Development of a cloud-based Bioinformatics Training Platform.

The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP.

Multiparameter Estimation in Voltammetry When an Electron Transfer Process Is Coupled to a Chemical Reaction.

Estimation of thermodynamic and kinetic parameters in electrochemical studies is usually undertaken via comparison of the experimental results with theory based on a model that mimics the experiment. The present study examines the credibility of transient d.c. and a.c. voltammetric theory-experiment comparisons for recovery of the parameters needed to model the ubiquitous mechanism when an electron transfer (E) reaction is followed by a chemical (C) step in the EC process ([Formula: see text]). The data analysis has been undertaken using optimization methods facilitated in some cases by grid computing. These techniques have been applied to the simulated (5% noise added) and experimental (reduction of trans-stilbene) voltammograms to assess the capabilities of parameter recovery of E(0) (reversible potential for the E step), k(0) (heterogeneous electron transfer rate constant at E(0)), α (charge transfer coefficient for the E step), and k(f) and k(b) (forward and backward rate constants for the C step) under different kinetic regimes. The advantages provided by the use of a.c. instead of d.c. voltammetry and data optimization methods over heuristic approaches to "experiment"-theory comparisons are discussed, as are the limitations in the efficient recovery of a unique set of parameters for the EC mechanism. In the particular experimental case examined herein, results for the protonation of the electrochemically generated stilbene dianion demonstrate that, notwithstanding significant advances in experiment and theory of voltammetric analysis, reliable recovery of the parameters for the EC mechanism with a fast chemical process remains a stiff problem.

Inappropriate use of the quasi-reversible electrode kinetic model in simulation-experiment comparisons of voltammetric processes that approach the reversible limit.

Many electrode processes that approach the "reversible" (infinitely fast) limit under voltammetric conditions have been inappropriately analyzed by comparison of experimental data and theory derived from the "quasi-reversible" model. Simulations based on "reversible" and "quasi-reversible" models have been fitted to an extensive series of a.c. voltammetric experiments undertaken at macrodisk glassy carbon (GC) electrodes for oxidation of ferrocene (Fc(0/+)) in CH3CN (0.10 M (n-Bu)4NPF6) and reduction of [Ru(NH3)6](3+) and [Fe(CN)6](3-) in 1 M KCl aqueous electrolyte. The confidence with which parameters such as standard formal potential (E(0)), heterogeneous electron transfer rate constant at E(0) (k(0)), charge transfer coefficient (α), uncompensated resistance (Ru), and double layer capacitance (CDL) can be reported using the "quasi-reversible" model has been assessed using bootstrapping and parameter sweep (contour plot) techniques. Underparameterization, such as that which occurs when modeling CDL with a potential independent value, results in a less than optimal level of experiment-theory agreement. Overparameterization may improve the agreement but easily results in generation of physically meaningful but incorrect values of the recovered parameters, as is the case with the very fast Fc(0/+) and [Ru(NH3)6](3+/2+) processes. In summary, for fast electrode kinetics approaching the "reversible" limit, it is recommended that the "reversible" model be used for theory-experiment comparisons with only E(0), Ru, and CDL being quantified and a lower limit of k(0) being reported; e.g., k(0) ≥ 9 cm s(-1) for the Fc(0/+) process.

New insights into the analysis of the electrode kinetics of flavin adenine dinucleotide redox center of glucose oxidase immobilized on carbon electrodes.

New insights into electrochemical kinetics of the flavin adenine dinucleotide (FAD) redox center of glucose-oxidase (GlcOx) immobilized on reduced graphene oxide (rGO), single- and multiwalled carbon nanotubes (SW and MWCNT), and combinations of rGO and CNTs have been gained by application of Fourier transformed AC voltammetry (FTACV) and simulations based on a range of models. A satisfactory level of agreement between experiment and theory, and hence establishment of the best model to describe the redox chemistry of FAD, was achieved with the aid of automated e-science tools. Although still not perfect, use of Marcus theory with a very low reorganization energy (≤0.3 eV) best mimics the experimental FTACV data, which suggests that the process is gated as also deduced from analysis of FTACV data obtained at different frequencies. Failure of the simplest models to fully describe the electrode kinetics of the redox center of GlcOx, including those based on the widely employed Laviron theory is demonstrated, as is substantial kinetic heterogeneity of FAD species. Use of a SWCNT support amplifies the kinetic heterogeneity, while a combination of rGO and MWCNT provides a more favorable environment for fast communication between FAD and the electrode.

Parametric computation predicts a multiplicative interaction between synaptic strength parameters that control gamma oscillations.

Gamma oscillations are thought to be critical for a number of behavioral functions, they occur in many regions of the brain and through a variety of mechanisms. Fast repetitive bursting (FRB) neurons in layer 2 of the cortex are able to drive gamma oscillations over long periods of time. Even though the oscillation is driven by FRB neurons, strong feedback within the rest of the cortex must modulate properties of the oscillation such as frequency and power. We used a highly detailed model of the cortex to determine how a cohort of 33 parameters controlling synaptic drive might modulate gamma oscillation properties. We were interested in determining not just the effects of parameters individually, but we also wanted to reveal interactions between parameters beyond additive effects. To prevent a combinatorial explosion in parameter combinations that might need to be simulated, we used a fractional factorial design (FFD) that estimated the effects of individual parameters and two parameter interactions. This experiment required only 4096 model runs. We found that the largest effects on both gamma power and frequency came from a complex interaction between efficacy of synaptic connections from layer 2 inhibitory neurons to layer 2 excitatory neurons and the parameter for the reciprocal connection. As well as the effect of the individual parameters determining synaptic efficacy, there was an interaction between these parameters beyond the additive effects of the parameters alone. The magnitude of this effect was similar to that of the individual parameters, predicting that it is physiologically important in setting gamma oscillation properties.

A global sensitivity tool for cardiac cell modeling: Application to ionic current balance and hypertrophic signaling.

Cardiovascular diseases are the major cause of death in the developed countries. Identifying key cellular processes involved in generation of the electrical signal and in regulation of signal transduction pathways is essential for unraveling the underlying mechanisms of heart rhythm behavior. Computational cardiac models provide important insights into cardiovascular function and disease. Sensitivity analysis presents a key tool for exploring the large parameter space of such models, in order to determine the key factors determining and controlling the underlying physiological processes. We developed a new global sensitivity analysis tool which implements the Morris method, a global sensitivity screening algorithm, onto a Nimrod platform, which is a distributed resources software toolkit. The newly developed tool has been validated using the model of IP3-calcineurin signal transduction pathway model which has 30 parameters. The key driving factors of the IP3 transient behaviour have been calculated and confirmed to agree with previously published data. We next demonstrated the use of this method as an assessment tool for characterizing the structure of cardiac ionic models. In three latest human ventricular myocyte models, we examined the contribution of transmembrane currents to the shape of the electrical signal (i.e. on the action potential duration). The resulting profiles of the ionic current balance demonstrated the highly nonlinear nature of cardiac ionic models and identified key players in different models. Such profiling suggests new avenues for development of methodologies to predict drug action effects in cardiac cells.

High-throughput cardiac science on the Grid.

Cardiac electrophysiology is a mature discipline, with the first model of a cardiac cell action potential having been developed in 1962. Current models range from single ion channels, through very complex models of individual cardiac cells, to geometrically and anatomically detailed models of the electrical activity in whole ventricles. A critical issue for model developers is how to choose parameters that allow the model to faithfully reproduce observed physiological effects without over-fitting. In this paper, we discuss the use of a parametric modelling toolkit, called Nimrod, that makes it possible both to explore model behaviour as parameters are changed and also to tune parameters by optimizing model output. Importantly, Nimrod leverages computers on the Grid, accelerating experiments by using available high-performance platforms. We illustrate the use of Nimrod with two case studies, one at the cardiac tissue level and one at the cellular level.