ArticleProceedings of the National Academy of Sciences of the United States of America2023
The metabolomic physics of complex diseases.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Omnidirectional Interaction Networks Modulating Phenotypic Plasticity in the Gut Microbiota Towards Ulcerative Colitis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Statistical learning of stochastic complex systems via the Yau-Yau nonlinear filter.Innovation (Cambridge (Mass.)) · 2026Article
- Graph statistics theory of individualized quantitative genetics under haplotype-resolved genome assembly.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- An omnigenic interactome model to chart the genetic architecture of individual plants.Horticulture research · 2026Article
- A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Topological data analysis and topological deep learning beyond persistent homology: a review.Artificial intelligence review · 2026Article
- Clinically constrained Bayesian network analysis of immune-metabolic factors associated with bone mineral density in a real-world orthopedic cohort.Frontiers in surgery · 2026Article
- idopNetwork Analysis of Salt-Responsive Transcriptomes Reveals Hub Regulatory Modules and Genes inInternational journal of molecular sciences · 2025Article
- Identification of biomarkers and target drugs for melanoma: a topological and deep learning approach.Frontiers in genetics · 2025Article
- Hypernetwork modeling and topology of high-order interactions for complex systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The metabolomic physics of complex diseases.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
8 authors.
Funding
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Abstract
Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.
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