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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 = {Sechkar, K and Tuza, ZA and Stan, G-B},
journal = {Synthetic Biology},
title = {A linear programming-based strategy to save pipette tips in automated DNA assembly},
url = {},
year = {2021}

RIS format (EndNote, RefMan)

AB - Laboratory automation and mathematical optimization are key to improving the efficiency of synthetic biology research.While there are algorithms optimizing the construct designs and synthesis strategies for DNA assembly, the optimizationof how DNA assembly reaction mixes are prepared remains largely unexplored. Here, we focus on reducing the pipettetip consumption of a liquid-handling robot as it delivers DNA parts across a multi-well plate where several constructsare being assembled in parallel. We propose a linear programming formulation of this problem based on the capacitatedvehicle routing problem, as well as an algorithm which applies a linear programming solver to our formulation, henceproviding a strategy to prepare a given set of DNA assembly mixes using fewer pipette tips. The algorithm performedwell in randomly generated and real-life scenarios concerning several modular DNA assembly standards, proving capableof reducing the pipette tip consumption by up to 59% in large-scale cases. Combining automatic process optimizationand robotic liquid-handling, our strategy promises to greatly improve the efficiency of DNA assembly, either used aloneor combined with other algorithmic DNA assembly optimization methods.
AU - Sechkar,K
AU - Tuza,ZA
AU - Stan,G-B
PY - 2021///
SN - 2397-7000
TI - A linear programming-based strategy to save pipette tips in automated DNA assembly
T2 - Synthetic Biology
UR -
ER -