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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.
@article{Plesa:2021:10.1098/rsif.2020.0985,
author = {Plesa, T and Stan, G-B and Ouldridge, TE and Bae, W},
doi = {10.1098/rsif.2020.0985},
journal = {Journal of the Royal Society Interface},
pages = {1--14},
title = {Quasi-robust control of biochemical reaction networks via stochastic morphing.},
url = {http://dx.doi.org/10.1098/rsif.2020.0985},
volume = {18},
year = {2021}
}
TY - JOUR
AB - One of the main objectives of synthetic biology is the development of molecular controllers that can manipulate the dynamics of a given biochemical network that is at most partially known. When integrated into smaller compartments, such as living or synthetic cells, controllers have to be calibrated to factor in the intrinsic noise. In this context, biochemical controllers put forward in the literature have focused on manipulating the mean (first moment) and reducing the variance (second moment) of the target molecular species. However, many critical biochemical processes are realized via higher-order moments, particularly the number and configuration of the probability distribution modes (maxima). To bridge the gap, we put forward the stochastic morpher controller that can, under suitable timescale separations, morph the probability distribution of the target molecular species into a predefined form. The morphing can be performed at a lower-resolution, allowing one to achieve desired multi-modality/multi-stability, and at a higher-resolution, allowing one to achieve arbitrary probability distributions. Properties of the controller, such as robustness and convergence, are rigorously established, and demonstrated on various examples. Also proposed is a blueprint for an experimental implementation of stochastic morpher.
AU - Plesa,T
AU - Stan,G-B
AU - Ouldridge,TE
AU - Bae,W
DO - 10.1098/rsif.2020.0985
EP - 14
PY - 2021///
SN - 1742-5662
SP - 1
TI - Quasi-robust control of biochemical reaction networks via stochastic morphing.
T2 - Journal of the Royal Society Interface
UR - http://dx.doi.org/10.1098/rsif.2020.0985
UR - https://www.ncbi.nlm.nih.gov/pubmed/33849334
UR - https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0985
UR - http://hdl.handle.net/10044/1/88175
VL - 18
ER -