Molecular Events in the Synthesis and Assembly of a Nicotinic Acetylcholine Receptor

The acetylcholine receptor (AChR) has proven the most accessible model system for structure-function studies of a transmitter-activated ion channel (Karlin 1980; Conti-Tronconi and Raftery 1982). However, it also presents a good opportunity to study molecular events in the assembly of a multi-subunit integral membrane protein. Moreover, the synthesis of AChR is regulated during development, both by trophic factors (Jessel et al, 1979) and by activity (Brockes and Hall 1975). To understand the mechanistic bases of these regulatory influences, it would be helpful to know all the molecular events and intermediates in AChR biogenesis. We can presume that these events encompass gene transcription, mRNA processing and transport out of the nucleus, translation, subunit assembly, and intracellular transport of the subunits. The following discussion will concern what we have learned about the stages including and following mRNA translation

A Linear Iterative Unfolding Method

A frequently faced task in experimental physics is to measure the probability distribution of some quantity. Often this quantity to be measured is smeared by a non-ideal detector response or by some physical process. The procedure of removing this smearing effect from the measured distribution is called unfolding, and is a delicate problem in signal processing, due to the well-known numerical ill behavior of this task. Various methods were invented which, given some assumptions on the initial probability distribution, try to regularize the unfolding problem. Most of these methods definitely introduce bias into the estimate of the initial probability distribution. We propose a linear iterative method, which has the advantage that no assumptions on the initial probability distribution is needed, and the only regularization parameter is the stopping order of the iteration, which can be used to choose the best compromise between the introduced bias and the propagated statistical and systematic errors. The method is consistent: "binwise" convergence to the initial probability distribution is proved in absence of measurement errors under a quite general condition on the response function. This condition holds for practical applications such as convolutions, calorimeter response functions, momentum reconstruction response functions based on tracking in magnetic field etc. In presence of measurement errors, explicit formulae for the propagation of the three important error terms is provided: bias error, statistical error, and systematic error. A trade-off between these three error terms can be used to define an optimal iteration stopping criterion, and the errors can be estimated there. We provide a numerical C library for the implementation of the method, which incorporates automatic statistical error propagation as well.Comment: Proceedings of ACAT-2011 conference (Uxbridge, United Kingdom), 9 pages, 5 figures, changes of corrigendum include