The network of biomolecular regulation in cells is large and complex. In order to efficiently analyze such a network, it can be helpful to identify the core structure of the network that maintain the essential regulatory functions. In this study, We identified the core structures of the biomolecular regulatory networks with respect to two properties such as evolvability and controllability and We suggested the topological and biological meanings of those two core structures.
Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? To investigate the evolvability and robustness of the human signaling network, Boolean network modeling and attractor landscape analysis were used. The results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core stronger than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability score are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness score are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability score whereas experimental drug targets have high robustness score.
Cellular behavior is determined not by a single molecule but by many molecules that interact strongly with one another and form a complex network. It is unclear whether cellular behavior can be controlled by regulating certain molecular components in the network. By analyzing a variety of biomolecular regulatory networks, we discovered that only a small fraction of the network components need to be regulated to govern the network dynamics and control cellular behavior. We defined a minimal set of network components that must be regulated to make the cell reach a desired stable state as the control kernel and developed a general algorithm for identifying it. We found that the size of the control kernel was related to both the topological and logical characteristics of a network. Intriguingly, the control kernel of the human signaling network included many drug targets and chemical-binding interactions, suggesting therapeutic application of the control kernel.