TitleApplication of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin.
Publication TypeJournal Article
Year of Publication2013
AuthorsLarkin, A, Siddens, LK, Krueger, SK, Tilton, SC, Waters, KM, Williams, DE, Baird, WM
JournalToxicol Appl Pharmacol
Date Published2013 Mar 1
KeywordsAnimals, Aryl Hydrocarbon Hydroxylases, Cytochrome P-450 CYP1B1, Female, Fuzzy Logic, Gene Regulatory Networks, Mice, Neural Networks (Computer), Polycyclic Hydrocarbons, Aromatic, Risk Assessment, Skin

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log(2) fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights.

Alternate JournalToxicol. Appl. Pharmacol.
PubMed ID23274566
PubMed Central IDPMC3626406
Grant ListP42 ES016465 / ES / NIEHS NIH HHS / United States
P42ES016465 / ES / NIEHS NIH HHS / United States
P42ES016465-S1 / ES / NIEHS NIH HHS / United States