Figure 4.

State space modeling predicts transcription factor influence. (a) Conceptual scheme of the state space modeling. An unknown function f (red square) relates the values of latent variables Z(t) and Z(t + 1) (for all t) corresponding to consecutive time measurements. Learning algorithms iteratively optimize the function f mapping latent values of transcription factors to changes to target genes (and transcription factors themselves at time t + 1). (b) The whole dataset (from 0 to 20 minutes of KNO3 treatment) has been learnt by state space modeling (validated to be predictive in a leave-one-last approach; Table 2). The resulting f function has learnt possible connections and can be displayed as an influence matrix. SPL9 is a transcription factor predicted to be a potential bottleneck and is further experimentally studied.

Krouk et al. Genome Biology 2010 11:R123   doi:10.1186/gb-2010-11-12-r123
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