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Multiplexed Cell-Based Diagnostic Devices for Detection of Renal Biomarkers Using Genetic Circuits

Kose, S.; Ahan, R.; Koksaldi, I.; Olgac, M.; Kasapkara, C.; Seker, U. O. S.

2021-11-15 synthetic biology
10.1101/2021.11.14.468540 bioRxiv
Show abstract

The number of synthetic biology based solutions employed in the medical industry is growing every year. The whole cell biosensors being one of them, have been proven valuable tools for developing low-cost, portable, personalized medicine alternatives to conventional techniques. Based on this concept, we targeted one of the major health problems in the world, Chronic Kidney Disease (CKD). To do so, we developed two novel biosensors for the detection of two important renal biomarkers; urea and uric acid. Using advanced gene expression control strategies we improved the operational range and the response profiles of each biosensor to meet clinical specifications. We further engineered these systems to enable multiplexed detection as well as an AND-logic gate operating system. Finally, we tested the applicability of these systems and optimized their working dynamics inside complex medium human blood serum. This study could help the efforts to transition from labor-intensive and expensive laboratory techniques to widely available, portable, low cost diagnostic options.

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

1
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PLOS ONE
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8
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Top 0.1%
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10
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11
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12
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Top 0.4%
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13
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○ 76 papers in training set
Top 0.7%
2.1%
14
Microbiology Spectrum
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Top 7%
1.7%
15
Nature Communications
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1.5%
16
Life
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Top 0.3%
1.5%
17
Nucleic Acids Research
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Top 10%
1.4%
18
International Journal of Molecular Sciences
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19
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Top 0.9%
1.1%
20
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Top 0.3%
1.1%
21
Computational and Structural Biotechnology Journal
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Top 5%
1.0%
22
Microbial Cell Factories
○ 27 papers in training set
Top 0.5%
0.8%
23
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Top 9%
0.6%
24
Metabolic Engineering Communications
○ 22 papers in training set
Top 0.4%
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25
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● 22 papers in training set
Top 1.0%
0.6%
26
eLife
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0.6%
27
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Top 0.4%
0.6%
28
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○ 53 papers in training set
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"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.