Oxford Molecular Motors

Abstract:

Advances in high‐resolution microscopy and other techniques have emphasized the spatio‐temporal nature of information transfer through signal transduction pathways. The compartmentalization of signaling molecules and the existence of microdomains are now widely acknowledged as key features in biochemical signaling. To complement experimental observations of spatio‐temporal dynamics, mathematical modeling has emerged as a powerful tool. Using modeling, one can not only recapitulate experimentally observed dynamics of signaling molecules, but also gain an understanding of the underlying mechanisms in order to generate experimentally testable predictions. Reaction–diffusion systems are commonly used to this end; however, the analysis of coupled nonlinear systems of partial differential equations, generated by considering large reaction networks is often challenging. Here, we aim to provide an introductory tutorial for the application of reaction–diffusion models to the spatio‐temporal dynamics of signaling pathways. In particular, we outline the steps for stability analysis of such models, with a focus on biochemical signal transduction.

Abstract:

© 2017, © 2017, © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. The bacterial flagellar motor (BFM) is a rotary electric nanomachine that drives swimming in a wide variety of bacterial species. There have been many milestones, both theoretical and experimental, that have furthered our understanding of this tiny motor since the first swimming flagellated bacteria was observed. In this article, we review some of these key events, and illustrate how theory and experiment intertwine and inform each other towards a deeper understanding of the BFM’s mechanism. Experimental results have inspired theoreticians to build and update models, while model predictions have served to guide experimental design. This cooperative and mutually beneficial communication is a prime example of the interdisciplinary and open nature of modern scientific research.