Controlling the speed and trajectory of evolution with counterdiabatic driving
| dc.contributor.author | Iram, Shamreen | |
| dc.contributor.author | Dolson, Emily | |
| dc.contributor.author | Chiel, Joshua | |
| dc.contributor.author | Pelesko, Julia | |
| dc.contributor.author | Krishnan, Nikhil | |
| dc.contributor.author | Güngör, Özenç | |
| dc.contributor.author | Kuznets-Speck, Benjamin | |
| dc.contributor.author | Deffner, Sebastian | |
| dc.contributor.author | Ilker, Efe | |
| dc.contributor.author | Scott, Jacob G. | |
| dc.contributor.author | Hinczewski, Michael | |
| dc.date.accessioned | 2020-08-14T16:35:54Z | |
| dc.date.available | 2020-08-14T16:35:54Z | |
| dc.date.issued | 2020-08-24 | |
| dc.description.abstract | The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change, and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modeling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and the distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context – counterdiabatic driving to control the behavior of quantum states for applications like quantum computing and manipulating ultra-cold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (i.e. varying drug concentrations / types, temperature, nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases, to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules. | en_US |
| dc.description.sponsorship | MH would like to thank the U.S. National Science Foundation for support through the CAREER grant (BIO/MCB1651560). JGS would like to thank the NIH Loan Repayment Program for their generous support and the PaulCalabresi Career Development Award for Clinical Oncology (NIH K12CA076917). SD acknowledges support from the U.S. National Science Foundation under Grant No. CHE-1648973. | en_US |
| dc.description.uri | https://www.nature.com/articles/s41567-020-0989-3 | en_US |
| dc.format.extent | 37 pages | en_US |
| dc.genre | journal articles preprints | en_US |
| dc.identifier | doi:10.13016/m2nalt-wz4d | |
| dc.identifier.citation | Shamreen Iram et al., Controlling the speed and trajectory of evolution with counterdiabatic driving, Nature Physics, volume 17, pages 135–142, 24 August, 2020; https://doi.org/10.1038/s41567-020-0989-3 | en_US |
| dc.identifier.uri | https://doi.org/10.1038/s41567-020-0989-3 | |
| dc.identifier.uri | http://hdl.handle.net/11603/19428 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Nature | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Physics Department Collection | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.title | Controlling the speed and trajectory of evolution with counterdiabatic driving | en_US |
| dc.type | Text | en_US |