Dynamical modeling of uncertain interaction-based genomic networks

BMC Bioinformatics

Proceedings

Michael Bittner¹  Jianping Hua¹  Edward R Dougherty²  Daniel N Mohsenizadeh³ 

[1] Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, 77843, College Station, TX, USA;Department of Electrical and Computer Engineering, Texas A&M University, 77843, College Station, TX, USA;Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, 77843, College Station, TX, USA;Department of Statistics, Texas A&M University, 77843, College Station, TX, USA;Department of Electrical and Computer Engineering, Texas A&M University, 77843, College Station, TX, USA;

关键词: Dynamical model;    Uncertain networks;    Algorithm design;   

DOI  :  10.1186/1471-2105-16-S13-S3

来源: Springer

【 摘 要 】

BackgroundMost 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.ResultsWe 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Mohsenizadeh et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.