Predictive mesoscale network model of cell fate decisions during c. elegans embryogenesis

The differentiation pathway of the nematode worm model organism C. elegans has been studied as a surrogate for future work on the human embryonic stem cell genetic networks. We extend earlier work on recursive networks by the introduction of a regularizer and more robust convergence algorithms, and by training the model to recapitulate experimental gene expression patterns rather than random expression patterns. We also assess the ability of the model to predict the expression profile on the next cell(s) in the lineage. The weight matrix from the model may be interpreted as a set of rules that guides the differentiation of the cells via a set of regulatory factors: internal genes or external entities. The activity of the regulatory factors shows patterns across the differentiation pathway that reflect the left-or right-hand split. Using these patterns, it may be possible to identify the actual factors responsible for the differentiation and to interpret the associated weights. The model was able to predict expression profiles of cells not used in training the model with a relatively low error rate.