In this section

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{O'Clery:2018,
author = {O'Clery, N and Yuan, Y and Stan, G-B and Barahona, M},
title = {Global Network Prediction from Local Node Dynamics},
url = {http://arxiv.org/abs/1809.00409v1},
year = {2018}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The study of dynamical systems on networks, describing complex interactiveprocesses, provides insight into how network structure affects globalbehaviour. Yet many methods for network dynamics fail to cope with large orpartially-known networks, a ubiquitous situation in real-world applications.Here we propose a localised method, applicable to a broad class of dynamicalmodels on networks, whereby individual nodes monitor and store the evolution oftheir own state and use these values to approximate, via a simple computation,their own steady state solution. Hence the nodes predict their own final statewithout actually reaching it. Furthermore, the localised formulation enablesnodes to compute global network metrics without knowledge of the full networkstructure. The method can be used to compute global rankings in the networkfrom local information; to detect community detection from fast, localtransient dynamics; and to identify key nodes that compute global networkmetrics ahead of others. We illustrate some of the applications of thealgorithm by efficiently performing web-page ranking for a large internetnetwork and identifying the dynamic roles of inter-neurons in the C. Elegansneural network. The mathematical formulation is simple, widely applicable andeasily scalable to real-world datasets suggesting how local computation canprovide an approach to the study of large-scale network dynamics.
AU  - O'Clery,N
AU  - Yuan,Y
AU  - Stan,G-B
AU  - Barahona,M
PY  - 2018///
TI  - Global Network Prediction from Local Node Dynamics
UR  - http://arxiv.org/abs/1809.00409v1
ER  -

Download