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ABSTRACT

Understanding gene-gene interaction and its causal relationship to protein-protein interaction is a viable route for understanding drug action at the genetic level, which is largely hindered by inability to robustly map gene regulatory networks. Here, we use biological prior knowledge of family-to-family gene interactions available in the KEGG database to reveal individual gene-to-gene interaction networks that underlie the gene expression profiles of 2 cell line data sets, sensitive and resistive to neoadjuvant docetaxel breast anticancer drug. Comparison of the topology of the 2 networks revealed that the resistant network is highly connected with 2 large domains of connectivity: one in which the RAF1 and MAP2K2 genes form hubs of connectivity and another in which the RAS gene is highly connected. On the contrary, the sensitive network is highly disrupted with a lower degree of connectivity. We investigated the interactions of the neoadjuvant docetaxel drug with the protein chains encoded by gene-gene interactions that underlie the disruption of the sensitive network topology using protein-protein and drug-protein docking techniques. We found that the sensitive network is likely to be disrupted by interaction of the neoadjuvant docetaxel drug with the DAXX and FGR1 proteins, which is consistent with the observed accumulation of cytoplasmic DAXX and overexpression of FGR1 precursors in cancer cell lines. This indicates that the DAXX and FGR1 proteins could be potential targets for the neoadjuvant docetaxel drug. The work, therefore, provides a new route for understanding the effect of the drug mode of action from the viewpoint of the change in the topology of gene-gene regulatory networks and provides a new avenue for bridging the gap between gene-gene interactions and protein-protein interactions which could have deep implications on mainstream drug development protocols.

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Wind turbine operation and maintenance costs depend on the reliability of its components. Thus, a critical task is to detect and isolate faults, as fast as possible, and restore optimal operating conditions in the shortest time. In this paper, a machine learning of graphical models approach is proposed for fault diagnosis of wind turbines, in particular pitch system. The role of the latter is to adjust the blade pitch angle by rotating it according to the current wind speed in order to optimize the wind turbine power production. This is achieved by a controller based on blade pitch angles measured by two redundant sensors in each blade. Without the sensor accuracy reading, the controller can be misled and fail to achieve the optimal control strategy according to the current operation conditions. In addition, pitch angle sensors complete failure can lead to dangerous actions of the controller; while fixed or drifted bias of sensor measurements may decrease the controllers efficiency. To better control and overcome these challenges, we propose a methodology that is based on Gaussian acyclic graphical models and the lasso estimate. The methodology has shown the ability to model, and diagnose faults that occur in the pitch system in wind turbines during its normal run and could lead to a fast recovery to the optimal operating conditions.