The availability of increasingly efficient computational systems has made feasible the otherwise burdensome analysis of complex neurobiological data, such as in vivo neuroendocrine glandular secretory activity. Neuroendocrine data sets are typically sparse, noisy and generated by combined processes (such as secretion and metabolic clearance) operating simultaneously over both short and long time spans. The concept of a convolution integral to describe the impact of two or more processes acting jointly has offered an informative mathematical construct with which to dissect (deconvolve) specific quantitative features of in vivo neuroendocrine phenomena. Appropriate computer-based deconvolution algorithms are capable of solving families of 100-300 simultaneous integral equations for a large number of secretion and/or clearance parameters of interest. For example, one application of computer technology allows investigators to deconvolve the number, amplitude and duration of statistically significant underlying secretory episodes of algebraically specifiable waveform and simultaneously estimate subject- and condition-specific neurohormone metabolic clearance rates using all observed data and their experimental variances considered simultaneously. Here, we will provide a definition of selected deconvolution techniques, review their conceptual basis, illustrate their applicability to biological data and discuss new perspectives in the arena of computer-based deconvolution methodologies for evaluating complex biological events.
|Original language||English (US)|
|Number of pages||6|
|State||Published - 1990|
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)