A Model of the Mammalian Circadian Oscillator Including the REV-ERB.ALPHA. Module
スポンサーリンク
概要
- 論文の詳細を見る
Many cellular and physiological processes have been shown to display a rhythm of about 24 hours. This so-called circadian rhythm is based on a system of interlocked negative and positive molecular feedback loops. Here we extend a previous model of the circadian oscillator by including REV-ERBa as an additional component. This new model will allow us to investigate the function of an additional negative feedback loop via REV-ERBα. We obtain circadian oscillations with the correct period and phase relations between clock components. Parameter variations that correspond to clock-gene mutations reproduce experimental results: With parameter variations mimicking the <I>Bmal1-/-</I> and the <I>Per2</I><SUP>Brdm1</SUP> mutation the oscillations cease to exist. In contrast, the system shows sustained oscillations if we use a parameter set that reflects the <I>Rev-erbα</I> mutation. The model also accounts for the differential effect of the <I>Cry1-/-</I> and <I>Cry2-/-</I> mutations on the circadian period. The simulations of the extended model indicate that the original model is robust with respect to the incorporation of the additional component. Depending on the kinetics of the <I>Per2/Cry</I> transcriptional activation by BMAL1, an increasing BMAL1 expression leads to either an increase or decrease of the clock period. This indicates that overexpression experiments could help to characterize the impact of BMAL1 on <I>Per2/Cry</I> transcription.
- 日本バイオインフォマティクス学会の論文
日本バイオインフォマティクス学会 | 論文
- Performance Improvement in Protein N-Myristoyl Classification by BONSAI with Insignificant Indexing Symbol
- A combined pathway to simulate CDK-dependent phosphorylation and ARF-dependent stabilization for p53 transcriptional activity
- A versatile petri net based architecture for modeling and simulation of complex biological processes
- XML documentation of biopathways and their simulations in Genomic Object Net
- Prediction of debacle points for robustness of biological pathways by using recurrent neural networks