Designing miRNA-Based Synthetic Cell Classifier Circuits Using Answer Set Programming

作     者：Becker, Katinka Klarner, Hannes Nowicka, Melania Siebert, Heike 

作者机构：Free Univ Berlin Dept Math & Comp Sci Berlin Germany Max Planck Inst Mol Genet IMPRS CBSC Berlin Germany 

出 版 物：《FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY》 (Front. Bioeng. Biotechnol.)

年 卷 期：2018年第6卷

页      面：70-70页

核心收录：

基　　金：German Research Foundation (DFG) via the Research Training Group 1772 Computational Systems Biology 

主　　题：Boolean modeling Answer Set Programming synthetic biology miRNA profiles cell classifier breast cancer 

摘      要：Cell classifier circuits are synthetic biological circuits capable of distinguishing between different cell states depending on specific cellular markers and engendering a state-specific response. An example are classifiers for cancer cells that recognize whether a cell is healthy or diseased based on its miRNA fingerprint and trigger cell apoptosis in the latter case. Binarization of continuous miRNA expression levels allows to formalize a classifier as a Boolean function whose output codes for the cell condition. In this framework, the classifier design problem consists of finding a Boolean function capable of reproducing correct labelings of miRNA profiles. The specifications of such a function can then be used as a blueprint for constructing a corresponding circuit in the lab. To find an optimal classifier both in terms of performance and reliability, however, accuracy, design simplicity and constraints derived from availability of molcular building blocks for the classifiers all need to be taken into account. These complexities translate to computational difficulties, so currently available methods explore only part of the design space and consequently are only capable of calculating locally optimal designs. We present a computational approach for finding globally optimal classifier circuits based on binarized miRNA datasets using Answer Set Programming for efficient scanning of the entire search space. Additionally, the method is capable of computing all optimal solutions, allowing for comparison between optimal classifier designs and identification of key features. Several case studies illustrate the applicability of the approach and highlight the quality of results in comparison with a state of the art method. The method is fully implemented and a comprehensive performance analysis demonstrates its reliability and scalability.