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http://www.biomedcentral.com/news/20040303/01
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Proportional dynamics illustrates commonality of gene expression levels in all organisms | By Cathy Holding
With an ever-increasing number of genomes available for analysis, there has been a shift in emphasis away from the study of single genes and a greater attempt to understand gene expression at the network or systems level. A report in the March 1 PNAS shows that power laws—a universal mathematical dynamic—govern the process.
Hiroki Ueda and colleagues at the Center for Developmental Biology describe the mathematical principle underlying observed levels of gene expression. They used information from public databases of whole genome sequences and from their own microarray analyses. Proportional dynamics, also known as “rich-travel-more,” showed that power law levels of gene expression were observed not only in different organisms, but also within discrete organs or at specific developmental times in the same organism (PNAS, DOI:10.1073/pnas.0306244101, March 1, 2004).
A power law is a mathematical relationship between two quantities where one quantity varies as a power of another. In simpler terms, it describes a situation where a small change in one variable can result in a large change in another variable.
In gene expression, a power law describes the relationship between the level of gene expression and the frequency of its occurrence. This means that a few genes are highly expressed while most genes are expressed at low levels.
The power law in gene expression helps us understand patterns of gene expression and how it relates to cellular function. It also provides insight into how genes are regulated and how different factors can influence their expression levels.
While the power law is a common phenomenon in gene expression, there are some exceptions. For example, certain genes may follow a different distribution pattern, such as a normal distribution, rather than a power law.
The power law in gene expression has implications for how we design experiments and interpret data. It reminds us that a small number of highly expressed genes may have a disproportionate impact on the overall gene expression profile. It also highlights the importance of studying rare or low-expressed genes that may be critical for certain biological processes.