Modeling, Learning and Simulating Biological Cells with Entity Grammar

In recent years, whole cell modeling approaches, which combine existing knowledge and machine learning results, have received considerable attention. These approaches are potentially very efficient for simulating and analyzing physiological function of cells. In this work, entity grammar system is proposed as formalism for knowledge representation and multistratgy learning techniques in systems biology. Modeling biological cells with entity grammar starts from the simple grammatical models. Integrating the simple models into a cooperating entity grammar of cell facilitates real-time model learning and updating. This makes a difference with many other formalisms to build preset models. The scheme of such a platform is described in the paper and the possible applications are discussed. The proposed formalism method is open to all reasoning paradigm and can be used for studying biological complex systems.