Designing a bioremediator: mechanistic models guide cellular and molecular specialization
- Published
- Accepted
- Subject Areas
- Biophysics, Food Science and Technology, Microbiology, Environmental Contamination and Remediation, Population Biology
- Keywords
- rational design, bioremediation, mechanistic modeling, quantum mechanical modeling, community assembly, cellular specialization, molecular specialization, community ecology, artificial selection
- Copyright
- © 2019 Zaccaria et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Designing a bioremediator: mechanistic models guide cellular and molecular specialization. PeerJ Preprints 7:e27838v1 https://doi.org/10.7287/peerj.preprints.27838v1
Abstract
Rational, mechanistic design can substantially improve the performance of bioremediators for applications including waste treatment and food safety. We highlight how such improvement can be informed at the cellular level by theoretical observations especially in the context of phenotype plasticity, cell signaling, and community assembly. At the molecular level, we suggest enzyme design using techniques such as Small Angle Neutron Scattering and Density Functional Theory. To provide an example of how these techniques could be synergistically combined, we present the case-study of the interaction of the enzyme laccase with the food pollutant aflatoxin B1. In designing bioremediators, we encourage interdisciplinary, mechanistic research to transition from an observation-oriented approach to a principle-based one.
Author Comment
We review some of the modeling approaches that can guide and facilitate efficient design of bioremediators. This is a preprint submission to PeerJ Preprints.