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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.



BibTex format

author = {Kotidis, P and Jedrzejewski, P and Sou, SN and Sellick, C and Polizzi, K and Del, Val IJ and Kontoravdi, C},
doi = {10.1002/bit.26960},
journal = {Biotechnology and Bioengineering},
pages = {1612--1626},
title = {Model-based optimization of antibody galactosylation in CHO cell culture},
url = {},
volume = {116},
year = {2019}

RIS format (EndNote, RefMan)

AB - Exerting control over the glycan moieties of antibody therapeutics is highly desirable from a product safety and batch-to-batch consistency perspective. Strategies to improve antibody productivity may compromise quality, while interventions for improving glycoform distribution can adversely affect cell growth and productivity. Process design therefore needs to consider the trade-off between preserving cellular health and productivity while enhancing antibody quality. In this work, we present a modeling platform that quantifies the impact of glycosylation precursor feeding - specifically that of galactose and uridine - on cellular growth, metabolism as well as antibody productivity and glycoform distribution. The platform has been parameterized using an initial training data set yielding an accuracy of ±5% with respect to glycoform distribution. It was then used to design an optimized feeding strategy that enhances the final concentration of galactosylated antibody in the supernatant by over 90% compared with the control without compromising the integral of viable cell density or final antibody titer. This work supports the implementation of Quality by Design towards higher-performing bioprocesses.
AU - Kotidis,P
AU - Jedrzejewski,P
AU - Sou,SN
AU - Sellick,C
AU - Polizzi,K
AU - Del,Val IJ
AU - Kontoravdi,C
DO - 10.1002/bit.26960
EP - 1626
PY - 2019///
SN - 0006-3592
SP - 1612
TI - Model-based optimization of antibody galactosylation in CHO cell culture
T2 - Biotechnology and Bioengineering
UR -
UR -
UR -
UR -
VL - 116
ER -