Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network.
BACKGROUND
The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain.


RESULTS
In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems.


CONCLUSIONS
Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies.
School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China.
Bingbo
B
Wang
School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China.
Lin
L
Gao
School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China.
Qingfang
Q
Zhang
School of Computer Science and Technology, Xi'an University of Technology, Xi'an, People's Republic of China.
Aimin
A
Li
School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China; Institute of Software Engineering, Xidian University, Xi'an, People's Republic of China.
Yue
Y
Deng
School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China.
Xingli
X
Guo
D000544:Alzheimer Disease / Q000235:genetics; D015415:Biomarkers / Q000378:metabolism*; D015179:Colorectal Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D005260:Female; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D007938:Leukemia / Q000235:genetics; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D058977:Molecular Sequence Annotation; D010051:Ovarian Neoplasms / Q000235:genetics; D010641:Phenotype; D015398:Signal Transduction; D049490:Systems Biology
D015415:Biomarkers