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Journal articleBacik KA, Schaub MT, Beguerisse-Diaz M, et al., 2016,
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
, PLOS Computational Biology, Vol: 12, ISSN: 1553-734XWe exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios.
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Book chapterAmor B, Vuik S, Callahan R, et al., 2016,
Community detection and role identification in directed networks: understanding the Twitter network of the care.data debate
, Dynamic Networks and Cyber-Security, Editors: Adams, Heard, Publisher: World Scientific, Pages: 111-136, ISBN: 978-1-60558752-3With the rise of social media as an important channel for the debate anddiscussion of public affairs, online social networks such as Twitter havebecome important platforms for public information and engagement by policymakers. To communicate effectively through Twitter, policy makers need tounderstand how influence and interest propagate within its network of users. Inthis chapter we use graph-theoretic methods to analyse the Twitter debatesurrounding NHS England's controversial care.data scheme. Directionality is acrucial feature of the Twitter social graph - information flows from thefollowed to the followers - but is often ignored in social network analyses;our methods are based on the behaviour of dynamic processes on the network andcan be applied naturally to directed networks. We uncover robust communities ofusers and show that these communities reflect how information flows through theTwitter network. We are also able to classify users by their differing roles indirecting the flow of information through the network. Our methods and resultswill be useful to policy makers who would like to use Twitter effectively as acommunication medium.
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Conference paperSootla A, Oyarzun DA, Angeli D, et al., 2015,
Shaping Pulses to Control Bi-Stable Biological Systems
, American Control Conference 2015, Publisher: IEEE, Pages: 3138-3143In this paper, we present a framework for shaping pulses to control biological systems, and specifically systems in synthetic biology. By shaping we mean computing the magnitude and the length of a pulse, application of which results in reaching the desired control objective. Hence the control signals have only two parameters, which makes these signals amenable to wetlab implementations. We focus on the problem of switching between steady states in a bistable system. We show how to estimate the set of the switching pulses, if the trajectories of the controlled system can be bounded from above and below by the trajectories of monotone systems. We then generalise this result to systems with parametric uncertainty under some mild assumptions on the set of admissible parameters, thus providing some robustness guarantees. We illustrate the results on some example genetic circuits.
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