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Chi.Bio: An open-source automated experimental platform for biological science research

Steel, H.; Habgood, R.; Kelly, C.; Papachristodoulou, A.

2019-10-07 bioengineering
10.1101/796516 bioRxiv
Show abstract

The precise characterisation and manipulation of in vivo biological systems is critical to their study.1 However, in many experimental frameworks this is made challenging by non-static environments during cell growth,2, 3 as well as variability introduced by manual sampling and measurement protocols.4 To address these challenges we present Chi.Bio, a parallelised open-source platform that offers a new experimental paradigm in which all measurement and control actions can be applied to a bulk culture in situ. In addition to continuous-culturing capabilities (turbidostat functionality, heating, stirring) it incorporates tunable light outputs of varying wavelengths and spectrometry. We demonstrate its application to studies of cell growth and biofilm formation, automated in silico control of optogenetic systems, and readout of multiple orthogonal fluorescent proteins. By combining capabilities from many laboratory tools into a single low-cost platform, Chi.Bio facilitates novel studies in synthetic, systems, and evolutionary biology, and broadens access to cutting-edge research capabilities.

Matching journals

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

1
Nature Communications
○ 5641 papers in training set
Top 12%
15.0%
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2
Lab on a Chip
● 96 papers in training set
Top 0.1%
13.0%
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3
ACS Synthetic Biology
● 287 papers in training set
Top 0.5%
6.7%
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4
Analytical Chemistry
● 218 papers in training set
Top 0.6%
6.2%
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5
ACS Sensors
● 49 papers in training set
Top 0.1%
5.4%
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6
Nature Methods
○ 385 papers in training set
Top 2%
4.3%
50% of probability mass above
7
Nature Biotechnology
○ 172 papers in training set
Top 1%
3.1%
8
Nature Chemistry
○ 42 papers in training set
Top 0.3%
2.7%
9
Science Advances
● 1243 papers in training set
Top 14%
2.4%
10
Angewandte Chemie International Edition
○ 93 papers in training set
Top 0.7%
2.4%
11
Nano Letters
● 71 papers in training set
Top 0.6%
2.1%
12
Communications Biology
○ 993 papers in training set
Top 12%
2.0%
13
Nature Nanotechnology
○ 32 papers in training set
Top 0.3%
1.9%
14
Advanced Science
○ 286 papers in training set
Top 5%
1.7%
15
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 29%
1.7%
16
Journal of the American Chemical Society
● 217 papers in training set
Top 2%
1.4%
17
ACS Central Science
● 71 papers in training set
Top 0.9%
1.4%
18
Light: Science & Applications
○ 16 papers in training set
Top 0.2%
1.1%
19
ACS Chemical Biology
● 167 papers in training set
Top 2%
1.1%
20
ACS Nano
● 113 papers in training set
Top 2%
1.1%
21
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.1%
22
iScience
○ 1154 papers in training set
Top 27%
1.1%
23
Nature Chemical Biology
○ 119 papers in training set
Top 2%
1.1%
24
ACS Applied Materials & Interfaces
● 39 papers in training set
Top 0.8%
1.1%
25
The Analyst
● 16 papers in training set
Top 0.3%
1.0%
26
Nature Protocols
○ 33 papers in training set
Top 0.4%
1.0%
27
ACS Photonics
● 13 papers in training set
Top 0.2%
1.0%
28
Nature Photonics
○ 10 papers in training set
Top 0.1%
1.0%
29
Advanced Materials
○ 56 papers in training set
Top 1%
0.8%
30
Advanced Intelligent Systems
○ 11 papers in training set
Top 0.3%
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.