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IJDMB
2007
119views more  IJDMB 2007»
13 years 4 months ago
Simulation study in Probabilistic Boolean Network models for genetic regulatory networks
: Probabilistic Boolean Network (PBN) is widely used to model genetic regulatory networks. Evolution of the PBN is according to the transition probability matrix. Steady-state (lon...
Shuqin Zhang, Wai-Ki Ching, Michael K. Ng, Tatsuya...
BMCBI
2010
172views more  BMCBI 2010»
13 years 4 months ago
Comparison of evolutionary algorithms in gene regulatory network model inference
Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineeri...
Alina Sîrbu, Heather J. Ruskin, Martin Crane
WABI
2010
Springer
167views Bioinformatics» more  WABI 2010»
13 years 3 months ago
Quantifying the Strength of Natural Selection of a Motif Sequence
Quantification of selective pressures on regulatory sequences is a central question in studying the evolution of gene regulatory networks. Previous methods focus primarily on sing...
Chen-Hsiang Yeang
BMCBI
2007
122views more  BMCBI 2007»
13 years 4 months ago
Dissecting complex transcriptional responses using pathway-level scores based on prior information
Background: The genomewide pattern of changes in mRNA expression measured using DNA microarrays is typically a complex superposition of the response of multiple regulatory pathway...
Harmen J. Bussemaker, Lucas D. Ward, André ...
CIBCB
2009
IEEE
13 years 5 months ago
Steady-state analysis of genetic regulatory networks modeled by nonlinear ordinary differential equations
Although Ordinary Differential Equations (ODEs) have been used to model Genetic Regulatory Networks (GRNs) in many previous works, their steady-state behaviors are not well studied...
Haixin Wang, Lijun Qian, Edward R. Dougherty