Back

Mathematical modeling using box-behnken response surface design for optimal DNA yield with variable parameters of time, temperature, and proteinase K concentration

Afzal, F.; Zia, S.; Saif, R.

2024-07-21 molecular biology
10.1101/2024.07.21.604461 bioRxiv
Show abstract

The current research aims to explore an enzyme-assisted inorganic DNA extraction protocol to obtain maximum yield from whole blood. We considered various operating conditions such as incubation temperature and time along with proteinase K enzyme concentration. To model and simulate the extraction process, Box-Behnken Design (BBD) was employed with three factors and its five levels, coupled with the desired functional response surface methodology (RSM). RSM entails fitting a mathematical model for experimental data to optimize a response variable with Box-Behnken Design. The best simulation settings yielded the maximum DNA at an incubation temperature of 56{degrees}C for 10 hours and proteinase K enzyme concentration of 10{micro}L. Utilizing these parameters, a yield of 300{micro}L was obtained. Furthermore, other DNA extraction parameters may also be added for a more robust design for obtaining the maximum DNA yield.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 3%
36.5%
Similar papers in this journal
2
Journal of Microbiological Methods
○ 13 papers in training set
Top 0.1%
7.1%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 27%
3.7%
Similar papers in this journal
4
Biology Methods and Protocols
◐ 61 papers in training set
Top 0.2%
3.4%
Similar papers in this journal
50% of probability mass above
5
Talanta
○ 14 papers in training set
Top 0.1%
3.3%
Similar papers in this journal
6
Molecular Biology Reports
○ 21 papers in training set
Top 0.2%
2.9%
7
Diagnostics
○ 50 papers in training set
Top 0.7%
2.6%
8
International Journal of Biological Macromolecules
○ 76 papers in training set
Top 0.7%
2.0%
9
BMC Research Notes
○ 33 papers in training set
Top 0.3%
1.8%
10
International Journal of Molecular Sciences
○ 494 papers in training set
Top 9%
1.5%
11
BMC Genomics
○ 406 papers in training set
Top 5%
1.2%
12
Frontiers in Microbiology
○ 427 papers in training set
Top 6%
1.2%
13
Methods
○ 34 papers in training set
Top 0.3%
1.2%
14
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 1%
1.2%
15
Medical Research Archives
● 11 papers in training set
Top 0.2%
1.2%
16
Forensic Science International: Genetics
○ 26 papers in training set
Top 0.1%
1.2%
17
F1000Research
○ 88 papers in training set
Top 3%
0.9%
18
Heliyon
○ 152 papers in training set
Top 7%
0.9%
19
ACS Omega
● 105 papers in training set
Top 3%
0.9%
20
MethodsX
○ 16 papers in training set
Top 0.2%
0.9%
21
Pathogens
○ 56 papers in training set
Top 2%
0.6%
22
Genes
○ 144 papers in training set
Top 4%
0.6%
23
Analytical and Bioanalytical Chemistry
○ 18 papers in training set
Top 0.3%
0.6%
24
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.6%
25
PeerJ
◐ 308 papers in training set
Top 13%
0.5%
26
Analytica Chimica Acta
○ 17 papers in training set
Top 0.5%
0.5%
27
Journal of Virological Methods
○ 37 papers in training set
Top 0.6%
0.5%
28
Biomolecules
○ 100 papers in training set
Top 4%
0.5%
29
BioTechniques
○ 25 papers in training set
Top 0.6%
0.5%

"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.