Research

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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.

Publications

Citation

BibTex format

@article{Brittain:2019:1367-2630/ab2484,
author = {Brittain, R and Jones, N and Ouldridge, T},
doi = {1367-2630/ab2484},
journal = {New Journal of Physics},
title = {Biochemical Szilard engines for memory-limited inference},
url = {http://dx.doi.org/10.1088/1367-2630/ab2484},
volume = {21},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - By designing and leveraging an explicit molecular realisation of a measurement-and-feedback-powered Szilard engine, we investigate the extraction of work from complex environments by minimal machines with finite capacity for memory and decision-making. Living systems perform inference to exploit complex structure, or correlations, in their environment, but the physical limits and underlying cost/benefit trade-offs involved in doing so remain unclear. To probe these questions, we consider a minimal model for a structured environment—a correlated sequence of molecules—and explore mechanisms based on extended Szilard engines for extracting the work stored in these non-equilibrium correlations. We consider systems limited to a single bit of memory making binary 'choices' at each step. We demonstrate that increasingly complex environments allow increasingly sophisticated inference strategies to extract more free energy than simpler alternatives, and argue that optimal design of such machines should also consider the free energy reserves required to ensure robustness against fluctuations due to mistakes.
AU  - Brittain,R
AU  - Jones,N
AU  - Ouldridge,T
DO  - 1367-2630/ab2484
PY  - 2019///
SN  - 1367-2630
TI  - Biochemical Szilard engines for memory-limited inference
T2  - New Journal of Physics
UR  - http://dx.doi.org/10.1088/1367-2630/ab2484
UR  - http://hdl.handle.net/10044/1/70596
VL  - 21
ER  -
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