High-resolution analysis of tomato leaf elongation: the application of novel time-series analysis techniques

This paper demonstrates the use of a novel suite of data-based, recursive m odelling techniques for the investigation of biological and other time-seri es data, including high resolution leaf elongation. The Data-Based Mechanis tic (DBM) modelling methodology rejects the common practice of empirical cu rve fitting for a more objective approach where the model structure is not assumed a priori, but instead is identified directly from the data series i n a stochastic form. Further, this novel approach takes advantage of the la test techniques in optimal recursive estimation of non-stationary and non-l inear time-series. ere, the utility and ease of use of these techniques is demonstrated in the examination of two time-series of leaf elongation in an expanding leaf of tomato (Lycopersicon esculentum L. cv. Ailsa Craig) grow ing in a root pressure vessel (RPV). Using this analysis, the component sig nals of the elongation series are extracted and considered in relation to p hysiological processes. It is hoped that this paper will encourage the wide r use of these new techniques, as well as the associated Data-Based Mechani stic (DBM) modelling strategy, in analytical plant physiology.