| Abstract | There is a wealth of cellular and molecular information regarding the differentiation of T-helper cells, as well as results of in vitro experimental treatments that modify the normal differentiation process. With the aid of such publicly available information, I inferred the regulatory network that controls the differentiation of T-helper cells. Such network was converted into a continuous dynamical system in the form of a set of coupled ordinary differential equations. The system has a set of fixed point attractors, but the basins of only five of them cover most of the state space. These attractors correspond to the activation states observed experimentally for the precursor (Th0), and effector (Th1, Th2, Th17 and Treg) T-helper cells. Moreover, the model is able to describe the differentiation from a precursor to an effector type due to specific molecular signals. Then, taking a network as a representation of a single cell, I constructed a model of a cell culture where cells respond to and produce different molecular signals. The dynamical behavior of this virtual cell culture was analyzed, showing that it leads to cultures containing subsets of undifferentiated and differentiated cells in appropriate proportions. This model will allow to study the mechanisms leading to imbalances of T-helper populations, which is the cause of several immune disorders. |
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