Dynamical modeling of uncertain interaction-based genomic networks. Issue 13 (December 2015)

• Record Type: Journal Article
• Authors: Mohsenizadeh, Daniel; Hua, Jianping; Bittner, Michael; Dougherty, Edward
• Is Part Of: BMC bioinformatics. Volume 16:Issue 13(2015)
• Page Start: 1
• Page End: 12
• Publication Date: 2015-12
• Subjects: Dynamical model -- Uncertain networks -- Algorithm design; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S13-S3
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processesa priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory. Results We propose a new methodology for dynamical modeling of genomic networks that can utilize the interaction knowledge provided in public databases. The model assigns discrete states for physical entities, sets priorities among interactions based on information provided in the database, and updates each interaction based on associated node states. Whenever uncertainty in dynamics arises, it explores all possible outcomes. By using the proposed model, biologists can study regulation networks that are too complex for manual analysis. Conclusions The proposed approach can be effectively used for constructing dynamical models of interaction-based genomic networks without requiring a complete knowledge of all parameters affecting the network dynamics, and thus based on a small set of available data.