Logic Synthesis for Genetic Diseases by Pey-Chang Kent Lin & Sunil P. Khatri

Author:Pey-Chang Kent Lin & Sunil P. Khatri
Language: eng
Format: epub
Publisher: Springer New York, New York, NY

3.4 Experimental Results

3.4.1 Model Implementation

We evaluate the SAT-based method for determining the BN on two GRNs, one synthetic (randomly generated) and one real (p53 network network [4], [21]). We first investigate the senstivity of our method regarding the number of available gene expression observations, and then we demonstrate our method on attractor data from the p53 network.

The p53 network is well-studied in genomics and medicine, due to the involvement of p53 gene in many human cancers. p53 is a tumor suppressor gene and is a transcription factor for many downstream genes involved in controlling cell cycle, repairing DNA damage, and inducing apoptosis (cell death) for example. The main pathways for p53 [22] involve DNA damage in the form of breaks in the DNA strand, as shown in Fig. 3.2 In the figure, forward arrows represents activation, while "arrows" with a perpendicular line represents repression. The presence of the external signal dna_dsb (DNA strand break damage) activates ATM, which in turn represses Mdm2, allowing for activation of p53. The expression of p53 blocks replication of DNA (a necessary response when DNA is damaged). From these pathways, [4] obtained the corresponding Boolean functions.

In our experiments, the function of each gene in both networks is known, but hidden from our algorithm. We extract both the predictor set and gene expression observations to test our algorithm with. The regulating logic functions of the synthetic and p53 GRNs are shown in Tables 3.2 and 3.3 respectively (these are kept hidden from our algorithm).Table 3.2Boolean regulating functions for Synthetic 5-Gene Network

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