Network modeling and inference of peroxisome proliferator-activated receptor pathway in high fat diet-linked obesity

Systems biology aims to understand how holistic systems theory can be used to explain the observable living system characteristics, and mathematical modeling tools have been successful in understanding the intricate relationships underlying cellular functions. Lately, researchers have been interested in understanding molecular mechanisms underlying obesity, which is a major health concern worldwide and has been linked to several diseases. Various mechanisms such as peroxisome proliferator-activated receptors (PPARs) are known to modulate obesity-induced inflammation and its consequences. In this study, we have modeled the PPAR pathway using a Bayesian model and inferred the sub-pathways that are potentially responsible for the activation of the output processes that are associated with high fat diet (HFD)-induced obesity. We examined a previously published dataset from a study that compared gene expression profiles of 40 mice maintained on HFD against 40 mice fed with chow diet (CD). Our simulations have highlighted that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice and are therefore favorable targets for anti-obesity strategies. We further cross-validated our observations with experimental results from the literature. We believe that mathematical models such as those presented in the present study can help in inferring other pathways and deducing significant biological relationships. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Systems biology uses holistic systems theory and mathematical modeling tools to understand complex cellular relationships and study molecular mechanisms underlying obesity. Modeling the PPAR pathway revealed that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice, suggesting they could be favorable targets for anti-obesity strategies. Cross-validating observations with experimental results from the literature further supported the potential of mathematical models in inferring significant biological relationships.