Project SIAN
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| site design © 2007 jerry yeh
| Project © 2007 jerry yeh & chris smith |
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project SIAN
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Simulating and Intervening Abnormal Networks (SIAN) Modeling a complex system in a global fashion can be realized by representing variables in the system with discrete values and establishing interaction patterns among the variables. A gene may or may not be expressed. This on/off state can be incorporated into a binary model to characterize a genetic network with a large number of genes. The representation of a gene as a binary state is the essence of Boolean network modeling. The aim of SIAN is to produce genetic network models in cancer by simulating and controlling the regulation of genetic networks as accurately as possible using this form of Boolean network modeling. Most importantly, SIAN intervenes with incongruous cancerous networks to modify networks to a state of normality with the least cost, thus suppressing cancer or any other genetic disease in an efficient manner. To accomplish this, SIAN implements three separate algorithms in order to model a genetic network and establish effectual intervention techniques. The program is partitioned into the Simulation, Inference, and Intervention algorithms. In addition, a built-in visualization of tumor growth is provided so the user can view a real-life scenario of how the dysfunctional cells can eventually lead to a tumor. Because genetic networks may have an abundant number of genes involved in cancerous activity, computational efficiency in determining the optimal gene therapy is crucial. Since the computations are extremely time-intensive, access to supercomputers was necessary to successfully characterize a network of a realistic size. Ultimately, once the expression of thousands of genes is collected through advanced biotechnology, our program can be employed to determine the genetic interactions that are responsible for tumor growth and design an effective strategy to intervene in abnormal networks in a proficient manner. Gene therapists around the world can then effectively determine the proper genetic modifications for various genetic mutations from the applied data.
