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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{Hurault:2020:10.1111/cea.13717,
author = {Hurault, G and Domínguez-Hüttinger, E and Langan, S and Williams, H and Tanaka, R},
doi = {10.1111/cea.13717},
journal = {Clinical and Experimental Allergy},
pages = {1258--1266},
title = {Personalised prediction of daily eczema severity scores using a mechanistic machine learning model},
url = {http://dx.doi.org/10.1111/cea.13717},
volume = {50},
year = {2020}
}

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TY  - JOUR
AB  - Background: A topic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares  and  remission. Designing  personalised  treatment  strategies  for  AD is  challenging, given  the  apparent  unpredictability  and  large  variation  in  AD  symptoms  and  treatment responses within  and  across  individuals.Better  prediction  of  AD  severity  over  time  for individual patients could help to select optimum timing and type of treatment for improving disease control.Objective: We aimed to develop a proof-of-principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis.Methods:  We   designed  a probabilistic predictive  model and  trained  it  using  Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children ove r6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting.Results: Our model  was able  to  predict  future  severity  scores  at  the  individual  level  and improved  chance-level  forecast  by  60%.  Heterogeneous  patterns  in  severity  trajectories were  captured  with  patient-specific parameters  such  as  the short-term  persistence of  AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment.Conclusions: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level,and could inform the design of personalised treatment strategies that can be tested in future studies.Our model-based approach can be applied to other  diseases such  as  asthma  with  apparent  unpredictability  and  large  variation  in symptoms and treatment responses.
AU  - Hurault,G
AU  - Domínguez-Hüttinger,E
AU  - Langan,S
AU  - Williams,H
AU  - Tanaka,R
DO  - 10.1111/cea.13717
EP  - 1266
PY  - 2020///
SN  - 0954-7894
SP  - 1258
TI  - Personalised prediction of daily eczema severity scores using a mechanistic machine learning model
T2  - Clinical and Experimental Allergy
UR  - http://dx.doi.org/10.1111/cea.13717
UR  - https://onlinelibrary.wiley.com/doi/10.1111/cea.13717
UR  - http://hdl.handle.net/10044/1/81333
VL  - 50
ER  -