The loss of cell cycle control is often associated with cancers and other different diseases. With the accumulation of omics data, the network for molecule interactions in the cell cycle process will become much clearer. The identification of the crucial modules in a giant network and investigation of inherent control relations are very important to the understanding of the molecular mechanisms of diseases for new drug design. The paper proposes novel techniques in analyzing such core regulatory modules based on network and system control theories. We initially define the degree of participation (DOP) and the rate of activity (ROA) for indentifying core module components, and then the diverse contribution elasticity functions for quantifying pairwise regulatory or control activities between those components, thus facilitating the decomposition of expanded core modules and the formation of feedback loops within the control schema. Motivated by the inherent regulatory mechanisms, we expound a kind of multiphase nonlinear adaptive control algorithm in repelling abnormal genetic mutations, which directly and indirectly impact cancer development in biological cells and organs. Experimental predictions are also elucidated within the work, helping those in vivo design, verification and performance evaluation.
Concurrent Learning-Based Adaptive Control of an Uncertain Robot Manipulator With Guaranteed Safety and Performance
