Construction and Experimental Validation of a Petri net Model of Wnt/β-catenin Signaling

Abstract

The Wnt/β-catenin signaling pathway is important for multiple developmental processes and tissue maintenance in adults. Consequently, deregulated signaling is involved in a range of human diseases including cancer and developmental defects. A better understanding of the intricate regulatory mechanism and effect of physiological (active) and pathophysiological (hyperactive) WNT signaling is important for predicting treatment response and developing novel therapies. The constitutively expressed CTNNB1 (commonly and hereafter referred to as β-catenin) is degraded by a destruction complex, composed of amongst others AXIN1 and GSK3. The destruction complex is inhibited during active WNT signaling, leading to β-catenin stabilization and induction of β-catenin/TCF target genes. In this study we investigated the mechanism and effect of β-catenin stabilization during active and hyperactive WNT signaling in a combined in silico and in vitro approach. We constructed a Petri net model of Wnt/β-catenin signaling including main players from the plasma membrane (WNT ligands and receptors), cytoplasmic effectors and the downstream negative feedback target gene AXIN2. We validated that our model can be used to simulate both active (WNT stimulation) and hyperactive (GSK3 inhibition) signaling by comparing our simulation and experimental data. We used this experimentally validated model to get further insights into the effect of the negative feedback regulator AXIN2 upon WNT stimulation and observed an attenuated β-catenin stabilization. We furthermore simulated the effect of APC inactivating mutations, yielding a stabilization of β-catenin levels comparable to the Wnt-pathway activities observed in colorectal and breast cancer. Our model can be used for further investigation and viable predictions of the role of Wnt/β-catenin signaling in oncogenesis and development.

BioASF: a framework for automatically generating executable pathway models specified in BioPAX

“…We first made BioPAX models for both networks, used the BioASF’s plugin to express the interaction rules required by the models, plugged the corresponding analysis module in BioASF, prepared the initial concentration values for the physical entities occurring in the models, generated Java code for all agents and finally ran the simulations. We observed that the results of the simulations performed using BioASF were similar to the results reported by the researchers in Bonzanni et al (2013) and Jacobsen et al (2016) . In the following sections, we give a brief explanation of the simulations and show the results.…”

“…In order to validate BioASF, we have simulated two different biological network models in BioASF: a gene regulatory network modeling the haematopoietic stem cell (HSC) regulators ( Bonzanni et al , 2013 ) and a signal transduction network modeling the Wnt / β-catenin signaling pathway ( Jacobsen et al , 2016 ). Both original models have been developed in Petri-net.…”

“…This leads to CTNNB1 accumulation and translocation to the nucleus, where it binds TCF/LEF and activates transcription of WNT target genes. In Jacobsen et al (2016) , the authors build a Wnt/β-catenin signaling model based on interactions, well established in literature. Signaling is initiated by the binding of external WNT ligand to the receptor, FZD, and the co-receptor, LRP.…”

“…Similar to the haematopoiesis simulation, for the simulation of the WNT/β-catenin model, we extracted a number of rules from the description of the original in silico experiment ( Jacobsen et al , 2016 ), and plugged them in BioASF. We recognized three categories of rules: template reaction regulation rules, template reaction rules and conversion rules.…”

“…This module initially populates the pre-defined concentration values of all proteins, complexes and genes, creates a file for writing the results of the simulations, utilizes BioASF to execute one simulation step, and writes the CTNNB1 levels in the file. In line with the original in silico experiment ( Jacobsen et al , 2016 ), the model is simulated with initial WNT levels ranging from 0 to 5 over 100 steps. The simulation for each initial WNT level is repeated 50 times and the CTNNB1 levels for each step is stored in a file.…”