Power-law distribution of gene expression fluctuations |
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Authors: | JC Nacher T Ochiai |
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Institution: | a Department of Complex Systems, Future University-Hakodate, 116-2 Kamedanakano-cho Hakodate, Hokkaido 041-8655, Japan b Faculty of Engineering, Toyama Prefectural University, 5180 Kurokawa Imizu-shi, Toyama 939-0398, Japan |
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Abstract: | Large-scale genomic technologies has opened new possibilities to infer gene regulatory networks from time series data. Here, we investigate the relationship between the dynamic information of gene expression in time series and the underlying network structure. First, our results show that the distribution of gene expression fluctuations (i.e., standard deviation) follows a power-law. This finding indicates that while most genes exhibit a relatively low variation in expression level, a few genes are revealed as highly variable genes. Second, we propose a stochastic model that explains the emergence of this power-law behavior. The model derives a relationship that connects the standard deviation (variance) of each node to its degree. In particular, it allows us to identify a global property of the underlying genetic regulatory network, such as the degree exponent, by only computing dynamic information. This result not only offers an interesting link to explore the topology of real systems without knowing the real structure but also supports earlier findings showing that gene networks may follow a scale-free distribution. |
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Keywords: | Fluctuations Gene expression Scaling-law Stochastic theory |
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