Metabolic engineering: methodologies and applications
Metabolic engineering aims to improve the production of economically valuable molecules
through the genetic manipulation of microbial metabolism. While the discipline is a little over …
through the genetic manipulation of microbial metabolism. While the discipline is a little over …
Machine learning in bioprocess development: from promise to practice
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess
development provides large amounts of heterogeneous experimental data, containing …
development provides large amounts of heterogeneous experimental data, containing …
Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution
P Ghadermazi, SHJ Chan - Bioinformatics, 2024 - academic.oup.com
Motivation Microbes are essential part of all ecosystems, influencing material flow and
shaping their surroundings. Metabolic modeling has been a useful tool and provided …
shaping their surroundings. Metabolic modeling has been a useful tool and provided …
Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges
MK Goshisht - ACS omega, 2024 - ACS Publications
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
Deep learning concepts and applications for synthetic biology
Synthetic biology has a natural synergy with deep learning. It can be used to generate large
data sets to train models, for example by using DNA synthesis, and deep learning models …
data sets to train models, for example by using DNA synthesis, and deep learning models …