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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{Beardall:2022:10.1089/genbio.2022.0017,
author = {Beardall, WAV and Stan, G-B and Dunlop, MJ},
doi = {10.1089/genbio.2022.0017},
journal = {GEN Biotechnology},
pages = {360--371},
title = {Deep Learning Concepts and Applications for Synthetic Biology.},
url = {http://dx.doi.org/10.1089/genbio.2022.0017},
volume = {1},
year = {2022}
}
TY - JOUR
AB - Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.
AU - Beardall,WAV
AU - Stan,G-B
AU - Dunlop,MJ
DO - 10.1089/genbio.2022.0017
EP - 371
PY - 2022///
SN - 2768-1556
SP - 360
TI - Deep Learning Concepts and Applications for Synthetic Biology.
T2 - GEN Biotechnology
UR - http://dx.doi.org/10.1089/genbio.2022.0017
UR - https://www.ncbi.nlm.nih.gov/pubmed/36061221
UR - http://hdl.handle.net/10044/1/100047
VL - 1
ER -