Dynamically characterizing individual clinical change by the steady state of disease-associated pathway
| Autor: | Xiangtian Yu, Ying Tang, Fengnan Sun, Tao Zeng, Shaoyan Sun, Juan Zhao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Steady state (electronics)
0206 medical engineering 02 engineering and technology Computational biology Disease Biology lcsh:Computer applications to medicine. Medical informatics Biochemistry 03 medical and health sciences Structural Biology Attractor Humans Precision Medicine lcsh:QH301-705.5 Molecular Biology 030304 developmental biology Regulation of gene expression 0303 health sciences Research Applied Mathematics Precision medicine Phenotype Computer Science Applications Boolean network lcsh:Biology (General) Gene Expression Regulation lcsh:R858-859.7 DNA microarray 020602 bioinformatics |
| Zdroj: | BMC Bioinformatics BMC Bioinformatics, Vol 20, Iss S25, Pp 1-12 (2019) |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-019-3271-x |
| Popis: | Background Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes. Results Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states. Conclusions Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction. |
| Databáze: | OpenAIRE |
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