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

Chen and Li BMC Systems Biology The decrease of consistence probability: at the crossroad of catastrophic transition of a biological system Pei Chen 0 Yongjun Li 0 0 School of Computer Science and Engineering , Wushan Road, 510640, Guangzhou , China Background: 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. Results: 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. Conclusion: 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. Dynamical network biomarker; Hidden Markov process; Pre-disease states - From IEEE International Conference on Bioinformatics and Biomedicine 2015 Washington, DC, USA.9-12 November 2015 Background Recently, evidence suggests that the deterioration of many complex diseases is not necessarily smooth but abrupt, that is, the sudden change of system state exists widely during the progression of complex diseases. For example, some chronic diseases such as cancer, the malignant deterioration may arise within a period of short-time progression, while before such catastrophic transitions the disease such as chronic inflammation may progress gradually for years of long incubative duration [1–5]. In other words, during the progression of illness there is a sudden critical state transition from a relatively healthy stage to a seriously diseased stage. For many complex diseases, it is crucial to detect such critical state transition in advance so as to prevent or at least get ready for such a catastrophic event. However, it is still a challenge work to signal the upcoming critical deterioration since the state of the system may show little apparent change before the tipping point is really reached. This is also the reason why diagnosis based on traditional biomarkers may fail to indicate a pre-disease state. A possible approach to study the warning signal of the sudden deterioration is to explore and analyze the dynamical features generated from the early abnormalities in distinct time-series prior to the emergence of the apparent malignancy. Therefore, in order to describe the underlying dynamical mechanism of complex diseases, their evolutions are often modeled as time-dependent nonlinear dynamical systems, in which the abrupt deterioration or qualitative transition is viewed as the state transition or phase shift at a bifurcation point [6]. We particularly focus on the complex diseases with sudden deterioration phases or critical transition points during their progressions. It was previously hypothesized that the disease progression can be modeled into three states (Fig. 1a): (A) a normal state (or a before-transition stage), representing a relatively healthy stage with high stability to external perturbations; (B) a pre-disease state (or a pre-transition stage), defined as the prelude to catastrophic deterioration into the disease state, occurring before the imminent phase transition point is reached, therefore, with low stability due to its dynamical structure; (C) a disease state (or an after-transition stage), representing a seriously deteriorated stage possibly with high stability, because the system usually finds it difficult to recover or return to the normal state even after treatment [7–9]. This is supported by the observations that there is usually sudden health catastrophic shift during the gradual progression of many chronic diseases [10–13]. Recently, a concept called dynamical network biomarker (DNB) was presented to detect the impending critical transition, or equivalently, the pre-disease state [14, 15]. The DNB method and its subsequent modifications have been successf (...truncated)