Various applications of nanosubstances in industrial and consumer goods sectors are becoming increasingly common because of their useful chemical and physical properties. Therefore the development of methods for the assessment of potential hazards to human and ecological health posed by increased exposure to nanosubstances is necessary. In this paper, mathematical models were developed to characterize proteomics patterns of Caco-2/HT29-MTX cells exposed for three and twenty four hours to two kinds of important nanoparticles: multi-walled carbon nanotubes (MWCNT) and TiO2 nanobelts (TiO2-NB). For each nanosubstance, cells were exposed to two concentrations of the material before carrying out proteomics analyses: 10 μg and 100 μg. In each case over 3,000 proteins were identified. Such data generated by the omics sciences fall in the ‘Big Data’ realm. Visualization of such massive data is a daunting task and methods are needed to simplify such data analysis. A mathematically based similarity index, which measures the changes in abundances of cellular proteins that are strongly affected by exposure to the nanosubstances, was used to characterize toxic effects of the nanomaterials. We identified 8 and 25 proteins, which are most highly perturbed by MWCNT and TiO2-NB, respectively. These proteins may be responsible for a specific response of the exposed cells to the nanoparticles. Furthermore, a set pf 14 proteins were affected by either of the two nanoparticles. These proteins likely reflect a non-specific toxic response of the cells. The similarity methods proposed in this paper may be useful in the management and visualization of the large amount of data generated by proteomics technologies while assessing nanomaterial safety.

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