Cancer is a complex disease which is composed of numerous interactive components that operate in an environment with high uncertainties. Approaching such a system hence requires its own set of tools. Here, we propose an in-silico model of cancer cell line based on local environmental information, and study its emergent complex global cellular behaviors. The proposed model is composed of a Fuzzy Inference System (FIS) and a Cellular Automata (CA). The FIS has five inputs including nutrition concentration, cell density, apoptosis rate, Vascular Endothelial Growth Factor (VEGF) concentration, and H+ ion concentration. The output of the proposed FIS is the cell state parameter. The FIS determines a threshold as the input of the CA for extracting the appropriate cellular patterns. The generated cellular patterns are based only on local information, but the overall system exhibits a global behavior. The merit of the model is in the simplicity of the rules in learning the complex global system behaviors.

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