Control Engineering Inspired Design Tools for Synthetic Biology
Lead Research Organisation:
Imperial College London
Department Name: Bioengineering
Abstract
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Publications
SCHAUB M
(2014)
Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution
in Network Science
Schaub MT
(2015)
Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.
in PLoS computational biology
Schaub MT
(2019)
Multiscale dynamical embeddings of complex networks.
in Physical review. E
Schumacher J
(2013)
Nitrogen and carbon status are integrated at the transcriptional level by the nitrogen regulator NtrC in vivo.
in mBio
Strelkowa N
(2012)
Stochastic oscillatory dynamics of generalized repressilators
Tomazou M
(2018)
Computational Re-design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation.
in Cell systems
Wang B
(2013)
Rewiring cell signalling through chimaeric regulatory protein engineering.
in Biochemical Society transactions
Wang B
(2014)
Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks.
in Nucleic acids research
Wang B
(2015)
Amplification of small molecule-inducible gene expression via tuning of intracellular receptor densities.
in Nucleic acids research
Wang B
(2013)
A modular cell-based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals.
in Biosensors & bioelectronics
Yuan Y
(2013)
Decentralised minimum-time consensus
in Automatica
Yuan Y
(2011)
Decentralised minimal-time consensus
Description | * A series of mathematical methods for the analysis of data from experiments in Systems and Synthetic Biology including: ** protocols for the generation of models in Synthetic Biology in iteration with data generation (elimination of variables and parameter fitting) ** algorithms for data fitting of time series using evolutionary Monte Carlo methods ** graph-theoretical algorithms for the analysis of flows on networks, including directed networks ** finding roles in directed networks of metabolites with applications to social networks ** application to a reformulation of metabolic networks using flux graphs in collaboration with Polytechnic University of Valencia and Oxford University ** data analysis tools for model reduction of synthetic genetic circuits applied directly to data in an iterative cycle, in collaboration with Oxford and Luxembourg ** time series data analysis has been applied to social network data and online behaviours (i.e., website usage in education). |
Exploitation Route | The methods have been taken up in different directions including: * the analysis of Twitter data by several companies and public bodies, * the analysis of anomalies in time series data from the finance industry, * the analysis of allosteric sites in proteins in collaboration with the pharmaceutical industry * the analysis of metabolic networks using Flux Balance Analysis models |
Sectors | Agriculture, Food and Drink,Communities and Social Services/Policy,Creative Economy,Digital/Communication/Information Technologies (including Software),Healthcare,Government, Democracy and Justice,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
Description | The results in this work have been applied to collaborations with Syngenta leading to the development of methods for the analysis of toxicological data collected by the company to assess the potential carcinogenic effect of compounds being developed as potential herbicides before they are released to the environment. The work was followed up with funding by Syngenta for a pilot project funding an RA and through an EU-funded PhD studentship under the AgriNet initiative. In addition, the work has also led to unexpected connections with other data types, including time series in social media, patient trajectories in healthcare, web usage of online course by students, network analytics of social networks, and analysis of text documents. This has led to ongoing collaborations with Spotify, LayerIV (companies in data science) as well as collaborations with NHS Trusts. |
First Year Of Impact | 2013 |
Sector | Agriculture, Food and Drink,Communities and Social Services/Policy,Creative Economy,Digital/Communication/Information Technologies (including Software),Healthcare,Government, Democracy and Justice,Pharmaceuticals and Medical Biotechnology |
Impact Types | Societal,Economic |
Description | Syngenta collaborative grant |
Amount | £70,000 (GBP) |
Organisation | Syngenta International AG |
Department | Syngenta Ltd (Bracknell) |
Sector | Private |
Country | United Kingdom |
Start | 09/2013 |
End | 11/2014 |
Description | Organisation of the international Workshop on "Control Engineering and Synthetic Biology", University of Oxford, Oxford, 10-12 September 2014 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Over 100 fellow researchers (professors, postdocs, graduate students) attended the workshop that Dr Stan co-organised at the University of Oxford. Furthermore, Dr Jordan Ang gave an talk describing the EPSRC funded project EP/K020617/1. Many requests for additional discussions. |
Year(s) Of Engagement Activity | 2014 |
URL | http://sysos.eng.ox.ac.uk/wiki/index.php/Workshop_on_Control_Engineering_and_Synthetic_Biology |