Structural Analysis of Petri Nets for Modeling and Analyzing Signaling Pathways

In this paper we present a Petri net model of Epidermal Growth Factor Receptor (EGFR) signaling to the Ras-Raf-Mek-Erk (Ras-MAPK) pathway. The EGFR-Ras-MAPK pathway has been strongly implicated in the development and progression of cancer. This has prompted the development of drugs targeting oncogenic proteins within this signaling pathway. However, the efficacy of these and other anti-cancer drugs has been hampered by the emergence of drug resistance. This has fuelled initiatives in developing combination therapies to concurrently target more than one oncogenic protein at a time. A central challenge of these multi-target therapy initiatives lie in identifying sets of targets which upon inhibition, provide improved therapeutic benefit over inhibiting the same targets alone. Physiochemical models based on ordinary differential equations (ODE) have been commonly employed to identify candidate drug target(s). However, the amount of knowledge required to dynamically model large signaling networks is currently lacking. This shortcoming has impeded attempts to use ODE-based models for identifying drug combinations. In this paper, we have implemented a Petri-net model of EGFR-Ras-MAPK signaling. An analytical method has been employed to identify nodes within the model from which a signal is irrecoverable once lost. Such nodes, called siphons, represent candidate drug target(s) for combination therapy. The potential utility of this method is highlighted by the identification of siphons composed of oncogenic nodes currently targeted by drugs in the clinic. Importantly, the method relies solely on structural information and constitutes a potentially valuable tool that could be scaled up to identify 'targetable' combinations in larger biochemical signaling networks.