Engineering complex genetic functions in mammalian cells will require predictive models of gene regulation.
Increasingly sophisticated models of gene regulation that include stochastic effects are beginning to predict the behaviour of small synthetic networks.
Nested canalyzing functions have been studied recently in the context of Boolean network models of gene regulatory networks.
We emphasize the importance of appropriately designed studies to test models of gene evolution between orthologs and between paralogs.
We propose further experiments on homotypic clusters that would be useful in developing mechanistic models of gene expression.
The interpretation might include multiple representations (or models) of gene expression that highlight subcellular, cellular, organismal, or population levels of biological organization to be used for various purposes.
We study the inverse problem, or the "reverse-engineering" problem, for two abstract models of gene expression dynamics, discrete-time Boolean networks and continuous-time switching networks.
It is also shown that the majority of regulatory functions in many published models of gene regulatory and signaling networks are nested canalyzing.
The techniques presented in this paper are general, and can be used to analyze other logical or threshold Boolean network models of gene regulatory networks.
These models are therefore more relevant to polygenic obesity in human than models of gene inactivation.
Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks.
