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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 Demis, P and Goey, C and Correa, E and McIntosh, C and Trepekli, S and Shah, N and Klymenko, O and Kontoravdi, K},
doi = {10.1016/j.compchemeng.2019.01.022},
journal = {Computers and Chemical Engineering},
pages = {558--568},
title = {Constrained global sensitivity analysis for bioprocess design space identification},
url = {},
volume = {125},
year = {2019}

RIS format (EndNote, RefMan)

AB - The manufacture of protein-based therapeutics presents unique challenges due to limited control over the biotic phase. This typically gives rise to a wide range of protein structures of varying safety and in vivo efficacy. Herein we propose a computational methodology, enabled by the application of constrained Global Sensitivity Analysis, for efficiently exploring the operatingrange of process inputs in silico and identifying a design space that meets output constraints. The methodology was applied to an antibody-producing Chinese hamster ovary (CHO) cell culture system: we explored >8000 feeding strategies to identify a subset of manufacturing conditions that meet constraints on antibody titre and glycan distribution as an attribute of product quality. Our computational findings were then verified experimentally, confirming the applicability of this approach to a challenging production system. We envisage that this methodology can significantly expedite bioprocess development and increase operational flexibility.
AU - Kotidis,P
AU - Demis,P
AU - Goey,C
AU - Correa,E
AU - McIntosh,C
AU - Trepekli,S
AU - Shah,N
AU - Klymenko,O
AU - Kontoravdi,K
DO - 10.1016/j.compchemeng.2019.01.022
EP - 568
PY - 2019///
SN - 1873-4375
SP - 558
TI - Constrained global sensitivity analysis for bioprocess design space identification
T2 - Computers and Chemical Engineering
UR -
UR -
VL - 125
ER -