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PyKappa: Rule-based modeling in Python

Alpay, B. A.; Lin, A.; Damiani, A. C.; Fontana, W.

2026-09-24 systems biology
10.64898/2026.09.18.752660 bioRxiv
Show abstract

Rule-based languages have proven effective for modeling systems of interacting structured entities as typically encountered in chemistry and molecular biology. We present PyKappa, a rule-based modeling package written in Python whose interpreted nature enables interactive simulation and analysis, including by agentic AI. The package seeks to broaden the base of developers by utilizing a widely known programming language and serves as an easy-to-deploy teaching tool. Using PyKappa, we conduct a case study of phase separation.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 12%
14.9%
Similar papers in this journal
2
PLOS Computational Biology
● 1863 papers in training set
Top 4%
7.9%
Similar papers in this journal
3
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 0.3%
6.7%
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4
Bioinformatics
◐ 1204 papers in training set
Top 3%
6.7%
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5
BMC Bioinformatics
○ 457 papers in training set
Top 1%
6.3%
Similar papers in this journal
6
Scientific Reports
○ 3612 papers in training set
Top 31%
3.2%
7
Ecology and Evolution
○ 267 papers in training set
Top 3%
2.6%
8
Protein Science
○ 246 papers in training set
Top 2%
2.4%
50% of probability mass above
9
Biosystems
○ 31 papers in training set
Top 0.2%
2.4%
10
iScience
○ 1154 papers in training set
Top 11%
2.4%
11
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.7%
2.4%
12
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.7%
13
Gigabyte
● 62 papers in training set
Top 0.6%
1.7%
14
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 2%
1.7%
15
Frontiers in Physiology
○ 106 papers in training set
Top 1%
1.4%
16
ACS Omega
● 105 papers in training set
Top 2%
1.3%
17
Royal Society Open Science
● 214 papers in training set
Top 4%
1.1%
18
Frontiers in Systems Biology
○ 10 papers in training set
Top 0.1%
1.1%
19
Mathematical Biosciences
○ 49 papers in training set
Top 0.9%
1.1%
20
eLife
● 5828 papers in training set
Top 59%
1.1%
21
Frontiers in Molecular Biosciences
○ 102 papers in training set
Top 2%
1.1%
22
Journal of Chemical Theory and Computation
● 140 papers in training set
Top 1.0%
1.0%
23
The Journal of Chemical Physics
● 56 papers in training set
Top 0.4%
1.0%
24
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.0%
25
The Journal of Physical Chemistry B
● 167 papers in training set
Top 2%
1.0%
26
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 2%
1.0%
27
Neuroinformatics
○ 46 papers in training set
Top 0.7%
0.9%
28
PeerJ
◐ 308 papers in training set
Top 11%
0.8%
29
The European Physical Journal E
○ 14 papers in training set
Top 0.2%
0.8%
30
Biotechnology and Bioengineering
○ 53 papers in training set
Top 0.9%
0.8%

"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.