Department of Electrical and Computer Engineering & The Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX, USA; Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki City, 567-0085 Osaka, Japan.
Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki City, 567-0085 Osaka, Japan.
J Theor Biol. 2021 Jun 21;519:110647. doi: 10.1016/j.jtbi.2021.110647. Epub 2021 Feb 26.
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.
系统生物学旨在了解整体系统理论如何用于解释可观察的生命系统特征，并且数学建模工具已成功用于理解细胞功能背后的复杂关系。最近，研究人员一直致力于了解肥胖的分子机制，肥胖是全球主要的健康问题，与多种疾病有关。已知各种机制，如过氧化物酶体增殖物激活受体（PPARs），可以调节肥胖引起的炎症及其后果。在这项研究中，我们使用贝叶斯模型对 PPAR 途径进行建模，并推断出可能负责激活与高脂肪饮食（HFD）诱导的肥胖相关的输出过程的亚途径。我们检查了一项先前发表的研究数据集，该研究比较了 40 只接受 HFD 的老鼠和 40 只接受标准饮食（CD）的老鼠的基因表达谱。我们的模拟结果表明，GPCR 和 FATCD36 亚途径在 HFD 老鼠中异常活跃，因此是抗肥胖策略的有利靶点。我们还通过文献中的实验结果进一步验证了我们的观察结果。我们相信，像本研究中提出的数学模型可以帮助推断其他途径并推导出重要的生物学关系。
