Design of miRNA-based synthetic cell classifiers
Cell classifiers are synthetic bio-devices performing type-specific in vivo classification of the cell’s molecular fingerprint. In particular, they can recognize cancerous cells and trigger their apoptosis, shaping novel therapies for cancer patients. Here, the classifiers describe the relationship between cells’ molecular profiles and their annotation as cancerous or non-cancerous. Such a relationship can be represented as a partially defined logical function where the output indicates the cell condition. A single circuit’s processing logic is usually described using a larger individual Boolean function, whereas multi-circuit classifiers are ensembles of simpler logic designs. Such a distributed classifier consists of a group of single-circuit classifiers deciding collectively whether a cell is cancerous according to a predefined threshold function. Both architectures have shown the potential to predict the cell condition with high accuracy. However, the lack of comprehensive workflows to design and evaluate the classifiers, in particular, assessing their robustness to noise and novel information, makes their application limited.
We propose a framework for designing miRNA-based distributed cell classifiers, employing genetic algorithms and Answer Set Programming. We develop optimization criteria comprising the accuracy and robustness of the circuits and train classifiers that achieve high performance (89.78% accuracy for the most-perturbed data set), as shown in multiple simulated data studies. The evaluation performed on cancer data demonstrates that distributed classifiers outperform single-circuit designs by up to 13.40%. Our workflow provides inherently interpretable classifiers that comprise relevant miRNAs previously described in the literature, as well as more complex regulation patterns underlying the data. Ultimately, we show how our approach can be applied to other binary classification problems comprising different biological modalities such as gene expression or mutation patterns providing interpretable classifiers