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Mohsenizadeh DN, Hua J, Bittner M, et al. Dynamical modeling of uncertain interaction-based genomic networks. BMC Bioinformatics. 2015;16:S3doi: 10.1186/1471-2105-16-S13-S3.
Mohsenizadeh, D. N., Hua, J., Bittner, M., & Dougherty, E. R. (2015). Dynamical modeling of uncertain interaction-based genomic networks. BMC bioinformatics, 16S3. https://doi.org/10.1186/1471-2105-16-S13-S3
Mohsenizadeh, Daniel N, et al. "Dynamical modeling of uncertain interaction-based genomic networks." BMC bioinformatics vol. 16 (2015): S3. doi: https://doi.org/10.1186/1471-2105-16-S13-S3
Mohsenizadeh DN, Hua J, Bittner M, Dougherty ER. Dynamical modeling of uncertain interaction-based genomic networks. BMC Bioinformatics. 2015;16:S3. doi: 10.1186/1471-2105-16-S13-S3. Epub 2015 Sep 25. PMID: 26423606; PMCID: PMC4596957.
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BMC Bioinformatics. 2015;16:S3. doi: 10.1186/1471-2105-16-S13-S3. Epub 2015 Sep 25.

Dynamical modeling of uncertain interaction-based genomic networks.

BMC bioinformatics

Daniel N Mohsenizadeh, Jianping Hua, Michael Bittner, Edward R Dougherty

PMID: 26423606 PMCID: PMC4596957 DOI: 10.1186/1471-2105-16-S13-S3

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 processes a 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.

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