Abstract
Hidden Variable Dynamic Modelling is a new approach to microarray analysis that quantitatively predicts the regulation of gene activity.
Highlights
In order to understand how gene networks function, it is necessary to identify their components and to quantitatively describe how they relate to one another [1,2,3]
Parameter estimation for a training set of five known p53 targets. (a) The model equation was solved to estimate values for the parameters basal transcription Bj sensitivity Sj, and degradation Dj for the five p53 targets DDB2, p21WAF1/CIP1, SESN1/hPA26, BIK, and TNFRSF10b/TRAILreceptor 2. (b) Simultaneously, the activity profile f(t) of p53 was derived from three separate microarray time courses
The time evolution of each gene transcript is described by the following non-autonomous linear differential equation for the rate of change in transcript concentration xj(t) of gene j at time t: dx j(t) dt
Summary
In order to understand how gene networks function, it is necessary to identify their components and to quantitatively describe how they relate to one another [1,2,3]. Experimental approaches can be applied to identify network components. Protein binding arrays and chromosome immunoprecipitation can be applied to identify transcription factor (TF)-binding sites and infer TF targets [7,8,9,10]. These approaches give a static view of the system. Purely experimental approaches cannot predict in a quantitative manner, and with statistical confidence, the dynamics of network activity without making an impractical number of experimental observations [11]
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