Decision tree modeling predicts effects of inhibiting contractility signaling on cell motility.

Computational models of cell signaling networks typically are aimed at capturing dynamics of molecular components to derive quantitative insights from prior experimental data, and to make predictions concerning altered dynamics under different conditions. However, signaling network models have rarely been used to predict how cell phenotypic behaviors result from the integrated operation of these networks. We recently developed a decision tree model for how EGF-induced fibroblast cell motility across two-dimensional fibronectin-coated surfaces depends on the integrated activation status of five key signaling nodes, including a proximal regulator of transcellular contractile force generation, MLC (myosin light chain) [Hautaniemi et al, Bioinformatics 21: 2027 {2005}], but we have not previously attempted predictions of new experimental effects from this model.

Pubmed ID: 17408516 RIS Download