Detecting the tipping points in a three-state model of complex diseases by temporal differential networks

Abstract

BackgroundThe progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a critical state or tipping point immediately preceding the disease state. Traditional biomarkers aim to identify a disease state by exploiting the information of differential expressions for the observed molecules, but may fail to detect a pre-disease state because there are generally little significant differences between the normal and pre-disease states. Thus, it is challenging to signal the pre-disease state, which actually implies the disease prediction.MethodsIn this work, by exploiting the information of differential associations among the observed molecules between the normal and pre-disease states, we propose a temporal differential network based computational method to accurately signal the pre-disease state or predict the occurrence of severe disease. The theoretical foundation of this work is the quantification of the critical state using dynamical network biomarkers.ResultsConsidering that there is one stationary Markov process before reaching the tipping point, a novel index, inconsistency score (I-score), is proposed to quantitatively measure the change of the stationary processes from the normal state so as to detect the onset of pre-disease state. In other words, a drastic increase of I-score implies the high inconsistency with the preceding stable state and thus signals the upcoming critical transition. This approach is applied to the simulated and real datasets of three diseases, which demonstrates the effectiveness of our method for predicting the deterioration into disease states. Both functional analysis and pathway enrichment also validate the computational results from the perspectives of both molecules and networks.ConclusionsAt the molecular network level, this method provides a computational way of unravelling the underlying mechanism of the dynamical progression when a biological system is near the tipping point, and thus detecting the early-warning signal of the imminent critical transition, which may help to achieve timely intervention. Moreover, the rewiring of differential networks effectively extracts discriminatively interpretable features, and systematically demonstrates the dynamical change of a biological system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-017-1320-7) contains supplementary material, which is available to authorized users.

Detecting early‐warning signals of influenza outbreak based on dynamic network marker

“…The 2‐fold change threshold is usually applied to recognize the significant changes in DNM score and obtain the warning signal. The DNM theory has been applied to a number of analyses of disease progression and biological processes to predict the critical states as well as their driven factors . In this work, by considering the flu outbreak process as a non‐linear dynamics process, we further applied the DNM method to detect the tipping point or the early‐warning signal of flu outbreak.…”

“…Unlike the traditional detection of the after‐outbreak state, the DNM enables the identification of the pre‐outbreak state or critical state that generally has no clear abnormalities but with future trending of deterioration or critical transition. This method has recently been successfully applied to a variety of biological progresses to detect the early‐warning signals to an irreversible catastrophic stage, such as the cell differentiation process, the process of cell fate decision, the critical transition in the immune checkpoint blockade‐responsive tumour, the multi‐stage deteriorations of T2D, acute lung injury, HCV induced liver cancer, cancer metastasis, and many others . In this study, DNM method was employed to explore the dynamical information based on a combination of city network and the high‐dimensional clinic hospitalization records, which are from over 278 clinics distributed in 23 wards in Tokyo, Japan, and 225 clinics distributed in 30 districts in Hokkaido, Japan.…”

Identifying critical state of complex diseases by single-sample Kullback–Leibler divergence

“…The theoretical background is our recently proposed DNB theory. Specifically, in order to theoretically and mathematically describe the dynamics of a complex disease, its evolution is usually modeled as a timedependent nonlinear dynamical system [23,69], in which the sudden deterioration is regarded as a state transition at a bifurcation point [16]. In ideal situation with small noise, when a complex system is near the critical point, among all observed variables there exists a dominant group defined as the DNB biomolecules, which satisfy the following three conditions based on the observed data [10]:…”

“…This DNB concept, directly from the critical slowingdown theory [15,16], provides statistical method to select relevant variables for the pre-disease state, that is, a small group of closely related variables (DNBs) convey early warning signals for the impending critical transition by some drastic statistical indices [17,18]. The DNB theory and its extensions have been applied to several cases, detected the tipping points of endocrine resistance [19] as well as cellular differentiation [20], investigated the immune checkpoint blockade [21], and helped to find the corresponding pre-disease states of several diseases [18,[22][23][24][25][26]. However, DNB method requires multiple samples at each time point, which are generally not available in clinics and other practical cases, thus significantly restricting the application of DNB method in most real cases.…”

Real-time forecast of influenza outbreak using dynamic network marker based on minimum spanning tree

“…The theoretical basis of MST-DNM is our recently proposed concept, the so-called dynamical network marker (DNM) [8], which is a dominant group of variables satisfying three generic properties for the impending critical transitions, that is, (1) the correlation between any pair of members in the DNM group rapidly increases; (2) the correlation between one member of the DNM group and any other non-DNM member rapidly decreases; (3) the standard deviation or coefficient of variation for any member in the DNM group drastically increases. Different from traditional biomarkers, DNM method aims at detecting the early-warning signal of the critical state before the occurrence of a catastrophic event, by mining the critical information from high dimensional time series data [8,9].The DNM method has been applied to real-world datasets and successfully identified the critical states for a number of biological processes, such as the critical state of cell differentiation [10], the tipping point during the cell fate decision process [11], the critical transition in the immune checkpoint blockade-responsive tumor [12], the multi-stage deteriorations of T2D [13], acute lung injury [14], HCV induced liver cancer [15], cancer metastasis [16], and other complex diseases [16][17][18][19][20]. However, to accurately predict the influenza outbreak, new computational method is required to explore and measure the criticality from a network perspective by considering the geographic information of a city.…”