The mathematical model is used to advance the modeling research of gene regulation network to the regulatory element of non-coding area.

Recently, the International Academic Journal of the Proceedings of the National Academy of Sciences (PNAS) published online by the Chinese Academy of Sciences Institute of Mathematics and Systems Science and the United States Stanford University, Tsinghua University and other units of researchers in cooperation with the genetic regulation network modeling results, proposed the use of matching gene expression and chromosate access data to depict the interaction of smooth control elements and trans-control elements of the mathematical modeling network from the coding gene to the non-coding region of the regulatory elements, is expected to be used to annotate disease-related genetic variation.
the central law of molecular biology points to the flow of genetic information from DNA encoded genes to RNA to proteins.
when a gene is tallyed as RNA, it is called "expression."
gene regulatory network, i.e. the interaction between proteins and DNA that precisely control gene expression levels.
network is at the heart of almost all biological processes.
Under certain conditions, the initiation or cessation, enhancement or inhibition of specific gene expression is the molecular basis for cells to select regulatory elements and interactions in the genome to complete basic life activities and respond to external stimuli.
and tissue- and cell-specific gene regulation that shapes different dedicates and is the cornerstone of health and disease research.
to illustrate the regulatory elements on which gene selective expression depends and the molecular mechanisms of their interactions, gene regulation needs to be modeled.
how to cooperate with trans-regulation elements such as transcription factors and trans-regulation elements such as enhancers to make rapid transcription of a gene in a specific cell environment is the core issue of gene regulation network research.
Scientists from China and the United States worked closely together to study the interactions between the core components of gene regulation research (transcription factor TF, chromosin regulation factor CR, and regulatory element RE), and then modeled quantitative prediction of gene expression, developing a new method of network inference PECA.
PECA focuses on modeling three key aspects of transcription regulation: first, to infer the binding point of CR on RE based on the interaction between CR and sequence-specific TF;
PECA infers that condition-specific gene regulatory networks annotate the functions of non-coding regions given in the QTL study, thus explaining the molecular mechanism level of point mutations and the link between structural variation and dedication that occur in non-coding regulatory regions.
The study used data from the Encyclopedia of DNA Components program after the Human Genome Project, especially some cell types with matching chromosome states and gene expression data, and interpreted these important data to greatly facilitate understanding of gene regulatory networks in the post-genome era.
Professor Wang Yongxiong of Stanford University, who led the study, Wang Yong, a researcher at the Faculty of Several Sciences of the Chinese Academy of Sciences, and Jiang Rui, an associate professor in the Department of Automation at Tsinghua University, received funding from the National Institutes of Health, the Chinese Academy of Sciences' Class B Pilot Project and the National Natural Fund Commission, respectively.
author of this paper is Dr. Dorenzana, a ph.D. student at several colleges.
.