Neural model of the genetic network

Many cell control processes consist of networks of interacting elements tha t affect the state of each other over time. Such an arrangement resembles t he principles of artificial neural networks, in which the state of a partic ular node depends on the combination of the states of other neurons. The A bacteriophage lysis/lysogeny decision circuit can be represented by such a network. It is used here as a model for testing the validity of a neural ap proach to the analysis of genetic networks. The model considers multigenic regulation including positive and negative feedback. It is used to simulate the dynamics of the lambda phage regulatory system; the results are compar ed with experimental observation. The comparison proves that the neural net work model describes behavior of the system in full agreement with experime nts; moreover, it predicts its function in experimentally inaccessible situ ations and explains the experimental observations. The application of the p rinciples of neural networks to the cell control system leads to conclusion s about the stability and redundancy of genetic networks and the cell funct ionality. Reverse engineering of the biochemical pathways from proteomics a nd DNA micro array data using the suggested neural network model is discuss ed.