[HTML][HTML] Machine learning for biochemical engineering: A review
The field of machine learning is comprised of techniques, which have proven powerful
approaches to knowledge discovery and construction of 'digital twins' in the highly …
approaches to knowledge discovery and construction of 'digital twins' in the highly …
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 …
Bioprocess control: current progress and future perspectives
Typical bioprocess comprises of different unit operations wherein a near optimal
environment is required for cells to grow, divide, and synthesize the desired product …
environment is required for cells to grow, divide, and synthesize the desired product …
Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization
Abstract Model‐based online optimization has not been widely applied to bioprocesses due
to the challenges of modeling complex biological behaviors, low‐quality industrial …
to the challenges of modeling complex biological behaviors, low‐quality industrial …
Enhancement of isoprene production in engineered Synechococcus elongatus UTEX 2973 by metabolic pathway inhibition and machine learning-based optimization …
An engineered Synechococcus elongatus UTEX 2973-IspS. IDI is used to enhance isoprene
production through geranyl diphosphate synthase (CrtE) inhibition and process parameters …
production through geranyl diphosphate synthase (CrtE) inhibition and process parameters …
Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design
Identifying optimal photobioreactor configurations and process operating conditions is
critical to industrialize microalgae‐derived biorenewables. Traditionally, this was addressed …
critical to industrialize microalgae‐derived biorenewables. Traditionally, this was addressed …
[HTML][HTML] Harnessing the potential of artificial neural networks for predicting protein glycosylation
P Kotidis, C Kontoravdi - Metabolic engineering communications, 2020 - Elsevier
Kinetic models offer incomparable insight on cellular mechanisms controlling protein
glycosylation. However, their ability to reproduce site-specific glycoform distributions …
glycosylation. However, their ability to reproduce site-specific glycoform distributions …
A transfer learning approach for predictive modeling of bioprocesses using small data
Predictive modeling of new biochemical systems with small data is a great challenge. To fill
this gap, transfer learning, a subdomain of machine learning that serves to transfer …
this gap, transfer learning, a subdomain of machine learning that serves to transfer …
When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development
Abstract Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess engineering. While …
engineering but has not yet played out its full potential in bioprocess engineering. While …
Optimization of non-catalytic transesterification of microalgae oil to biodiesel under supercritical methanol condition
The present study aims to maximize the conversion of microalgae oil to fatty acid methyl
ester (FAME) using supercritical methanol (SCM) transesterification by sequential hybrid …
ester (FAME) using supercritical methanol (SCM) transesterification by sequential hybrid …