Biology subjects

Chong, K. H.

Publications and source records attributed to Chong, K. H..

3 recordsLinked to original sources

bioRxiv2024

An intuitive method to visualize Boolean network attractors landscape based on probability distribution from trajectories

MotivationBoolean network modeling is a coarse-grained modeling approach that has been highly valued for its simple abstract representation of biological regulation in molecular network. The simplified On and Off expression of gene or protein product is a qualitative representation of experimental findings for gene gets turned on or turned off. Boolean network constructed can be analyzed as having point attractor or cyclic attractor; however, current softwares can visualize the Boolean network attractors on a plane and not visualizing Boolean attractors landscape in a 3 dimensional view. ResultsHere, we propose an intuitive method to visualize Boolean network attractors land-scape based on probability distribution P of all possible trajectories in the state transition graph. We formulate a quasi-potential value U = - ln P that enables us to plot the Boolean network attractors landscape in 3D. There are two types of attractors in Boolean network: cyclic attractor and point attractor. Our intuitive method can visualize Boolean network in a landscape with point attractor (node labeled with yellow color) and cyclic attractor (node labeled with pink color). In the 3D landscape the attractors are located at the bottom of the directed graphs. The propose method can be applied in the study of networks in general, for example applications in directed graph, social network for popular users or e-commerce store to find favorite products as identified by attractors. Availability and implementationThe source code of Python implementation of the propose method can be downloaded from Boolean-net-attractor-Land URL link.

systems biology↗

bioRxiv2024

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

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.

bioinformatics↗

bioRxiv2018

A Monte Carlo method for in silico modeling and visualization of Waddington’s epigenetic landscape with intermediate details

Waddingtons epigenetic landscape is a classic metaphor for describing the cellular dynamics during the development modulated by gene regulation. Quantifying Waddingtons epigenetic landscape by mathematical modeling would be useful for understanding the mechanisms of cell fate determination. A few computational methods have been proposed for quantitative modeling of landscape; however, to model and visualize the landscape of a high dimensional gene regulatory system with realistic details is still challenging. Here, we propose a Monte Carlo method for modeling the Waddingtons epigenetic landscape of a gene regulatory network (GRN). The method estimates the probability distribution of cellular states by collecting a large number of time-course simulations with random initial conditions. By projecting all the trajectories into a 2-dimensional plane of dimensions i and j, we can approximately calculate the quasi-potential U (xi, xj) = -ln P (xi, xj), where P (xi, xj) is the estimated probability of an equilibrium steady state or a non-equilibrium state. A state with locally maximal probability corresponds to a locally minimal potential and such a state is called an attractor. Compared to the state-of-the-art methods, our Monte Carlo method can quantify the global potential landscape (or emergence behavior) of GRN for a high dimensional system. The same topography of landscape can be produced from deterministic or stochastic time-course simulations. The potential landscapes show that not only attractors represent stability, but the paths between attractors are also part of the stability or robustness of biological systems. We demonstrate the novelty and reliability of our method by plotting the potential landscapes of a few published models of GRN. Besides GRN-driven landscapes of cellular dynamics, the algorithm proposed can also be applied to studies of global dynamics (or emergence behavior) of other dynamical systems.

systems biology↗