Network biomarker for quantifying regular state of a biological system, and dynamic network biomarker for quantifying critical state of a biological system

2020 ◽  
Author(s):  
Luonan Chen
Keyword(s):  
Biological System ◽  
Critical State ◽  
Dynamic Network ◽  
Network Biomarker

scholarly journals Dynamic Network Biomarker of Pre-Exhausted CD8+ T Cells Contributed to T Cell Exhaustion in Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiaqi Hu ◽  
Chongyin Han ◽  
Jiayuan Zhong ◽  
Huisheng Liu ◽  
Rui Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
T Cells ◽  
T Cell ◽  
Dynamic Network ◽  
Cell Subpopulation ◽  
T Cell Exhaustion ◽  
Data Set ◽  
Cell Exhaustion ◽  
Network Biomarker ◽  
Core Genes

Immunotherapy has achieved positive clinical responses in various cancers. However, in advanced colorectal cancer (CRC), immunotherapy is challenging because of the deterioration of T-cell exhaustion, the mechanism of which is still unclear. In this study, we depicted CD8+ T-cell developmental trajectories and characterized the pre-exhausted T cells isolated from CRC patients in the scRNA-seq data set using a dynamic network biomarker (DNB). Moreover, CCT6A identified by DNB was a biomarker for pre-exhausted T-cell subpopulation in CRC. Besides, TUBA1B expression was triggered by CCT6A as DNB core genes contributing to CD8+ T cell exhaustion, indicating that core genes serve as biomarkers in pre-exhausted T cells. Remarkably, both TUBA1B and CCT6A expressions were significantly associated with the overall survival of COAD patients in the TCGA database (p = 0.0082 and p = 0.026, respectively). We also observed that cellular communication between terminally differentiated exhausted T cells and pre-exhausted T cells contributes to exhaustion. These findings provide new insights into the mechanism of T-cell exhaustion and provide clue for targeted immunotherapy in CRC.

Identifying critical states of hepatocellular carcinoma based on single-sample dynamic network biomarkers combined with simulated annealing algorithm

2021 ◽  
Vol 16 ◽  
Author(s):  
Hongqian Zhao ◽  
Jie Gao ◽  
Yichen Sun ◽  
Yujie Wang ◽  
Tianhao Guan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Simulated Annealing ◽  
Critical State ◽  
Simulated Annealing Algorithm ◽  
Dynamic Network ◽  
Disease State ◽  
Statistical Characteristics ◽  
Low Grade ◽  
Network Biomarkers ◽  
Annealing Algorithm

Background: Hepatocellular carcinoma(HCC) is one of the most common malignant tumors. Due to the insidious onset and poor prognosis, most patients have reached the advanced stage at the time of diagnosis. Objective: Studies have shown thatdynamic network biomarkers (DNB) can effectively identify the critical state of complex diseases such as HCC from normal state to disease state. Therefore, it is very important to detect DNB efficiently and reliably. Methods: This paper selects a dataset containing eight HCC disease states. First, anindividual-specific network is constructed for each sample and features are extracted. In the context of this network, a simulated annealing algorithm is used to search for potential dynamic network biomarker modules, and the evolution of HCC is determined. Results: In fact, in the period of low-grade dysplasia (LGD) and high-grade dysplasia (HGD), DNB will send an indicative warning signal, which means that liver dysplasia is a very important critical state in the development of HCC disease. Compared with landscape dynamic network biomarkers method (L-DNB), our method can not only describe the statistical characteristics of each disease state, but also yield better results including getting more DNBs enriched in HCC related pathways. Conclusion: The results of this study may be of great significance to the prevention and early diagnosis of HCC.

Understanding migraine using dynamic network biomarkers

Cephalalgia ◽  
2014 ◽  
Vol 35 (7) ◽  
pp. 627-630 ◽  
Author(s):  
Markus A Dahlem ◽  
Jürgen Kurths ◽  
Michel D Ferrari ◽  
Kazuyuki Aihara ◽  
Marten Scheffer ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Dynamic Network ◽  
Tipping Point ◽  
Trigger Factors ◽  
Clinical Neuroscience ◽  
Complex Interactions ◽  
Network Biomarker ◽  
Network Biomarkers ◽  
Premonitory Symptoms

Background Mathematical modeling approaches are becoming ever more established in clinical neuroscience. They provide insight that is key to understanding complex interactions of network phenomena, in general, and interactions within the migraine-generator network, in particular. Purpose In this study, two recent modeling studies on migraine are set in the context of premonitory symptoms that are easy to confuse for trigger factors. This causality confusion is explained, if migraine attacks are initiated by a transition caused by a tipping point. Conclusion We need to characterize the involved neuronal and autonomic subnetworks and their connections during all parts of the migraine cycle if we are ever to understand migraine. We predict that mathematical models have the potential to dismantle large and correlated fluctuations in such subnetworks as a dynamic network biomarker of migraine.

Dynamic network biomarker analysis discovers IbNAC083 in the initiation and regulation of sweet potato root tuberization

2021 ◽  
Author(s):  
Shutao He ◽  
Hongxia Wang ◽  
Xiaomeng Hao ◽  
Yinliang Wu ◽  
Xiaofeng Bian ◽  
...  
Keyword(s):  
Sweet Potato ◽  
Dynamic Network ◽  
Potato Root ◽  
Network Biomarker ◽  
Biomarker Analysis

scholarly journals Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage

2021 ◽  
Author(s):  
Chengming Zhang ◽  
Hong Zhang ◽  
Jing Ge ◽  
Tingyan Mi ◽  
Xiao Cui ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Tipping Point ◽  
Critical Transition ◽  
Skin Model ◽  
Uvb Irradiation ◽  
Network Biomarker ◽  
Skin Response ◽  
Landscape Dynamic ◽  
Skin Lightening ◽  
3D Skin

Abstract Skin, as the outmost layer of human body, is frequently exposed to environmental stressors including pollutants and ultraviolet (UV), which could lead to skin disorders. Generally, skin response process to ultraviolet B (UVB) irradiation is a nonlinear dynamic process, with unknown underlying molecular mechanism of critical transition. Here, the landscape dynamic network biomarker (l-DNB) analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels. The advanced l-DNB analysis approach showed that: (i) there was a tipping point before critical transition state during pigmentation process, validated by 3D skin model; (ii) 13 core DNB genes were identified to detect the tipping point as a network biomarker, supported by computational assessment; (iii) core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening, validated by independent human skin data. Overall, this study provides new insights for skin response to repetitive UVB irradiation, including dynamic pathway pattern, bi-phasic response, and DNBs for skin lightening change, and enables us to further understand the skin resilience process after external stress.

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