Cellular decision making in differentiation, proliferation or apoptosis is mediated by molecular signaling processes, which control the regulation and expression of genes. Vice versa, the expression of genes can trigger the activity of signaling pathways. I summarize methodology by Markowetz et al. known as the Nested Effects Models (NEMs) to reconstruct static non-transcriptional networks using subset relationships from perturbation data and bring out its limitation to model slow-going biological processes like cell differentiation.
I introduce and describe new statistical methodologies called Dynamic Nested Effects Models (DNEMs) and Cyclic Dynamic Nested Effects Models (CDNEMs) for analyzing the temporal interplay of cell signaling and gene expression. DNEMs and CDNEMs are Bayesian models of signal propagation in a network. They decompose observed time delays of multiple step signaling processes into single steps. Time delays are assumed to be exponentially distributed. Rate constants of signal propagation are model parameters, whose joint posterior distribution is assessed via Gibbs sampling. They hold information on the interplay of different forms of biological signal propagation: Molecular signaling in the cytoplasm acts at high rates, direct signal propagation via transcription and translation at intermediate rates, while secondary effects operate at low rates. I evaluate my methods in simulation experiments and demonstrate their practical applications to embryonic stem cell development in mice. The results from these models explain how stem cells could succeed to carry out differentiation to specialized cells of the body such as muscle cells or neurons, a process that goes in one direction. The inferred molecular communication underlying such a process proposes how organisms protect themselves against the reversal of cell differentiation and thereby against cancer
