9th International Conference on Bioinformatics, Abstracts
巻, 号, ページ
pp. 50
出版年月
2010年9月
出版者
和文:
英文:
会議名称
和文:
英文:
9th International Conference on Bioinformatics
開催地
和文:
英文:
Tokyo
アブストラクト
Background
In the analysis of gene regulatory networks, we have to consider many possible behaviors depending on initial conditions, scenarios of external inputs, and settings of parameters. Quantitative methods, such as numerical simulations of ordinary differential equations, are not suitable to analyze all of possible behaviors, since they need concrete values for biological parameters, which are usually unknown. Therefore, we developed a qualitative method for analyzing all of possible behaviors of gene regulatory networks, by focusing on essential qualitative features of behaviors.
Results
In our method, behaviors are captured in transition systems using propositions for genes state (ON or OFF), and threshold values for genes activation/inhibition. We characterized possible behaviors of networks by specifying in Linear Temporal Logic how concentrations of genes’ products change according to its state, how genes get ON or OFF, and general rules for order relation on thresholds. This constraint is intended to cover all possible behaviors of networks. Then, biological properties to be checked, such as reachability, stability and oscillation etc. are also described in Linear Temporal Logic. We can check two types of properties, which are possible property and global property, by analyzing the logical combinations of the constraint and the property. Possible property is that there exists some behavior of the network which satisfies a property. Global property is that all possible behaviors satisfy a property. For the demonstration of usability of our method, we analyzed the mucus production in Pseudomonas aeruginosa as an example. We checked the hypothesis that the wild-type bacteria have multi-stationarity where one is non-mucoid and the other is mucoid in silico. The result of checking was true. Based on this result, biologists will be motivated to verify the hypothesis in vivo.
Conclusions
We developed a method that considers all possible behaviors of gene regulatory networks in purely qualitative way. We applied our method to analysis of mucus production in Pseudomonas aeruginosa and showed that the bacteria can be in mucoid state and non-mucoid state. Our method will be useful for biologists to test many hypotheses and predict behaviors of networks in silico.
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