Back

Modeling metabolic variation with single-cell expression data

Zhang, Y.; Kim, M. S.; Nguyen, E.; Taylor, D. M.

2020-01-29 systems biology
10.1101/2020.01.28.923680 bioRxiv
Show abstract

Cellular metabolism encompasses the biochemical reactions and transportation of various metabolites in cells and their surroundings, which are integrated at all levels of cellular functions. We developed a method to systematically simulate cellular metabolism using single-cell RNA-seq (scRNA-seq) data through constraint-based context specific metabolic modeling. We simulated the NAD+ biosynthesis activity in 7 different mouse tissues, and the simulated NAD+ biosynthesis flux levels showed significant linear correlation with experimental measurements in previous research. We also show that the simulated NAD+ biosynthesis fluxes are reproducible using two additional scRNA-seq datasets.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 1%
18.5%
Similar papers in this journal
2
PLOS Computational Biology
● 1863 papers in training set
Top 4%
9.7%
Similar papers in this journal
3
Metabolic Engineering
○ 75 papers in training set
Top 0.1%
9.7%
Similar papers in this journal
4
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.1%
7.9%
Similar papers in this journal
5
iScience
○ 1154 papers in training set
Top 0.6%
7.9%
Similar papers in this journal
50% of probability mass above
6
eLife
● 5828 papers in training set
Top 22%
5.5%
7
Cell Reports Methods
○ 165 papers in training set
Top 0.3%
4.4%
8
Genome Research
● 468 papers in training set
Top 1%
4.3%
9
Nature Communications
○ 5641 papers in training set
Top 32%
4.0%
10
Cell Systems
○ 201 papers in training set
Top 2%
2.4%
11
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.1%
12
Life Science Alliance
● 285 papers in training set
Top 3%
1.9%
13
Genome Biology
○ 637 papers in training set
Top 6%
1.7%
14
Molecular Systems Biology
○ 162 papers in training set
Top 1%
1.7%
15
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.7%
16
Biotechnology and Bioengineering
○ 53 papers in training set
Top 0.5%
1.5%
17
Cell Reports
○ 1498 papers in training set
Top 20%
1.5%
18
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
1.1%
19
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 41%
0.8%
20
Molecular Biology of the Cell
● 311 papers in training set
Top 3%
0.8%
21
Scientific Reports
○ 3612 papers in training set
Top 76%
0.6%
22
Communications Biology
○ 993 papers in training set
Top 34%
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.