This report addresses the following problems associated with the generation of computer models of phospholipid bilayer membranes using molecular dynamics simulations: arbitrary initial structures and short equilibration periods, an Ewald-induced strong coupling of phospholipids, uncertainty regarding which value should be used for surface tension to alleviate the problem of the small size of the membrane, and simultaneous realization of both order parameters and the surface area. We generated a computer model of the liquid-crystalline L-alpha-dimyristoylphosphatidylcholine (DMPC) bilayer, starting from a configuration based on a crystal structure (rather than from an arbitrary structure). To break the crystalline structure, a 20-ps high-temperature pulse of 510 K (but not 450 or 480 K) was effective. The system finally obtained is an all-atom model, with Ewald summation to evaluate Coulombic interactions and a constant surface tension of 35 dynes/cm/water-membrane interface, equilibrated for 12 ns (over 50 ns total calculation time), which reproduces all of the experimentally observed parameters examined in this work. Furthermore, this model shows the presence of significant orientational correlations between neighboring alkyl chains and between shoulder vectors (which show the orientations of the lipids about their long axes) of neighboring DMPCs.

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This paper investigates nonlinear time series modelling using the general state-dependent autoregressive model. To achieve the estimate of the model, an attempt is made to approximate the state-dependent parameter by employing the Gaussian radial basis function for its universal approximation capability. As a result, a radial basis function-based autoregressive (RBF-AR) model is derived which has a form similar to a generalized exponential autoregressive model. To reach the applicability of the RBF-AR model, the evolutionary programming algorithm is employed to select a suitable set of radial basis function centres. By applying the resulting model to some complex data, it is shown that the RBF-AR model can not only reconstruct the dynamics of given nonlinear time series e ectively, but also give much better ® tting to complex time series than the approach of directly RBF neural network modelling. Therefore, as a paradigm combining a statistical model and neural network, the RBF-AR model has better performance than the RBF neural network especially for its problem reduction of the curse of dimensionality which is usually regarded as one of the potential main di culties of RBF neural network modelling.

We developed a dual oscillator model to facilitate the understanding of dynamic interactions between the parafacial respiratory group (pFRG) and the preBötzinger complex (preBötC) neurons in the respiratory rhythm generation. Both neuronal groups were modeled as groups of 81 interconnected pacemaker neurons; the bursting cell model described by Butera and others [model 1 in Butera et al. (J Neurophysiol 81:382–397, 1999a)] were used to model the pacemaker neurons. We assumed (1) both pFRG and preBötC networks are rhythm generators, (2) preBötC receives excitatory inputs from pFRG, and pFRG receives inhibitory inputs from preBötC, and (3) persistent Na+ current conductance and synaptic current conductances are randomly distributed within each population. Our model could reproduce 1:1 coupling of bursting rhythms between pFRG and preBötC with the characteristic biphasic firing pattern of pFRG neurons, i.e., firings during pre-inspiratory and post-inspiratory phases. Compatible with experimental results, the model predicted the changes in firing pattern of pFRG neurons from biphasic expiratory to monophasic inspiratory, synchronous with preBötC neurons. Quantal slowing, a phenomena of prolonged respiratory period that jumps non-deterministically to integer multiples of the control period, was observed when the excitability of preBötC network decreased while strengths of synaptic connections between the two groups remained unchanged, suggesting that, in contrast to the earlier suggestions (Mellen et al., Neuron 37:821–826, 2003; Wittmeier et al., Proc Natl Acad Sci USA 105(46):18000–18005, 2008), quantal slowing could occur without suppressed or stochastic excitatory synaptic transmission. With a reduced excitability of preBötC network, the breakdown of synchronous bursting of preBötC neurons was predicted by simulation. We suggest that quantal slowing could result from a breakdown of synchronized bursting within the preBötC.