Research

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Synthetic Biology underpins advances in the bioeconomy

Biological systems - including the simplest cells - exhibit a broad range of functions to thrive in their environment. Research in the Imperial College Centre for Synthetic Biology is focused on the possibility of engineering the underlying biochemical processes to solve many of the challenges facing society, from healthcare to sustainable energy. In particular, we model, analyse, design and build biological and biochemical systems in living cells and/or in cell extracts, both exploring and enhancing the engineering potential of biology. 

As part of our research we develop novel methods to accelerate the celebrated Design-Build-Test-Learn synthetic biology cycle. As such research in the Centre for Synthetic Biology highly multi- and interdisciplinary covering computational modelling and machine learning approaches; automated platform development and genetic circuit engineering ; multi-cellular and multi-organismal interactions, including gene drive and genome engineering; metabolic engineering; in vitro/cell-free synthetic biology; engineered phages and directed evolution; and biomimetics, biomaterials and biological engineering.

Publications

Citation

BibTex format

@article{Kotidis:2020:10.1016/j.mec.2020.e00131,
author = {Kotidis, P and Kontoravdi, K},
doi = {10.1016/j.mec.2020.e00131},
journal = {Metabolic Engineering Communications},
title = {Harnessing the potential of artificial neural networks for predicting protein glycosylation},
url = {http://dx.doi.org/10.1016/j.mec.2020.e00131},
volume = {10},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.
AU  - Kotidis,P
AU  - Kontoravdi,K
DO  - 10.1016/j.mec.2020.e00131
PY  - 2020///
SN  - 2214-0301
TI  - Harnessing the potential of artificial neural networks for predicting protein glycosylation
T2  - Metabolic Engineering Communications
UR  - http://dx.doi.org/10.1016/j.mec.2020.e00131
UR  - http://hdl.handle.net/10044/1/80115
VL  - 10
ER  -
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