Computational and mathematical models have become increasingly important and have contributed to significant advances in our understanding of complex biological systems. We developed a mathematical model to characterise the expression profile of transmembrane electrogenic proteins of excitable cells. The cell of interest is the myometrium smooth muscle cell, which is the principal unit of electrical activity in the uterus. These cells remain quiescent throughout most of gestation, whereas just prior to and during labour they are able to generate spontaneous action potentials. A more detailed and comprehensive characterisation of these cells, in comparison to previous models, would furnish an appropriate tool for the development of therapeutics to manage preterm birth and other perinatal problems associated with uterine contractility, such as postpartum haemorrhage. The "conductome" can be defined as the totality of ion channels and ion transporters expressed by an electrically active cell, i.e., a list specifying the cell surface density and oligomeric composition of each of these species. Gene expression techniques can accurately survey the complete set of all mRNA species encoding electrogenic proteins (e.g., subunits of channels). The conductome is constrained by this transcriptome, but the link between the two is complicated by the facts that (i) presence of an mRNA species does not necessarily imply the presence at the transmembrane proteomics level; and (ii) subunits can combine in various ways to give rise to conducting channels with different properties. Every individual potential oligomeric channel complex was represented as a mathematical model on the basis of biophysical data taken from the literature; these data were obtained mainly using heterologous expression systems. We investigate the possibility of combining the behavioural information of the action potential with the detailed molecular data of the transcriptome. The general problem is that electrical behaviour does not necessarily lead to a unique solution. The question addressed here is to what extent the additional information provided by transcriptomics helps to constrain the solution space. We develop and apply a method to characterise the functional redundancy of electrically active cells. We use mRNA sequencing to determine which electrogenic species the cell is capable of expressing, combined with a least-squares parameter estimation procedure to determine the conductome from electrophysiological data. Moreover, we estimate the parameters associated with the gating kinetics from published data, so that the only remaining free parameters are the surface densities of the species on the list defined by the transcriptomics analysis.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:641014 |
| Date | January 2015 |
| Creators | Atia, Jolene |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/66818/ |
Page generated in 0.0125 seconds