The decrease of consistence probability: at the crossroad of catastrophic transition of a biological system

Unlike traditional detection of a disease state in which there are clear phenomena, it is usually a challenge to identify the pre-disease state during the progression of a complex disease just before the serious deterioration, not only because of the high complexity of the biological system, but there may be few clues and apparent changes appearing until the catastrophic critical transition occurs.

In this work, by exploiting the different dynamical features between the normal and pre-disease states, we present a hidden-Markov-model (HMM) based computational method to identify the pre-disease state and elucidate the essential mechanisms during the critical transition at the network level. Specifically, by considering the network variation and regarding that the pre-disease state is the end or shift-point of a stationary Markov process, a consistence score is proposed to measure the probability that a system is in consistency with the normal state. As validation, this approach is applied to detect the upcoming critical transition of complex systems based on both the dataset generated from a simulated network and the rich information provided by high-throughput microarray data. The effectiveness of our method has been demonstrated by the identification of the pre-disease states for two real datasets including HCV-induced hepatocellular carcinoma and virus-induced influenza infection.

From dynamical view point, the critical-transition phenomena in many biological processes are of some generic properties, which can be detected by the established method.