Miguel Fuentes-Cabrera
Paolo Zuliani
Bowen Li
Jared L. Wilmoth
Aivett Bilbao
Alice C. Dohnalkova- 1Khoury College of Computer Sciences, Northeastern University, Oakland, CA, United States
- 2Dipartimento di Informatica, Sapienza University of Rome, Rome, Italy
- 3Research Software Engineering Group, Catalyst Building, Newcastle University, Newcastle upon Tyne, United Kingdom
- 4Department of Environmental Science and Technology, University of Maryland, College Park, MD, United States
- 5Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
Editorial on the Research Topic
Combining machine learning, computational modeling, and high throughput experimentation to accelerate discovery in systems microbiology
In recent years, Machine Learning (ML) has impacted various fields, not just business and social sciences, but also basic sciences. Those of us who became scientists before ML became so popular, have witnessed with amazement how its impact has grown and how useful the incorporation of ML techniques has been for our own research. Despite this, we have noticed that the impact of ML on microbiology has lagged. While experiments in microbiology now produce large amounts of data, and modeling techniques build bridges with experiments, the impact of ML has been slower to materialize.
Motivated by all of this, we decided to assemble an editorial team of theorists, experimentalists, and computer scientists to invite papers that combine these three areas in microbiology. The result is the Research Topic you are reading, which has also been made possible by the insights of the Frontiers in Microbiology editorial team. This Research Topic contains seven papers that cover a variety of areas and applications of ML in microbiology.
The manuscript by Bommanapally et al. describes a ML-based image super-resolution (SR) technique for improving the image quality of microscopy images that will improve the synthesis of high-quality microscopy images. Muralidharan et al. assess how training ML taxonomic classifiers with computationally-generated data can significantly skew the assignment of microbial community sequence annotations and provide guidance toward more resilient ML classifiers. Yang et al. propose a novel ML framework to address the challenge of predicting synthetic promoter strength in metabolic engineering and synthetic biology. Applied to the Trc synthetic promoter library, the new approach significantly improved model performance by achieving up to a 61.30% enhancement. The study conducted by Huang et al. evaluates the effectiveness of various machine learning methods and descriptors in predicting psychrophilic enzymes. The results could aid in the design and identification of cold-active enzymes.
Overall, these seven manuscripts showcase the potential of using machine learning in microbial studies, and we hope the scientific community will greatly benefit from their insights.
Author contributions
MF-C: Writing – original draft, Writing – review & editing. PZ: Writing – original draft, Writing – review & editing. BL: Writing – original draft, Writing – review & editing. JW: Writing – original draft, Writing – review & editing. AB: Writing – original draft, Writing – review & editing. AD: Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
MF-C, PZ, BL, JW, and AB would like to send a heartfelt thank you to AD, whose participation in this Research Topic was invaluable and whose friendship we cherish.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: machine learning, microbiology, high throughput experiments, computational simulations, accelerate discovery
Citation: Fuentes-Cabrera M, Zuliani P, Li B, Wilmoth JL, Bilbao A and Dohnalkova AC (2024) Editorial: Combining machine learning, computational modeling, and high throughput experimentation to accelerate discovery in systems microbiology. Front. Microbiol. 15:1451243. doi: 10.3389/fmicb.2024.1451243
Received: 18 June 2024; Accepted: 24 June 2024;
Published: 17 July 2024.
Edited and reviewed by: George Tsiamis, University of Patras, Greece
Copyright © 2024 Fuentes-Cabrera, Zuliani, Li, Wilmoth, Bilbao and Dohnalkova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Miguel Fuentes-Cabrera, m.fuentes-cabrera@northeastern.edu