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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 = {Kuntz, Nussio J and Thomas, P and Stan, G and Barahona, M},
doi = {10.1137/19M1268847},
journal = {SIAM Journal on Optimization},
pages = {604--625},
title = {Approximations of countably-infinite linear programs over bounded measure spaces},
url = {},
volume = {31},
year = {2021}

RIS format (EndNote, RefMan)

AB - We study a class of countably-infinite-dimensional linear programs (CILPs)whose feasible sets are bounded subsets of appropriately defined spaces ofmeasures. The optimal value, optimal points, and minimal points of these CILPscan be approximated by solving finite-dimensional linear programs. We show howto construct finite-dimensional programs that lead to approximations witheasy-to-evaluate error bounds, and we prove that the errors converge to zero asthe size of the finite-dimensional programs approaches that of the originalproblem. We discuss the use of our methods in the computation of the stationarydistributions, occupation measures, and exit distributions of Markov~chains.
AU - Kuntz,Nussio J
AU - Thomas,P
AU - Stan,G
AU - Barahona,M
DO - 10.1137/19M1268847
EP - 625
PY - 2021///
SN - 1052-6234
SP - 604
TI - Approximations of countably-infinite linear programs over bounded measure spaces
T2 - SIAM Journal on Optimization
UR -
UR -
VL - 31
ER -