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Research

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{Antonakoudis:2020:10.1016/j.csbj.2020.10.011,
author = {Antonakoudis, A and Barbosa, R and Kotidis, P and Kontoravdi, K},
doi = {10.1016/j.csbj.2020.10.011},
journal = {Computational and Structural Biotechnology Journal},
pages = {3287--3300},
title = {The era of big data: Genome-scale modelling meets machine learning},
url = {http://dx.doi.org/10.1016/j.csbj.2020.10.011},
volume = {18},
year = {2020}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
AU  - Antonakoudis,A
AU  - Barbosa,R
AU  - Kotidis,P
AU  - Kontoravdi,K
DO  - 10.1016/j.csbj.2020.10.011
EP  - 3300
PY  - 2020///
SN  - 2001-0370
SP  - 3287
TI  - The era of big data: Genome-scale modelling meets machine learning
T2  - Computational and Structural Biotechnology Journal
UR  - http://dx.doi.org/10.1016/j.csbj.2020.10.011
UR  - http://hdl.handle.net/10044/1/84715
VL  - 18
ER  -