Parameterization Process of Multi-value Logic Networks

This paper presents a generator for creating pa-rameterized multi-value logic models. It can be challenging to precisely capture the behavior of a biological system and translate it into a set of multi-value logic network functions due to the difficulty of gathering sufficient data. However, these issues can be addressed to some extent by using parameterized multi-value logic networks, as this model allows for leaving some update functions unspecified. The generator employs various complex network models, including the Watts-Strogatz model and ErdOs-Renyi random network model, among others. In this study, either existing non-parametric networks are parameterized or new net-works are constructed based on specified criteria. Additionally, the generator is capable of producing visual model graphs and conducting relevant analyses. Finally, the dynamic structure and description of the parameterized multi-value logic network are provided to demonstrate the feasibility and effectiveness of the proposed generator.