Identifying Critical Transitions of Biological Processes by Dynamical Network Biomarkers

Abstract

There are non-smooth or even abrupt state changes during many biological processes, e.g., cell differentiation process, proliferation process, or even disease deterioration process. Such changes generally signal the emergence of critical transition phenomena, which result in drastic transitions in system states or phenotypes [1-4]. Therefore, it is of great importance to identify such transitions and further reveal their molecular mechanism. Recently based on dynamical network biomarkers (DNBs), we developed a novel theory as well as the computational method to detect critical transitions even with a small number of samples. We show that DNBs can identify not only early-warning signals of the critical transitions but also their leading networks, which drive the whole system to initiate such transitions [1-4]. Examples for complex diseases are also provided to detect pre-disease stages (or detect early-signal of complex diseases) for which traditional methods failed, for demonstrating the effectiveness of this novel approach.