albicans yeast for hyphal transition, to a matrix cell density sensing to contact inhibition, proliferation, migration, and apoptosis, or to illustrate the regulatory effects in cervical cancer. For example, it has been used to analyze oncogene rules in Non-small cell lung cancer, to model the C. A Boolean network model implicitly assumes that all biological components are described by binary values, and their interactions represented by Boolean regulatory functions, and it is well-known to capture the silent dynamical properties of biological networks. In this study, we employed the Boolean network model to simulate the network dynamics. To quantify the pleiotropic degree of a gene in terms of network dynamics, we proposed in silico pleiotropic score s P S, which is a measure to represent how differently a gene affects the dynamics of other genes against different mutations, such as knockout and over-expression mutations.
Therefore, a new approach is needed to investigate the pleiotropy induced by a directed signaling network because it can explain the pleiotropy caused by a gene–gene dynamical relationship. Despite the interesting observations in previous studies, most of the previous approaches focused on the pleiotropy analysis induced by undirected molecular correlation networks. Finally, protein–protein interaction network analysis was also widely used, and some studies confirmed pleiotropic effects in biological molecular function, which lead to complex diseases. cerevisiae and found that pleiotropy is an emergent property of metabolic network. Another study using a metabolic model conducted constraint-based simulations for E. In addition, a few computational models deployed a pairwise combination of genome-wide association data from the complex disease pleiotropy analysis and modular gene expression analysis. In another study, a statistical analysis using canonical correlation analysis identified a novel candidate pleiotropic associations between genetic variants and phenotypes. For example, an experimental study performed several laboratory cultures with the nath-10 polymorphism and explained its pleiotropic role in the evolution of a cryptic genetic variation in C. In this regard, many methods have been modeled to understand the pleiotropy. This becomes an important contributor in identifying a novel function of individual genes with respect to gene–gene interactions in system-level biological diseases. In human genetic diseases, it means that different mutations within the same gene cause different pathological effects. Pleiotropy is the phenomenon in which one gene can result in multiple phenotypes or traits. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations. Finally, we found some biological evidence showing that s P S analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. This implies that the structural characteristics are principles to identify new pleiotropic genes. We investigated the relations of s P S with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. In addition, we found that many types of functionally important genes tend to have higher s P S values than other genes in other words, they are more pleiotropic. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores ( s P S), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases.
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