Back

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.

2024-01-17 bioinformatics
10.1101/2024.01.15.575795 bioRxiv
Show 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.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Briefings in Bioinformatics
◐ 354 papers in training set
Top 0.4%
12.1%
Similar papers in this journal
2
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 0.2%
8.0%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 6%
8.0%
Similar papers in this journal
4
BMC Bioinformatics
○ 457 papers in training set
Top 1%
7.4%
Similar papers in this journal
5
F1000Research
○ 88 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
6
Bioinformatics
◐ 1204 papers in training set
Top 3%
6.8%
7
Biosystems
○ 31 papers in training set
Top 0.1%
5.6%
50% of probability mass above
8
PLOS ONE
● 5266 papers in training set
Top 31%
4.4%
9
Computational Biology and Chemistry
○ 28 papers in training set
Top 0.1%
3.5%
10
Physical Biology
○ 46 papers in training set
Top 0.3%
2.2%
11
Computers in Biology and Medicine
○ 128 papers in training set
Top 2%
1.8%
12
Journal of Computational Chemistry
○ 13 papers in training set
Top 0.1%
1.8%
13
PLOS Computational Biology
● 1863 papers in training set
Top 14%
1.8%
14
Journal of Biomolecular Structure and Dynamics
○ 43 papers in training set
Top 0.8%
1.1%
15
The Journal of Chemical Physics
● 56 papers in training set
Top 0.4%
1.1%
16
IEEE/ACM Transactions on Computational Biology and Bioinformatics
● 38 papers in training set
Top 0.7%
1.1%
17
RSC Advances
● 22 papers in training set
Top 0.5%
1.1%
18
Journal of Theoretical Biology
○ 162 papers in training set
Top 2%
1.1%
19
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.2%
1.1%
20
PeerJ
◐ 308 papers in training set
Top 9%
1.0%
21
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 2%
0.9%
22
BMC Genomics
○ 406 papers in training set
Top 7%
0.9%
23
Frontiers in Cell and Developmental Biology
○ 233 papers in training set
Top 5%
0.9%
24
The Journal of Physical Chemistry B
● 167 papers in training set
Top 2%
0.6%
25
Frontiers in Genetics
○ 230 papers in training set
Top 6%
0.6%
26
Methods
○ 34 papers in training set
Top 0.7%
0.6%
27
SoftwareX
○ 15 papers in training set
Top 0.3%
0.6%
28
Neurocomputing
○ 13 papers in training set
Top 0.4%
0.6%
29
Life
○ 29 papers in training set
Top 1%
0.6%
30
Mathematics
○ 11 papers in training set
Top 0.3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.