Mathematical modeling deciphering balance between cell survival and cell death using insulin

Abstract

When modelling cell signalling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a deterministic numerical method to the analysis of a large, systematic dataset describing the dynamics of cell signalling downstream of, tumor necrosis factor-α (TNF), epidermal growth factor (EGF), and insulin receptors in human colon carcinoma cells. Deterministic modeling is useful as a means to assemble and test what we know about proteins and networks. We extensively study the space of parameters to show that the model is structurally stable and robust over a broad range of parameter values. We have made the biological view of the main paths of the insulin input showing cell survival and cell death. Than with the help of different parameters relating to that protein present in the model we have designed scheme of the biochemical paths/deterministic model. With those parameters equations were formed which vary with time i.e. differential equation. Thus, our model is suitable for implementation in multi-scale simulation programs that are presently under development to study the behavior of large tumor cell populations.