Multi-Stage Probabilistic Bipartite Graph Algorithm - Effect of Herbal Medicines on the Gut Ecosystem

Suganya Chandrababu, Dhundy R. Bastola

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The wide variety of bacterial population in the human gastrointestinal tract (the gut) are the key players in maintaining host physiological functions and colon health. Also, these gut microbiota-mediated productions of metabolites, while mostly beneficial to the host, have also been shown to contribute to the imbalance of the protective versus harmful intestinal bacterial species (dysbiosis). Such imbalance could result in intestinal and extra-intestinal disorders, including inflammatory bowel disease (IBD), diabetes mellitus (DM), obesity, etc. Furthermore, herbal medicines are capable of modulating the bacterial metabolic pathways by affecting the bacterial gene functions that serving as drug targets. This holds a great promise in balancing the gut ecosystem and prevent microbial-associated diseases. However, the diversity of small molecules in the herbs and the complexity of their mechanism of action poses a great challenge for data integration and herbal drug-target association identification. To overcome this challenge and get invaluable insights into microbiome-associated disease biology, a systems-level characterization of the effects of the small molecules from herbs on the bacterial genes and metabolic pathways were made in the current study. We proposed a computational network-based approach; the multi-stage probabilistic bipartite graph algorithm (MPBA), which integrates the complex heterogeneous metabolism/pathway data from herbs and gut bacteria. This approach provided a graphically intuitive solution to study the impact of herbs on gut microorganisms and employed a probabilistic latent semantic analysis (PLSA) framework to allow estimation of candidate entities. A case study using the 8 most important pathways during IBD and 405 enzymes belonging to these pathways was conducted. Our results show that 273 pathway enzymes were harbored by the bacteria, and 262 of them were targeted by small molecules present in 24 culinary herbs. The proposed MPBA enabled simplification of complex biological models needed for an in-depth study of the herb-microbes complex system.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages334-341
Number of pages8
ISBN (Electronic)9781728118673
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: Nov 18 2019Nov 21 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
CountryUnited States
CitySan Diego
Period11/18/1911/21/19

Keywords

  • Bipartite graphs
  • Gut Micro-biome
  • Herbal Drugs
  • Network Pharmacology
  • Probabilistic Latent Semantic Analysis

ASJC Scopus subject areas

  • Biochemistry
  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Health Informatics
  • Pharmacology (medical)
  • Public Health, Environmental and Occupational Health

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  • Cite this

    Chandrababu, S., & Bastola, D. R. (2019). Multi-Stage Probabilistic Bipartite Graph Algorithm - Effect of Herbal Medicines on the Gut Ecosystem. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 334-341). [8982981] (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8982981