ArticleNature communications2020
Robustness and lethality in multilayer biological molecular networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Metabolic robustness is an emergent property of hierarchical network organization in yeast.mSystems · 2026Article
- Integrated analysis of transcriptome and proteome reveal that PDCoV infection induces autophagy-dependent ferroptosis to facilitate viral replication.Veterinary research · 2026Article
- Mixtures Biotransformation: Multilayer Molecular Networking of Kratom Liver Metabolites.Journal of natural products · 2026Article
- Rethinking network analysis in ethnopharmacology: a multi-omics and AI roadmap to overcome conceptual and methodological biases.Frontiers in pharmacology · 2026Article
- Network-Associated Mechanotransduction in Health and Disease.Results and problems in cell differentiation · 2026Review
- A small set of critical hyper-motifs governs heterogeneous flow-weighted network resilience.Nature communications · 2025Article
- Intermittent Fasting: Implications for Obesity-Related Colorectal Tumorigenesis.Endocrinology and metabolism clinics of North America · 2025Review
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- Target repositioning using multi-layer networks and machine learning: The case of prostate cancer.Computational and structural biotechnology journal · 2024Article
- AI-empowered perturbation proteomics for complex biological systems.Cell genomics · 2024Article
- Non-Coding RNAs Extended Omnigenic Module of Cancers.Entropy (Basel, Switzerland) · 2024Article
- Multi-Omics and Single-Cell Omics: New Tools in Drug Target Discovery.Arteriosclerosis, thrombosis, and vascular biology · 2024Article
- The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment.Neurobiology of aging · 2024Article
- Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions.Nucleic acids research · 2024Article
- Hyper-brain hyper-frequency network topology dynamics when playing guitar in quartet.Frontiers in human neuroscience · 2024Article
- Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein-protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system's robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.