Is the optimal intervention policy UC superior to the suboptimal policy MFPT over inferred probabilistic Boolean network models?

A salient problem in translational genomics is the use of gene regulatory networks to determine therapeutic intervention strategies. Theoretically, in a complete network, the optimal policy performs better than the suboptimal policy. However, this theory may not hold if we intervene in a system based on a control policy derived from imprecise inferred networks, especially in the small-sample scenario. In this paper, we compare the performance of the unconstrained (UC) policy with that of the mean-first-passage-time (MFPT) policy in terms of the quality of the determined control gene and the effectiveness of the policy. Our simulation results reveal that the quality of the control gene determined by the robust MFPT policy is better in the small-sample scenario, whereas the sensitive UC policy performs better in the large-sample scenario. Furthermore, given the same control gene, the MFPT policy is more efficient than the UC policy for the small-sample scenario. Owing to these two features, the MFPT policy performs better in the small-sample scenario and the UC policy performs better only in the large-sample scenario. Additionally, using a relatively complex model (gene number N is more than 1) is beneficial for the intervention process, especially for the sensitive UC policy.

Association of novel single-nucleotide polymorphisms of the vascular endothelial growth factor-A gene with high-altitude adaptation in yak (Bos grunniens).

Vascular endothelial growth factor-A gene (VEGF-A) is a key regulator of angiogenesis and an endothelial cell mitogen that plays an important role in high-altitude adaptation. In this study, we detected 2 novel single-nucleotide polymorphisms (SNPs) of VEGF-A by screening for genetic variation in 700 individuals of 3 domestic Chinese yak breeds--namely Gannan (GN), Datong (DT), and Tianzhu white (TZW)--using polymerase chain reaction-restriction fragment length polymorphism and DNA sequencing techniques. GN and DT yaks live at high altitude and TZW yaks live at low altitude on the Qinghai-Tibetan Plateau. SNP g.8430T>C is located in intron 4 of VEGF-A. SNP g.14853G>A is located in the 3' untranslated region of VEGF-A. Frequencies of the GA and AA genotypes and the A allele of SNP g.14853G>A observed in GN and DT yaks were significantly higher than that in TZW yaks (P < 0.01). No significant difference among the breeds was observed for SNP g.8430T>C. The frequency of haplotype TA was significantly higher (P < 0.01), whereas the frequency of TG (P < 0.01) was significantly lower in GN and DT yaks compared with that in TZW yaks. The 2 SNPs were in moderate linkage disequilibrium in GN and DT yaks, but not in TZW yaks. The fixation index (FST) pairwise value was significantly different among the breeds studied. The neutral test result indicated that the region between the 2 SNPs may have been subjected to positive or balancing selection, and the high-altitude hypoxia environment might be the main determinant for selection. These results suggest that VEGF-A might contribute to the high-altitude adaptability of yak.