Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.18028 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915809293500416 |
|---|---|
| author | Nandi, Mintu Ito, Sosuke |
| author_facet | Nandi, Mintu Ito, Sosuke |
| contents | A central challenge in cellular signal processing is understanding how biochemical networks perform reliably despite molecular noise. Traditionally, mutual information has been widely used to quantify signaling fidelity, capturing how well outputs discriminate distinct input states. However, it fails to capture whether the output also faithfully mirrors the statistical structure of the input, a property crucial in processes like morphogen patterning, dose-dependent signaling, and cellular communication. To address this gap, we introduce the 2-Wasserstein distance from optimal transport theory, which provides a geometric basis for comparing input and output distributions. In our proposed framework, we define mutual information as informational fidelity and the inverse of the 2-Wasserstein distance as geometric fidelity. Applying this dual-fidelity framework to canonical regulatory motifs under Gaussian channel approximation reveals a topology-dependent trade-off: coherent feed-forward loops can achieve high performance in both dimensions, whereas feedback architectures typically sacrifice informational fidelity to enhance geometric fidelity. We demonstrate that theoretical predictions of feedback regulation are well supported by experimental data from tumor necrosis factor signaling. Our results demonstrate that maximizing information alone is not always advantageous and that reliable signaling arises from balancing information transmission with the geometric aspects of signaling. Thus, our analysis establishes geometric fidelity as a fundamental, yet previously unrecognized, dimension of signaling fidelity. It also provides a quantitative, experimentally accessible framework for dissecting natural networks and designing task-specific synthetic circuits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18028 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decoding cell signaling via optimal transport and information theory Nandi, Mintu Ito, Sosuke Biological Physics Statistical Mechanics A central challenge in cellular signal processing is understanding how biochemical networks perform reliably despite molecular noise. Traditionally, mutual information has been widely used to quantify signaling fidelity, capturing how well outputs discriminate distinct input states. However, it fails to capture whether the output also faithfully mirrors the statistical structure of the input, a property crucial in processes like morphogen patterning, dose-dependent signaling, and cellular communication. To address this gap, we introduce the 2-Wasserstein distance from optimal transport theory, which provides a geometric basis for comparing input and output distributions. In our proposed framework, we define mutual information as informational fidelity and the inverse of the 2-Wasserstein distance as geometric fidelity. Applying this dual-fidelity framework to canonical regulatory motifs under Gaussian channel approximation reveals a topology-dependent trade-off: coherent feed-forward loops can achieve high performance in both dimensions, whereas feedback architectures typically sacrifice informational fidelity to enhance geometric fidelity. We demonstrate that theoretical predictions of feedback regulation are well supported by experimental data from tumor necrosis factor signaling. Our results demonstrate that maximizing information alone is not always advantageous and that reliable signaling arises from balancing information transmission with the geometric aspects of signaling. Thus, our analysis establishes geometric fidelity as a fundamental, yet previously unrecognized, dimension of signaling fidelity. It also provides a quantitative, experimentally accessible framework for dissecting natural networks and designing task-specific synthetic circuits. |
| title | Decoding cell signaling via optimal transport and information theory |
| topic | Biological Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2602.18028 |