Continuous Mammalian Cortical Area Development Model : Transforming From Qualitative To Quantitative Model

T he understanding of mammalian cortical area development network has long b een a core objective in Systems Biology. The cerebral cortex is spliced into many functionally distinct areas. The evolution of these areas during neural growth is relied on a set of gen es expression patterns. For instance, through the anterior posterior a xis, gradients of set of genes such as Fgf8, Emx2, Pax6, Coup tfi, and Sp8 control the role in specifying a real identity. The Knowledge about this network is mainly of qualitative nature and incomplete. Therefore, we utilized a computati onal method to und erstand the complex dynamic interactions behavior between the aforementioned genes. There is a need to understand which interactions, and other combinations of interactions would be appeared in networks that mimic the anterior posterior expression patterns . In addition, a continuous concentration measurements are considered as significant indicators for proteins activities. The concentration level during t he interaction can provide more insight to understand system activities and supports both of diagnosis and drugs treatment studies. Thus, to achieve this task, herein, we present a two step approaches to facilitate the computational model. The first step is employing a Boolean network model since the availabl e knowledge in qualitative form, this model mimic s the genes interactions. The Boolean model treats expression levels as Boolean since the nature of the expression data is currently available in the qualitative form. However, even the discrete model is a powerful tool to study and underst and the dynamic behavior of genes interaction, but it can never provide detailed time courses of concentration levels. Therefore, in the second step, we transformed the discrete Boolean model to continuous simulation to reproduce a continuous protein concentration. We en gaged a standard method such as multivariate polynomial interpolation to transform the logic operations to ordinary differential equations model (ODE).