Decoding cell signaling via optimal transport and information theory

Physics > Biological Physics [Submitted on 20 Feb 2026 (v1), last revised 18 Jun 2026 (this version, v2)] Title:Decoding cell signaling via optimal transport and information theory View PDF HTML (experimental)Abstract:Cellular signal processing performs reliably despite molecular noise. Mutual information (MI) is widely used to quantify signaling fidelity, capturing how well outputs discriminate input states. However, it fails to capture whether the output preserves the statistical structure of the input, a property crucial in morphogen patterning and dose-dependent signaling. To address this gap, we introduce the 2-Wasserstein (2-WD) distance, which provides a geometric basis for comparing input and output distributions. We define MI as informational fidelity (INF) and the inverse of the 2-WD as geometric fidelity (GMF). Applying this dual-fidelity framework to canonical regulatory motifs under Gaussian channel approximation reveals topology-dependent trade-offs: coherent feed-forward loops can perform well in both dimensions, whereas feedback architectures sacrifice INF to enhance GMF. Experimental analysis of tumor necrosis factor signaling supports the predicted role of feedback regulation. Analysis of RAS-MAPK signaling shows that intracellular signal relay is better described by a balance between INF and GMF than by information transmission alone. Our results demonstrate that reliable signaling need not maximize information alone, but can arise from balancing information transmission with distributional correspondence. Thus, GMF represents a distinct dimension of signaling fidelity and provides a framework for analyzing natural networks and designing task-specific synthetic circuits. Submission history From: Mintu Nandi [view email][v1] Fri, 20 Feb 2026 06:53:55 UTC (2,594 KB) [v2] Thu, 18 Jun 2026 05:24:14 UTC (6,146 KB) Current browse context: physics.bio-ph Change to browse by: References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) 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.