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Recent years witnessed increased utility of biologically inspired computational models. Neural networks transformed the industry by enabling a plethora of applications in computer vision, and natural language processing, among others. Chemical reaction networks started gaining increased interest within the area of molecular programming considering the wide range of potential applications in synthetic biology, medicine, nanofabrication and other fields. In order to program desired behavior in a manner similar to conventional programming, both of the models require intuitive and effective programming paradigms. However, programs are susceptible to errors, and are hard to systematically analyze and synthesize.