Stochastic Petri Net Models for the Study of Macrophage Reprogramming In recent years, Petri nets are being used for modeling complex biological systems. Examples include the modeling of biochemical reactions, protein interaction networks, gene regulation networks and the immune system. In this work, Petri nets are used to model the innate immune response to a bacterial infection. The objective of this work is to study how changes in macrophage phenotype impact the regulation and resolution of the immune response. From simulations performed, we have observed that the conversion between a M1 proinflammatory phenotype to a M2 regulatory phenotype during an inflammatory response is an important process to avoid a state of persistent inflammation.

Disentangling a complex response in cell reprogramming and probing the Waddington landscape by automatic construction of Petri nets. Abstract   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 representing finite state machines assembled from trajectories of differential gene expression in single cells revealed alternative, genotypedependent interconnected developmental routes and identified reversible steps, metastable states, commitment points, and subsequent irreversible steps together with molecular signatures associated with cell fate decision and differentiation. Formation of cyclic transits identified by 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, drug response or the analysis of attractor landscapes in other complex systems of uncertain structure might be additional fields of application.