Towards modeling automation for synthetic biology

Abstract

Rule-based modeling was first introduced to address the 'combinatorial complexity' problem in cellular signaling. A number of software tools and methods have been developed in recent years to make accurate predictions about the functional role of proteins in signaling transduction systems. Many of these approaches are based on formal languages, such as Kappa and BioNetGen (BNGL). Modeling also plays an integrant role in synthetic biology, a new interdisciplinary subject aiming to design novel biological systems. The specification of synthetic biology systems using high level languages is still a challenge. In this article, we proposed to extend the Rule-based modeling rule-based modeling from systems biology to synthetic biology and introduced a new model-specification language, which allows quickly generating mathematical models encoding the phenotypical behaviors of biological systems. Our approach (termed AutoModel) also takes into account the context dependencies of biological interactions, which makes it a desirable method for synthetic biology research. A software implementation of our approach is available at https://github.com/cliao15/Rulebase2011.