A framework for designing miRNA-based distributed cell classifier circuits

Motivation Cell classifiers are synthetic bio-devices performing type-specific in vivo classification. The circuits identify a cell state based on its molecular fingerprint. In particular, the classifiers may be designed to recognize cancerous cells and trigger their apoptosis, shaping a novel therapy for cancer patients. Recently, we introduced a new theoretical design of such devices employing distributed classifiers. Here, a group of single-circuit classifiers decides collectively according to a pre-defined threshold function whether a cell is cancerous. The multi-circuit architecture has shown the potential to predict the cell condition with high accuracy. However, lack of far-reaching machinery to design and evaluate distributed cell classifiers, in particular, assessing their robustness to noise and novel information, makes their application limited. Results In this study, we present a comprehensive framework for designing and evaluating miRNA-based distributed cell classifiers comprising data simulation, pre-processing, and an extensive testing scheme. We develop optimization criteria that allow increasing the accuracy and robustness of classifiers to noise and novel information as shown in simulated and real-world data studies. The evaluation performed on cancer data demonstrates that distributed classifiers outperform single-circuit designs in terms of prediction accuracy. Our classifiers include relevant miRNAs previously described in the literature, as well as more complex regulation patterns included in the data. Availability The code and data are available at: https://github.com/MelaniaNowicka/RAccoon . Contact [email protected]