MCLand: A Python program for drawing emerging shapes of Waddington's epigenetic landscape by Monte Carlo simulations

Abstract

Waddingtons epigenetic landscape is a powerful metaphor for illustrating the process of cell differentiation. Recently, it has been used to model cancer progression and stem cell reprogramming. User-friendly software for landscape quantification and visualization is needed to allow more modeling researchers to benefit from this theory. ResultsWe present MCLand, a Python program for plotting Waddingtons epigenetic landscape with a user-friendly graphical user interface. It models gene regulatory network (GRN) in ordinary differential equations (ODEs), and uses a Monte Carlo method to estimate the probability distribution of cell states from simulated time-course trajectories to quantify the landscape. Monte Carlo method has been tested on a few GRN models with biologically meaningful results. MCLand shows better intermediate details of kinetic path in Waddingtons landscape compared to the state-of-the-art software Netland. Availability and implementationThe source code and user manual of MCLand can be downloaded from https://mcland-ntu.github.io/MCLand/index.html.