From protein networks to biological systems

From protein networks to biological systems.

A system-level understanding of any biological process requires a map of the relationships among the various molecules involved. Technologies to detect and predict protein interactions have begun to produce very large maps of protein interactions, some including most of an organism's proteins. These maps can be used to study how proteins work together to form molecular machines and regulatory p...

A Combination Method of Centrality Measures and Biological Properties to Improve Detection of Protein Complexes in Weighted PPI Networks

Introduction: In protein-protein interaction networks (PPINs), a complex is a group of proteins that allows a biological process to take place. The correct identification of complexes can help better understanding of the function of cells used for therapeutic purposes, such as drug discoveries. One of the common methods for identifying complexes in the PPINs is clustering, but this study aimed ...

A Combination Method of Centrality Measures and Biological Properties to Improve Detection of Protein Complexes in Weighted PPI Networks

Introduction: In protein-protein interaction networks (PPINs), a complex is a group of proteins that allows a biological process to take place. The correct identification of complexes can help better understanding of the function of cells used for therapeutic purposes, such as drug discoveries. One of the common methods for identifying complexes in the PPINs is clustering, but this study aimed ...

From Coupled Dynamical Systems to Biological Irreversibility

In the first half of the paper, some recent advances in coupled dynamical systems, in particular, a globally coupled map are surveyed. First, dominance of Milnor attractors in partially ordered phase is demonstrated. Second, chaotic itinerancy in high-dimensional dynamical systems is briefly reviewed, with discussion on a possible connection with a Milnor attractor network. Third, infinite-dime...

From biological to artificial complex systems