sciences, KAIST; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jae%2BKyoung%2BKim%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Kim%20JK&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJae%2BKyoung%2BKim%2B) * [ORCID record for Jae Kyoung Kim](http://orcid.org/0000-0001-7842-2172 "Open in new tab") * For correspondence: jaekkim@kaist.ac.kr * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.20.760317v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5900171/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.20.760317v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5900171/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.20.760317v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5900171/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.20.760317v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5900171/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Biological clocks reset and entrain to external cues in a phase-dependent manner. The infinitesimal phase response curve (iPRC) provides the local dynamical rule underlying this process by quantifying how an input changes the instantaneous phase velocity of an oscillator. Inferring iPRCs would make it possible to predict how rhythms respond to new stimulus timings and durations, but existing approaches often require detailed mechanistic models, long perturbation recordings, or specialized population-level resetting experiments. Here we introduce Physics-informed neural network for iPRC inference (PINN-iPRC), a physics-informed neural network framework for inferring iPRCs from standard finite-pulse stimulus-response recordings. PINN-iPRC represents the unknown iPRC as a neural network and embeds the phase equation into the loss function, identifying the iPRC that leads to the observed transient phase trajectory under the known stimulus. In synthetic oscillators, PINN-iPRC accurately recovered iPRCs from finite-pulse recordings, including complex ones that are difficult to capture with simpler parametric reconstructions. Applied to existing experimental Arabidopsis circadian recordings, PINN-iPRC recovered iPRCs comparable to those obtained from specialized population-level resetting experiments, while requiring only conventional stimulus-response trajectories. The inferred iPRCs were further used to predict finite-duration PRCs and circadian peak phases across photoperiods. In particular, the predicted near-invariance of the prr7 peak phase across 8-16 h of darkness was consistent with independently published prr7-11 root recordings. These results support PINN-iPRC as a practical physics-informed approach for extracting interpretable phase-sensitivity functions from routine biological oscillator recordings, with potential applications in experimental design, entrainment prediction, and chronotherapeutic or pharmacological schedule optimization. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared Institute for Basic Science, https://ror.org/00y0zf565, IBS-R029-C3, J. K. K. Samsung Science and Technology Foundation (South Korea), https://ror.org/00gjmje40, SRFC-IT2601-01, J. K. K. National Research Foundation of Korea, https://ror.org/013aysd81, RS-2026-25613293, J. K. K. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.20.760317v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/24/2026.10.20.760317.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) A physics-informed neural network framework for inferring infinitesimal phase response curves from finite-pulse circadian recordings Dongwoo Won, Gyuyoung Hwang, Jae Kyoung Kim bioRxiv 2026.10.20.760317; doi: https://doi.org/10.64898/2026.10.20.760317 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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