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bioRxiv · 10.1101/2024.01.15.575795

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

Chong, K. H.·Zhang, X.·Zhu, L.·Zheng, J.
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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.

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Chong, K. H., Zhang, X., Zhu, L., Zheng, J.. 2024-01-17. MCLand: A Python program for drawing emerging shapes of Waddington's epigenetic landscape by Monte Carlo simulations. https://doi.org/10.1101/2024.01.15.575795

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