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Data-driven verification of synthetic gene networks

Automatic design of synthetic gene networks with specific functions is an emerging field in synthetic biology. Quantitative evaluation of gene network designs is a missing feature of the existing automatic design tools. In this work, we address this issue and present a framework to probabilistically analyze the dynamic behavior of a gene network against specifications given in a rich and high level language. Given a gene network built from primitive DNA parts, and given experimental data for the parts, the tool proposed here allows for the automatic construction of a stochastic model of the gene network and in silico probabilistic verification against a rich specification.