  This paper deals with the cellular biological network analysis of the
tumor-growth model, consisting of multiple spaces and time scales. In this
paper, we present a model in graph simulation using ABM for tumor growth. In
particular, we propose a graph agent-based modeling and simulation system in
the format of tumor growth scenario for evolving analysis. To manage cellular
biological network analysis, we developed a workflow that allows us to estimate
the tumor model and the complexity of the evolving behavior in a principled
manner. By developing the model using Python, which has enabled us to run the
model multiple times (more than what is possible by conventional means) to
generate a large amount of data, we have succeeded in getting deep in to the
micro-environment of the tumor, employing network analysis. Combining
agent-based modeling with graph-based modeling to simulate the structure,
dynamics, and functions of complex networks is exclusively important for
biological systems with a large number of open parameters, e.g., epidemic
models of disease spreading or cancer. Extracting data from evolutionary
directed graphs and a set of centrality algorithms helps us to tackle the
problems of pathway analysis and to develop the ability to predict, control,
and design the function of metabolisms. Reproducing and performing complex
parametric simulations a known phenomenon at a sufficient level of detail for
computational biology could be an impressive achievement for fast analysis
purposes in clinics, both on the predictive diagnostic and therapeutic side.
