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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 = {Scholes, N and Schnoerr, D and Isalan, M and Stumpf, M},
journal = {Cell Systems},
title = {A comprehensive network atlas reveals that Turing patterns are common but not robust},
url = {},

RIS format (EndNote, RefMan)

AB - Turing patterns (TPs) underlie many fundamental de-velopmental processes, but they operate over narrowparameter ranges, raising the conundrum of how evo-lution can ever discover them. Here we explore TPdesign space to address this question and to distill de-sign rules. We exhaustively analyze 2- and 3-node bio-logical candidate Turing systems, amounting to 7,625networks and more than3×1011analysed scenar-ios. We find that network structure alone neither im-plies nor guarantees emergent TPs. A large fraction(>61%) of network design space can produce TPs,but these are sensitive to even subtle changes in pa-rameters, network structure and regulatory mecha-nisms. This implies that TP networks are more com-mon than previously thought, and evolution mightregularly encounter prototypic solutions. We deducecompositional rules for TP systems that are almostnecessary and sufficient (96%of TP networks containthem, and92%of networks implementing them pro-duce TPs). This comprehensive network atlas providesthe blueprints for identifying natural TPs, and for en-gineering synthetic systems.
AU - Scholes,N
AU - Schnoerr,D
AU - Isalan,M
AU - Stumpf,M
SN - 2405-4712
TI - A comprehensive network atlas reveals that Turing patterns are common but not robust
T2 - Cell Systems
UR -
ER -