Biomedical subjects

David Gavaghan

Publications and source records attributed to David Gavaghan.

8 recordsLinked to original sources

PubMed2006

SGTx1, a Kv channel gating-modifier toxin, binds to the interfacial region of lipid bilayers.

SGTx1 is a gating-modifier toxin that has been shown to inhibit the voltage-gated potassium channel Kv2.1. SGTx1 is thought to bind to the S3b-S4a region of the voltage-sensor, and is believed to alter the energetics of gating. Gating-modifier toxins such as SGTx1 are of interest as they can be used to probe the structure and dynamics of their target channels. Although there are experimental data for SGTx1, its interaction with lipid bilayer membranes remains to be characterized. We performed atomistic and coarse-grained molecular dynamics simulations to study the interaction of SGTx1 with a POPC and a 3:1 POPE/POPG lipid bilayer membrane. We reveal the preferential partitioning of SGTx1 into the water/membrane interface of the bilayer. We also show that electrostatic interactions between the charged residues of SGTx1 and the lipid headgroups play an important role in stabilizing SGTx1 in a bilayer environment.

Animals↗

PubMed2006

Enabling computer models of the heart for high-performance computers and the grid.

Although it is now feasible to compute multi-cellular models of the heart on a personal desktop or laptop computer, it is not feasible to undertake the detailed sweeps of high-dimensional parameter spaces required if we are to undertake in silico experimentation of the complex processes that constitute heart disease. For this research, modelling requirements move rapidly beyond the limit of commodity computers' resource both in terms of their memory footprint and the speed of calculation, so that multi-processor architectures must be considered. In addition, as such models have become more mature and have been validated against experimental data, there is increasing pressure for experimentalists to be able to make use of these models themselves as a key tool for hypothesis formulation and in planning future experimental studies to test those hypotheses. This paper discusses our initial experiences in a large-scale project (the Integrative Biology (IB) e-Science project) aimed at meeting these dual aims. We begin by putting the research in context by describing in outline the overall aims of the IB project, in particular focusing on the challenge of enabling novice users to make full use of high-performance resources without the need to gain detailed technical expertise in computing. We then discuss our experience of adapting one particular heart modelling package, Cellular Open Resource, and show how the solving engine of this code was dissected from the rest of the package, ported to C++ and parallelized using the Message-Passing Interface. We show that good parallel efficiency and realistic memory reduction can be achieved on simple geometries. We conclude by discussing lessons learnt in this process.

Action Potentials↗

PubMed2006

Post-genomic science: cross-disciplinary and large-scale collaborative research and its organizational and technological challenges for the scientific research process.

We examine recent developments in cross-disciplinary science and contend that a 'Big Science' approach is increasingly evident in the life sciences-facilitated by a breakdown of the traditional barriers between academic disciplines and the application of technologies across these disciplines. The first fruits of 'Big Biology' are beginning to be seen in, for example, genomics, (bio)-nanotechnology and systems biology. We suggest that this has profound implications for the research process and presents challenges both in technological design, in the provision of infrastructure and training, in the organization of research groups, and in providing suitable research funding mechanisms and reward systems. These challenges need to be addressed if the promise of this approach is to be fully realized. In this paper, we will draw on the work of social scientists to understand how these developments in science and technology relate to organizational culture, organizational change and the context of scientific work. We seek to learn from previous technological developments that seemed to offer similar potential for organizational and social change.

Biological Science Disciplines↗

PubMed2006

Mathematical models in physiology.

Computational modelling of biological processes and systems has witnessed a remarkable development in recent years. The search-term (modelling OR modeling) yields over 58000 entries in PubMed, with more than 34000 since the year 2000: thus, almost two-thirds of papers appeared in the last 5-6 years, compared to only about one-third in the preceding 5-6 decades. The development is fuelled both by the continuously improving tools and techniques available for bio-mathematical modelling and by the increasing demand in quantitative assessment of element inter-relations in complex biological systems. This has given rise to a worldwide public domain effort to build a computational framework that provides a comprehensive theoretical representation of integrated biological function-the Physiome. The current and next issues of this journal are devoted to a small sub-set of this initiative and address biocomputation and modelling in physiology, illustrating the breadth and depth of experimental data-based model development in biological research from sub-cellular events to whole organ simulations.

Computer Simulation↗

PubMed2006

Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts.

Functional magnetic resonance imaging (FMRI) is a noninvasive method of imaging brain function in vivo. However, images produced in FMRI experiments are imperfect and contain several artifacts that contaminate the data. These artifacts include rigid-body motion effects, B0-field inhomogeneities, chemical shift, and eddy currents. To investigate these artifacts, with the eventual aim of minimizing or removing them completely, a computational model of the FMR image acquisition process was built that can simulate all of the above-mentioned artifacts. This paper gives an overview of the development of the FMRI simulator. The simulator uses the Bloch equations together with a geometric definition of the object (brain) and a varying T2* model for the BOLD activations. Furthermore, it simulates rigid-body motion of the object by solving Bloch equations for given motion parameters that are defined for an object moving continuously in time, including during the read-out period, which is a novel approach in the area of MRI computer simulations. With this approach it is possible, in a controlled and precise way, to simulate the full effects of various rigid-body motion artifacts in FMRI data (e.g. spin-history effects, B0-motion interaction, and within-scan motion blurring) and therefore formulate and test algorithms for their reduction.

Algorithms↗

PubMed2006

Three-dimensional models of individual cardiac histoanatomy: tools and challenges.

There is a need for, and utility in, the acquisition of data sets of cardiac histoanatomy, with the vision of reconstructing individual hearts on the basis of noninvasive imaging, such as MRI, enriched by reference to detailed atlases of serial histology obtained from representative samples. These data sets would be useful not only as a repository of knowledge regarding the specifics of cardiac histoanatomy, but could form the basis for generation of individualized high-resolution cardiac structure-function models. The current article presents a step in this general direction: it illustrates how whole-heart noninvasive imaging can be combined with whole-heart histology in an approach to achieve automated construction of histoanatomically detailed models of cardiac 3D structure and function at hitherto unprecedented resolution and accuracy (based on 26.4 x 26.4 x 24.4 microm MRI voxel size, and enriched by histological detail). It provides an overview of the tools used in this quest and outlines challenges posed by the approach in the light of applications that may benefit from the availability of such data and tools.

Animals↗

PubMed1998

Size is everything--large amounts of information are needed to overcome random effects in estimating direction and magnitude of treatment effects.

Variability in patients' response to interventions in pain and other clinical settings is large. Many explanations such as trial methods, environment or culture have been proposed, but this paper sets out to show that the main cause of the variability may be random chance, and that if trials are small their estimate of magnitude of effect may be incorrect, simply because of the random play of chance. This is highly relevant to the questions of 'How large do trials have to be for statistical accuracy?' and 'How large do trials have to be for their results to be clinically valid?' The true underlying control event rate (CER) and experimental event rate (EER) were determined from single-dose acute pain analgesic trials in over 5000 patients. Trial group size required to obtain statistically significant and clinically relevant (0.95 probability of number-needed-to-treat within -/+0.5 of its true value) results were computed using these values. Ten thousand trials using these CER and EER values were simulated using varying group sizes to investigate the variation due to random chance alone. Most common analgesics have EERs in the range 0.4-0.6 and CER of about 0.19. With such efficacy, to have a 90% chance of obtaining a statistically significant result in the correct direction requires group sizes in the range 30-60. For clinical relevance nearly 500 patients are required in each group. Only with an extremely effective drug (EER > 0.8) will we be reasonably sure of obtaining a clinically relevant NNT with commonly used group sizes of around 40 patients per treatment arm. The simulated trials showed substantial variation in CER and EER, with the probability of obtaining the correct values improving as group size increased. We contend that much of the variability in control and experimental event rates is due to random chance alone. Single small trials are unlikely to be correct. If we want to be sure of getting correct (clinically relevant) results in clinical trials we must study more patients. Credible estimates of clinical efficacy are only likely to come from large trials or from pooling multiple trials of conventional (small) size.

Analgesics↗

PubMed1997

Deriving dichotomous outcome measures from continuous data in randomised controlled trials of analgesics: use of pain intensity and visual analogue scales.

The aim of this study was to examine whether mean data from categorical pain intensity and visual analogue scales for both pain intensity and relief could be used reliably to derive dichotomous outcome measures for meta-analysis. Individual patient data from randomised controlled trials of single-dose analgesics in acute postoperative pain were used. The methods used were as follows: data from 132 treatments with over 4700 patients were used to calculate mean %maxSPID (categorical pain intensity), %maxVAS-SPID (visual analogue pain intensity) and %maxVAS-TOTPAR (visual analogue pain relief); these were used to derive relationships with the number of patients who achieved at least 50% pain relief (%maxTOTPAR). Good agreement was obtained between the actual number of patients with > 50%maxTOTPAR and the number calculated for all three measures. For SPID, verification included independent data sets. For calculations involving each measure, summing the positive and negative differences between actual and calculated numbers of patients with > 50%maxTOTPAR gave an average difference of less than 0.25 patients per treatment arm. Reports of randomised trials of analgesics frequently describe results of studies in the form of mean derived indices, rather than using discontinuous events, such as number of proportion of patients obtaining at least 50% pain relief. Because mean data inadequately describe information with a non-normal distribution, combining such mean data in systematic reviews may compromise the results. Showing that dichotomous data can reliably be derived from mean SPID, VAS-SPID and VAS-TOTPAR as well as TOTPAR data in previously published acute pain studies makes much more information accessible for meta-analysis.

Analgesics↗