Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks.
BACKGROUND
Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.


RESULTS
In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.


CONCLUSIONS
Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.
Hu
Xiaohua
X
College of Information Science & Technology, Drexel University, Philadelphia, PA 19104, USA. thu@cis.drexel.edu
Wu
Fang-Xiang
FX
D000465:Algorithms; D002468:Cell Physiological Phenomena; D016000:Cluster Analysis; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D025941:Protein Interaction Mapping; D049490:Systems Biology / Q000379:methods*