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How to model DNA replication in stochastic models of synthetic genetic circuits (and why)

Clamons, S.; Murray, R. M.

2021-09-27 synthetic biology
10.1101/2021.09.26.461880 bioRxiv
Show abstract

Biocircuit modeling sometimes requires explicit tracking of a self-replicating DNA species. The most obvious, straightforward way to model a replicating DNA is structurally unstable and leads to pathological model behavior. We describe a simple, stable replication mechanism with good model behavior and show how to derive it from a mechanistic model of ColE1 replication.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
ACS Synthetic Biology
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Top 0.2%
18.6%
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2
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Top 2%
15.1%
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3
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6
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Top 32%
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50% of probability mass above
7
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○ 3612 papers in training set
Top 25%
4.1%
8
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● 235 papers in training set
Top 1%
3.5%
9
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○ 162 papers in training set
Top 1%
2.4%
10
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○ 46 papers in training set
Top 0.2%
2.4%
11
GENETICS
◐ 483 papers in training set
Top 2%
2.4%
12
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○ 13 papers in training set
Top 0.1%
2.1%
13
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○ 457 papers in training set
Top 3%
2.1%
14
iScience
○ 1154 papers in training set
Top 13%
2.1%
15
Royal Society Open Science
● 214 papers in training set
Top 3%
2.0%
16
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○ 31 papers in training set
Top 0.2%
1.9%
17
PeerJ
◐ 308 papers in training set
Top 5%
1.7%
18
Journal of Mathematical Biology
○ 40 papers in training set
Top 0.3%
1.7%
19
npj Systems Biology and Applications
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Top 1%
1.4%
20
Synthetic Biology
◐ 24 papers in training set
Top 0.2%
1.3%
21
Philosophical Transactions of the Royal Society B
● 51 papers in training set
Top 0.5%
1.1%
22
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 34%
1.1%
23
eLife
● 5828 papers in training set
Top 59%
1.1%
24
Molecular Systems Biology
○ 162 papers in training set
Top 3%
0.8%
25
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 2%
0.8%
26
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
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27
Nucleic Acids Research
◐ 1281 papers in training set
Top 14%
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.