scholarly journals Machine learning guided batched design of a bacterial Ribosome Binding Site

2022 ◽  
Author(s):  
Mengyan Zhang ◽  
Maciej B Holowko ◽  
Huw Hayman Zumpe ◽  
Cheng Soon Ong

Optimisation of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved through engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, design of specific genetic parts can still be challenging due to lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to show how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of cycle and the Upper Confidence Bound multi-armed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we experimentally validated RBSs with high translation initiation rates equalling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way towards more complicated genetic devices.

1998 ◽  
Vol 180 (15) ◽  
pp. 3940-3945 ◽  
Author(s):  
Sharlene R. Matten ◽  
Thomas D. Schneider ◽  
Steven Ringquist ◽  
William S. A. Brusilow

ABSTRACT The uncB gene codes for the a subunit of the Fo proton channel sector of the Escherichia coli F1 Fo ATPase. Control of expression of uncB appears to be exerted at some step after translational initiation. Sequence analysis by the perceptron matrices (G. D. Stormo, T. D. Schneider, L. Gold, and A. Ehrenfeucht, Nucleic Acids Res. 10:2997–3011, 1982) identified a potential ribosome binding site within the uncB reading frame preceding a five-codon reading frame which is shifted one base relative to theuncB reading frame. Elimination of this binding site by mutagenesis resulted in a four- to fivefold increase in expression of an uncB′-′lacZ fusion gene containing most ofuncB. Primer extension inhibition (toeprint) analysis to measure ribosome binding demonstrated that ribosomes could form an initiation complex at this alternative start site. Two fusions oflacZ to the alternative reading frame demonstrated that this site is recognized by ribosomes in vivo. The results suggest that expression of uncB is reduced by translational frameshifting and/or a translational false start at this site within the uncB reading frame.


2020 ◽  
Vol 237 (12) ◽  
pp. 1430-1437
Author(s):  
Achim Langenbucher ◽  
Nóra Szentmáry ◽  
Jascha Wendelstein ◽  
Peter Hoffmann

Abstract Background and Purpose In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measures where classical methods are too complex or fail. The purpose of this paper is to model the measured postoperative position of an intraocular lens implant after cataract surgery, based on preoperatively assessed biometric effect sizes using techniques of machine learning. Patients and Methods In this study, we enrolled 249 eyes of patients who underwent elective cataract surgery at Augenklinik Castrop-Rauxel. Eyes were measured preoperatively with the IOLMaster 700 (Carl Zeiss Meditec), as well as preoperatively and postoperatively with the Casia 2 OCT (Tomey). Based on preoperative effect sizes axial length, corneal thickness, internal anterior chamber depth, thickness of the crystalline lens, mean corneal radius and corneal diameter a selection of 17 machine learning algorithms were tested for prediction performance for calculation of internal anterior chamber depth (AQD_post) and axial position of equatorial plane of the lens in the pseudophakic eye (LEQ_post). Results The 17 machine learning algorithms (out of 4 families) varied in root mean squared/mean absolute prediction error between 0.187/0.139 mm and 0.255/0.204 mm (AQD_post) and 0.183/0.135 mm and 0.253/0.206 mm (LEQ_post), using 5-fold cross validation techniques. The Gaussian Process Regression Model using an exponential kernel showed the best performance in terms of root mean squared error for prediction of AQDpost and LEQpost. If the entire dataset is used (without splitting for training and validation data), comparison of a simple multivariate linear regression model vs. the algorithm with the best performance showed a root mean squared prediction error for AQD_post/LEQ_post with 0.188/0.187 mm vs. the best performance Gaussian Process Regression Model with 0.166/0.159 mm. Conclusion In this paper we wanted to show the principles of supervised machine learning applied to prediction of the measured physical postoperative axial position of the intraocular lenses. Based on our limited data pool and the algorithms used in our setting, the benefit of machine learning algorithms seems to be limited compared to a standard multivariate regression model.


1998 ◽  
Vol 44 (12) ◽  
pp. 1186-1192
Author(s):  
Guy Daxhelet ◽  
Philippe Gilot ◽  
Etienne Nyssen ◽  
Philippe Hoet

pGR71, a composite of plasmids pUB110 and pBR322, replicates in Escherichia coli and in Bacillus subtilis. It carries the chloramphenicol resistance gene (cat) from Tn9, which is not transcribed in either host by lack of a promoter. The cat gene is preceded by a Shine-Dalgarno sequence functional in E. coli but not in B. subtilis. Deleted pGR71 plasmids were obtained in B. subtilis when cloning foreign viral DNA upstream of this cat sequence, as well as by BAL31 exonuclease deletions extending upstream from the cat into the pUB110 moiety. These mutant plasmids expressed chloramphenicol acetyltransferase (CAT), conferring on B. subtilis resistance to high chloramphenicol concentrations. CAT expression peaked at the early postexponential phase of B. subtilis growth. The transcription initiation site of cat, determined by primer extension, was located downstream of a putative promoter sequence within the pUB110 moiety. N-terminal amino acid sequencing showed that native CAT was produced by these mutant plasmids. The cat ribosome-binding site, functional in E. coli, was repositioned within the pUB110 moiety and had consequently an extended homology with B. subtilis 16S rRNA, explaining the production of native enzyme.Key words: chloramphenicol acetyltransferase, Bacillus subtilis, postexponential gene expression, plasmid pUB110, ribosome-binding site, transcriptional promoter.


2021 ◽  
Author(s):  
Jack Woollam ◽  
Jannes Münchmeyer ◽  
Carlo Giunchi ◽  
Dario Jozinovic ◽  
Tobias Diehl ◽  
...  

<p>Machine learning methods have seen widespread adoption within the seismological community in recent years due to their ability to effectively process large amounts of data, while equalling or surpassing the performance of human analysts or classic algorithms. In the wider machine learning world, for example in imaging applications, the open availability of extensive high-quality datasets for training, validation, and the benchmarking of competing algorithms is seen as a vital ingredient to the rapid progress observed throughout the last decade. Within seismology, vast catalogues of labelled data are readily available, but collecting the waveform data for millions of records and assessing the quality of training examples is a time-consuming, tedious process. The natural variability in source processes and seismic wave propagation also presents a critical problem during training. The performance of models trained on different regions, distance and magnitude ranges are not easily comparable. The inability to easily compare and contrast state-of-the-art machine learning-based detection techniques on varying seismic data sets is currently a barrier to further progress within this emerging field. We present SeisBench, an extensible open-source framework for training, benchmarking, and applying machine learning algorithms. SeisBench provides access to various benchmark data sets and models from literature, along with pre-trained model weights, through a unified API. Built to be extensible, and modular, SeisBench allows for the simple addition of new models and data sets, which can be easily interchanged with existing pre-trained models and benchmark data. Standardising the access of varying quality data, and metadata simplifies comparison workflows, enabling the development of more robust machine learning algorithms. We initially focus on phase detection, identification and picking, but the framework is designed to be extended for other purposes, for example direct estimation of event parameters. Users will be able to contribute their own benchmarks and (trained) models. In the future, it will thus be much easier to compare both the performance of new algorithms against published machine learning models/architectures and to check the performance of established algorithms against new data sets. We hope that the ease of validation and inter-model comparison enabled by SeisBench will serve as a catalyst for the development of the next generation of machine learning techniques within the seismological community. The SeisBench source code will be published with an open license and explicitly encourages community involvement.</p>


Toxicon ◽  
2020 ◽  
Vol 177 ◽  
pp. S45
Author(s):  
Xiao-Ping Li ◽  
Nilgun E. Tumer ◽  
Jennifer Nielsen Kahn

2020 ◽  
Vol 52 (9) ◽  
pp. 1602-1613
Author(s):  
Jinho Yang ◽  
Hyo Eun Moon ◽  
Hyung Woo Park ◽  
Andrea McDowell ◽  
Tae-Seop Shin ◽  
...  

Abstract The human microbiome has been recently associated with human health and disease. Brain tumors (BTs) are a particularly difficult condition to directly link to the microbiome, as microorganisms cannot generally cross the blood–brain barrier (BBB). However, some nanosized extracellular vesicles (EVs) released from microorganisms can cross the BBB and enter the brain. Therefore, we conducted metagenomic analysis of microbial EVs in both serum (152 BT patients and 198 healthy controls (HC)) and brain tissue (5 BT patients and 5 HC) samples based on the V3–V4 regions of 16S rDNA. We then developed diagnostic models through logistic regression and machine learning algorithms using serum EV metagenomic data to assess the ability of various dietary supplements to reduce BT risk in vivo. Models incorporating the stepwise method and the linear discriminant analysis effect size (LEfSe) method yielded 12 and 29 significant genera as potential biomarkers, respectively. Models using the selected biomarkers yielded areas under the curves (AUCs) >0.93, and the model using machine learning resulted in an AUC of 0.99. In addition, Dialister and [Eubacterium] rectale were significantly lower in both blood and tissue samples of BT patients than in those of HCs. In vivo tests showed that BT risk was decreased through the addition of sorghum, brown rice oil, and garlic but conversely increased by the addition of bellflower and pear. In conclusion, serum EV metagenomics shows promise as a rich data source for highly accurate detection of BT risk, and several foods have potential for mitigating BT risk.


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