arXiv subjects

Nicholas A. M. Monk

Publications and source records attributed to Nicholas A. M. Monk.

3 recordsLinked to original sources

arXiv2021

Learning to Transfer: A Foliated Theory

Learning to transfer considers learning solutions to tasks in a such way that relevant knowledge can be transferred from known task solutions to new, related tasks. This is important for general learning, as well as for improving the efficiency of the learning process. While techniques for learning to transfer have been studied experimentally, we still lack a foundational description of the problem that exposes what related tasks are, and how relationships between tasks can be exploited constructively. In this work, we introduce a framework using the differential geometric theory of foliations that provides such a foundation.

cs.LG↗

arXiv2009

Oscillatory Notch pathway activity in a delay model of neuronal differentiation

Lateral inhibition resulting from a double-negative feedback loop underlies the assignment of different fates to cells in many developmental processes. Previous studies have shown that the presence of time delays in models of lateral inhibition can result in significant oscillatory transients before patterned steady states are reached. We study the impact of local feedback loops in a model of lateral inhibition based on the Notch signalling pathway, elucidating the roles of intracellular and intercellular delays in controlling the overall system behaviour. The model exhibits both in-phase and out-of-phase oscillatory modes, and oscillation death. Interactions between oscillatory modes can generate complex behaviours such as intermittent oscillations. Our results provide a framework for exploring the recent observation of transient Notch pathway oscillations during fate assignment in vertebrate neurogenesis.

q-bio.CB↗

arXiv2003

On the structure of proten-protein interaction networks

We present a simple model for the underlying structure of protein-protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the interactions of human proteins lend support to this model. The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large scale yeast two-hybrid screens of yeast protein-protein interactions. Sampling sub-graphs from the underlying graphs generated with our model, in a way analogous to the sampling performed in large scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

q-bio.MN↗