Journal article
Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks
BMC bioinformatics, v 8(1), 324
31 Aug 2007
PMID: 17764552
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
Conclusion: 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.
Metrics
Details
- Title
- Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks
- Creators
- Xiaohua Hu - Drexel University, Information ScienceFang-Xiang Wu - Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A9, Canada
- Publication Details
- BMC bioinformatics, v 8(1), 324
- Publisher
- Springer Nature
- Number of pages
- 18
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science; College of Information Science and Technology (1995-2013)
- Web of Science ID
- WOS:000252936000001
- Scopus ID
- 2-s2.0-38649114712
- Other Identifier
- 991014632299904721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology