A stochastic spectral analysis of transcriptional regulatory cascades

The past decade has seen great advances in our understanding of the role of noise in gene regulation and the physical limits to signaling in biological networks. Here, we introduce the spectral method for computation of the joint probability distribution over all species in a biological network. The...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the National Academy of Sciences - PNAS Ročník 106; číslo 16; s. 6529
Hlavní autoři: Walczak, Aleksandra M, Mugler, Andrew, Wiggins, Chris H
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 21.04.2009
Témata:
Computational Biology - methods
Down-Regulation
Gene Expression Regulation
Signal Transduction - genetics
Stochastic Processes
Transcription, Genetic
Up-Regulation
ISSN:1091-6490, 1091-6490
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The past decade has seen great advances in our understanding of the role of noise in gene regulation and the physical limits to signaling in biological networks. Here, we introduce the spectral method for computation of the joint probability distribution over all species in a biological network. The spectral method exploits the natural eigenfunctions of the master equation of birth-death processes to solve for the joint distribution of modules within the network, which then inform each other and facilitate calculation of the entire joint distribution. We illustrate the method on a ubiquitous case in nature: linear regulatory cascades. The efficiency of the method makes possible numerical optimization of the input and regulatory parameters, revealing design properties of, e.g., the most informative cascades. We find, for threshold regulation, that a cascade of strong regulations converts a unimodal input to a bimodal output, that multimodal inputs are no more informative than bimodal inputs, and that a chain of up-regulations outperforms a chain of down-regulations. We anticipate that this numerical approach may be useful for modeling noise in a variety of small network topologies in biology.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1091-6490
1091-6490
DOI:10.1073/pnas.0811999106