Evolving graphs: dynamical models, inverse problems and propagation

Applications such as neuroscience, telecommunication, online social networking, transport and retail trading give rise to connectivity patterns that change over time. In this work, we address the resulting need for network models and computational algorithms that deal with dynamic links. We introduce a new class of evolving range-dependent random graphs that gives a tractable framework for modelling and simulation. We develop a spectral algorithm for calibrating a set of edge ranges from a sequence of network snapshots and give a proof of principle illustration on some neuroscience data. We also show how the model can be used computationally and analytically to investigate the scenario where an evolutionary process, such as an epidemic, takes place on an evolving network. This allows us to study the cumulative effect of two distinct types of dynamics.

Therapeutic modulation of microbiota-host metabolic interactions

The complex metabolic relationships between the host and its microbiota change throughout life and vary extensively between individuals, affecting disease risk factors and therapeutic responses through drug metabolism. Elucidating the biochemical mechanisms underlying this human supraorganism symbiosis is yielding new therapeutic insights to improve human health, treat disease, and potentially modify human disease risk factors. Therapeutic options include targeting drugs to microbial genes or co-regulated host pathways and modifying the gut microbiota through diet, probiotic and prebiotic interventions, bariatric surgery, fecal transplants, or ecological engineering. The age-associated co-development of the host and its microbiota provides a series of windows for therapeutic intervention from early life through old age