Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history

[2506.09374] Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Quantitative Methods arXiv:2506.09374 (q-bio) [Submitted on 11 Jun 2025 ( v1 ), last revised 2 Jul 2025 (this version, v3)] Title: Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history Authors: Pedro Pessoa , Juan Andres Martinez , Vincent Vandenbroucke , Frank Delvigne , Steve Pressé View a PDF of the paper titled Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history, by Pedro Pessoa and 4 other authors View PDF HTML (experimental) Abstract: Inferring protein production kinetics for dividing cells is complicated due to protein inheritance from the mother cell. For instance, fluorescence measurements -- commonly used to assess gene activation -- may reflect not only newly produced proteins but also those inherited through successive cell divisions. In such cases, observed protein levels in any given cell are shaped by its division history. As a case study, we examine activation of the glc3 gene in yeast involved in glycogen synthesis and expressed under nutrient-limiting conditions. We monitor this activity using snapshot fluorescence measurements via flow cytometry, where GFP expression reflects glc3 promoter activity. A naïve analysis of flow cytometry data ignoring cell division suggests many cells are active with low expression. Explicitly accounting for the (non-Markovian) effects of cell division and protein inheritance makes it impossible to write down a tractable likelihood -- a key ingredient in physics-inspired inference, defining the probability of observing data given a model. The dependence on a cell's division history breaks the assumptions of standard (Markovian) master equations, rendering traditional likelihood-based approaches inapplicable. Instead, we adapt conditional normalizing flows (a class of neural network models designed to learn probability distributions) to approximate otherwise intractable likelihoods from simulated data. In doing so, we find that glc3 is mostly inactive under stress, showing that while cells occasionally activate the gene, expression is brief and transient. Subjects: Quantitative Methods (q-bio.QM) ; Data Analysis, Statistics and Probability (physics.data-an); Applications (stat.AP); Machine Learning (stat.ML) Cite as: arXiv:2506.09374 [q-bio.QM] (or arXiv:2506.09374v3 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2506.09374 Focus to learn more arXiv-issued DOI via DataCite Journal reference: , Proc. Natl. Acad. Sci. U.S.A. 123 (15) e2517309123 2026 Related DOI : https://doi.org/10.1073/pnas.2517309123 Focus to learn more DOI(s) linking to related resources Submission history From: Pedro Pessoa [ view email ] [v1] Wed, 11 Jun 2025 03:44:18 UTC (6,049 KB) [v2] Mon, 30 Jun 2025 22:13:16 UTC (6,048 KB) [v3] Wed, 2 Jul 2025 01:26:21 UTC (6,048 KB) Full-text links: Access Paper: View a PDF of the paper titled Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history, by Pedro Pessoa and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.QM < prev | next > new | recent | 2025-06 Change to browse by: physics physics.data-an q-bio stat stat.AP stat.ML References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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