Evolutionary engineering is an inverse metabolic engineering strategy which is based on increasing genetic diversity and screening large populations for desired phenotypes. This strategy is highly advantageous in certain situations over rational metabolic engineering approaches, since there is little or no requirement of detailed genetic background information for the trait of interest. Here, we describe the experimental methodology for selecting stress-resistant yeast strains via evolutionary engineering approach by either serial batch or chemostat cultivations.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/978-1-4939-0563-8_10 | DOI Listing |
Astrobiology
January 2025
Department of Aerospace, Physics and Space Sciences, Florida Institute of Technology, Melbourne, Florida, USA.
Waste heat production represents an inevitable consequence of energy conversion as per the laws of thermodynamics. Based on this fact, by using simple theoretical models, we analyze constraints on the habitability of Earth-like terrestrial planets hosting putative technological species and technospheres characterized by persistent exponential growth of energy consumption and waste heat generation. In particular, we quantify the deleterious effects of rising surface temperature on biospheric processes and the eventual loss of liquid water.
View Article and Find Full Text PDFScience
January 2025
Electrical and Computer Engineering Department, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, USA.
Genomes contain mosaics of discordant evolutionary histories, challenging the accurate inference of the tree of life. While genome-wide data are routinely used for discordance-aware phylogenomic analyses, due to modeling and scalability limitations, the current practice leaves out large chunks of genomes. As more high-quality genomes become available, we urgently need discordance-aware methods to infer the tree directly from a multiple genome alignment.
View Article and Find Full Text PDFBull Math Biol
January 2025
Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark.
Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more complex biological phenomena, such as variations in lineage-specific evolutionary rates over time (heterotachy), other approaches such as the GTR with rate variation (GTR ) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for Bayesian analysis.
View Article and Find Full Text PDFZool Res
January 2025
BGI Research, Hangzhou, Zhejiang 310030, China.
The amniote pallium, a vital component of the forebrain, exhibits considerable evolutionary divergence across species and mediates diverse functions, including sensory processing, memory formation, and learning. However, the relationships among pallial subregions in different species remain poorly characterized, particularly regarding the identification of homologous neurons and their transcriptional signatures. In this study, we utilized single-nucleus RNA sequencing to examine over 130 000 nuclei from the macaque ( ) neocortex, complemented by datasets from humans ( ), mice ( ), zebra finches ( ), turtles ( ), and lizards ( s), enabling comprehensive cross-species comparison.
View Article and Find Full Text PDFSci Technol Adv Mater
December 2024
JST-CREST, Saitama, Japan.
In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!