Local fractal dimension as an indicator of functional reorganization in molecular interaction networks

Changes in protein abundance incompletely describe how molecular perturbations reshape cellular organization. Here we introduce DIFFRACT, a framework that integrates quantitative proteomics with protein-protein interaction networks to identify reorganization through changes in local fractal dimension. Molecular associations define effective distances between connected proteins, allowing the geometry of their surrounding network neighborhoods to be compared across conditions. Analyses across four organisms (E. coli, A. thaliana, C. elegans, and H. sapiens) and three interaction resources (STRING, BioGRID, and IntAct) establish heterogeneous local scaling, while controlled perturbations demonstrate sensitivity to regional changes and invariance to uniform distance rescaling. Applied to RAVER1-deficient A549 cells, DIFFRACT identifies components associated with established RNA-regulatory functions and candidate roles in tRNA biology, mitochondrial organization and ciliogenesis. Selected RNA-regulatory and ciliary themes are not significantly enriched by abundance-ranked gene set analysis, supporting complementary information from network geometry. Selected functional annotations also recur in HEK293T cells. Guided by the ciliary predictions, we show that RAVER1 depletion increases the proportion of ciliated A549 cells 2.57-fold and increases cilium length. DIFFRACT thus connects changes in molecular network geometry to experimentally testable biological responses, extending the interpretation of proteomic perturbations beyond differential abundance.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-11-02
DOI
https://doi.org/10.64898/2026.10.27.762149
Primary Topic
Bioinformatics and Genomic Networks
Type
preprint

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preprint

Local fractal dimension as an indicator of functional reorganization in molecular interaction networks

Alice Wedler, Sebastian Wussow, Dirk Tänzler, Frank Hause et al.
bioRxiv (Cold Spring Harbor Laboratory)
Bioinformatics and Genomic Networks
preprint

Local fractal dimension as an indicator of functional reorganization in molecular interaction networks

Alice Wedler, Sebastian Wussow, Dirk Tänzler, Frank Hause, Tommy Fuchs, René Keil, Stefan Hüttelmaier, Christian H. Ihling, Andrea Sinz, Marcell Lederer, Oleksandr Sorokin
preprint en

Abstract

Changes in protein abundance incompletely describe how molecular perturbations reshape cellular organization. Here we introduce DIFFRACT, a framework that integrates quantitative proteomics with protein-protein interaction networks to identify reorganization through changes in local fractal dimension. Molecular associations define effective distances between connected proteins, allowing the geometry of their surrounding network neighborhoods to be compared across conditions. Analyses across four organisms (E. coli, A. thaliana, C. elegans, and H. sapiens) and three interaction resources (STRING, BioGRID, and IntAct) establish heterogeneous local scaling, while controlled perturbations demonstrate sensitivity to regional changes and invariance to uniform distance rescaling. Applied to RAVER1-deficient A549 cells, DIFFRACT identifies components associated with established RNA-regulatory functions and candidate roles in tRNA biology, mitochondrial organization and ciliogenesis. Selected RNA-regulatory and ciliary themes are not significantly enriched by abundance-ranked gene set analysis, supporting complementary information from network geometry. Selected functional annotations also recur in HEK293T cells. Guided by the ciliary predictions, we show that RAVER1 depletion increases the proportion of ciliated A549 cells 2.57-fold and increases cilium length. DIFFRACT thus connects changes in molecular network geometry to experimentally testable biological responses, extending the interpretation of proteomic perturbations beyond differential abundance.

bioRxiv (Cold Spring Harbor Laboratory)
Martin Luther University Halle-Wittenberg (DE)
Martin-Luther-Universität Halle-Wittenberg
Bioinformatics and Genomic Networks
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