We analyzed the developmental switch to sporulation of a multinucleate Physarum polycephalum plasmodial cell, a complex response to phytochrome photoreceptor activation. Automatic construction of Petri nets from trajectories of differential gene expression in single cells revealed alternative, genotype-dependent interconnected developmental routes and identified metastable states, commitment points, and subsequent irreversible steps together with molecular signatures associated with cell fate decision and differentiation. Formation of transition-invariants in mutants that are locked in a proliferative state is remarkable considering the view that oncogenic alterations may cause the formation of cancer attractors. We conclude that the Petri net approach is useful to probe the Waddington landscape of cellular reprogramming, to disentangle developmental routes for the reconstruction of the gene regulatory network, and to understand how genetic alterations or physiological conditions reshape the landscape eventually creating new basins of attraction. Unraveling the complexity of pathogenesis, disease progression, and drug response or the analysis of attractor landscapes in other complex systems of uncertain structure might be additional fields of application.

Quantitative analysis of differential gene expression is of central importance in molecular life sciences. The Gene eXpression Profiling technology (GeXP) relies on multiplex RT-PCR and subsequent capillary electrophoretic separation of the amplification products and allows to quantify the transcripts of up to approximately 35 genes with a single reaction and one dye. Here, we provide a kinetic model of primer binding and PCR product formation as the rational basis for taking and evaluating calibration curves. With the help of a purposeful designed data processing workflow supported by easy-to-use Perl scripts for calibration, data evaluation, and quality control, the calibration procedure and the model predictions were confirmed and the robustness and linearity of transcript quantification demonstrated for differentiatingPhysarum polycephalum plasmodial cells. We conclude that GeXP analysis is a robust, sensitive, and useful method when the transcripts of tens to few hundred genes are to be precisely quantified in a high number of samples.