Quantifying structural uncertainty in chemical reaction network inference

[2505.15653] Quantifying structural uncertainty in chemical reaction network inference Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2505.15653 (stat) [Submitted on 21 May 2025 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: Quantifying structural uncertainty in chemical reaction network inference Authors: Yong See Foo , Adriana Zanca , Jennifer A. Flegg , Ivo Siekmann View a PDF of the paper titled Quantifying structural uncertainty in chemical reaction network inference, by Yong See Foo and 3 other authors View PDF HTML (experimental) Abstract: Dynamical systems in biology are complex, and one often does not have comprehensive knowledge about the interactions involved. Chemical reaction network (CRN) inference aims to identify, from observing species concentrations over time, the unknown reactions between the species. Existing approaches such as sparse regularisation largely focus on identifying a single, most likely CRN, without addressing uncertainty about the network structure. However, it is important to quantify structural uncertainty to have confidence in our inference and predictions. In this work, we explore how effective sparse regularisation methods are for quantifying structural uncertainty. Locally optimal solutions to sparse regularisation are mapped to CRN structures; however, it is unclear whether this approach encompasses all plausible CRNs. We find that inducing sparsity with nonconvex penalty functions results in better coverage of the plausible CRNs compared to the popular lasso regularisation. To validate our approach, we apply our methods to real-world data examples, and are able to simultaneously recover reactions proposed across multiple literature sources for a reaction system. Our emphasis on network-level probabilities enables a novel, hierarchical representation of structural ambiguities in the space of CRNs. This representation translates into alternative reaction pathways suggested by the available data, thus guiding the efforts of future experimental design. Comments: 35 pages, 12 figures Subjects: Methodology (stat.ME) ; Quantitative Methods (q-bio.QM) MSC classes: 34A55, 62F07, 62F15 ACM classes: G.3 Cite as: arXiv:2505.15653 [stat.ME] (or arXiv:2505.15653v3 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2505.15653 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yong See Foo Mr [ view email ] [v1] Wed, 21 May 2025 15:31:00 UTC (4,124 KB) [v2] Sat, 30 Aug 2025 05:29:19 UTC (4,108 KB) [v3] Tue, 14 Apr 2026 03:12:28 UTC (4,070 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantifying structural uncertainty in chemical reaction network inference, by Yong See Foo and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2025-05 Change to browse by: q-bio q-bio.QM stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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