Using Nets-Within-Nets for Modeling Differentiating Cells in the Epigenetic Landscape

Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters. 1 Introduction In this work the authors propose the use of a high-level Petri net formalism for modeling developmental processes at the cell level, taking explicitly into account the role of epigenetic regulation. The term " epigenetics " can refer to all possible mechanisms " acting on the genomic information between genotype and phenotype " [1], changing the actual condition of a system without changing the underlying DNA sequence. " For example, even though the vast majority of cells in a multicellular organism share an identical genotype, organismal development generates a diversity of cell types with disparate, yet stable, profiles of gene expression and distinct cellular functions. Thus, cellular differentiation may be considered an epigenetic phenomenon, largely governed by changes in what is described as the " epigenetic landscape " " [1, 2]. The epigenetic landscape was initially proposed as a visual metaphor [2] for describing the scenario where developmental processes take place. Cell type differentiation is represented as increasingly irreversible, as ridges rise between the valleys where the different cells are traveling [3]. These two metaphoric terms refer to a quasi-potential scenario, as proposed in [6]. In computational terms, an epigenetic landscape can be interpreted as the ensemble of all possible regulation configurations a complex system can be in. Unfortunately, the huge number of variables involved in epigenetic regulation make this task extremely challenging. The proposed model, based on Petri Nets, is designed to simulate and conceptually characterize the contributions of the epigenetic regulation from those of other mechanisms within the cells, including transcriptional and post-transcriptional regulation, and metabolism. In this work we present the idea and the main properties of the model. Experimental results are not yet available but will be presented at the conference.