Dr. David J. G. Bakewell Profile

Dr. David J. G. Bakewell

Lecturer at Univ of Liverpool

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Proceedings Article | 22 February 2008 Paper

Statistical analysis of the motility of nano-objects propelled by molecular motors

Raquel Conceição, David Bakewell, Dan Nicolau

Proceedings Volume 6865, 686506 (2008) https://doi.org/10.1117/12.759116

KEYWORDS: Statistical analysis, Proteins, Image resolution, Motion analysis, Head, Automatic tracking, In vitro testing, In vivo imaging, Excel, Electrodes

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Motility assays are the tools of choice for the studies regarding the motility of protein molecular motors in vitro. Despite their wide usage, some simple, but fundamental issues still need to be specifically addressed in order to achieve the best and the most meaningful motility analyses. Several tracking methods used for the study of motility have been compared. By running different statistical analyses, the impact of space versus time resolution was also studied. It has been found that for a space resolution of 80 nm and 145 nm per pixel for kinesin-microtubule and actomyosin assays, respectively, the best time resolution was ~0.9 and ~10 frame per second, respectively. A rough relationship - Ratio_A and Ratio_M - between space and time resolutions and velocity for actin filaments and microtubules, respectively, was found. The motility parameters such as velocity, acceleration and deflection angle were statistically analysed in frequency distribution and time domain graphs for both motors assays. One of the aims of these analyses was to study if one or two populations were present in either assay. Particularly for actomyosin assays, electric fields varying from 0 to ~10000 Vm^-1 were applied and the previous parameters and the angle between filaments motion and the electric field vector were also statistically analysed. It was observed that this angle was reduced by ~55º with ~5900 Vm^-1. The overall behaviour of the motors was discussed bearing in mind both present and previous results and some physio-biological characteristics. Kinesin-microtubule and actomyosin (simple and electric fields) assays were compared. Some new experiments are suggested in order to accomplish a better understanding of these motors and optimise their role in the applications that depend on them.

Proceedings Article | 27 December 2007 Paper

Nanosize particle movement in time-modulated nonuniform electric fields: a Fourier-Bessel series model

David Bakewell

Proceedings Volume 6799, 679914 (2007) https://doi.org/10.1117/12.759696

KEYWORDS: Dielectrophoresis, Particles, Electrodes, Finite element methods, Diffusion, Polarizability, Computer simulations, Stochastic processes, Mathematical modeling, 3D modeling

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Dielectrophoresis (DEP) is a popular, noncontact electrokinetic method for separating and transporting nanosize biomolecules and colloids in microdevices. DEP is the movement of polarizable particles arising from the action of nonuniform electric fields. The spatial-temporal distribution of nanosize particles moving under the action of a deterministic DEP force and stochastic Brownian thermal motion can be described by the Fokker Planck equation (FPE). The application of DEP electrokinetics in micro-technologies means nanoscale particle movement needs to be modeled and measured quantitatively. Quantitative FPE prediction (using numerical values for relevant dielectric and fluid parameters) of DEP-driven particle transport is usually achieved numerically by using Finite Element methods (FEMs). The drawbacks of FEMs are inaccuracy where the electric field is extremely inhomogeneous and they offer little insight into the mathematical structure of the FPE solution. The latter is important, not only for prediction of particle movement, but also the 'reverse' process where parameter values are estimated from measurements of DEP experiments. In this paper, a Fourier-Bessel series solution to the FPE is derived that describes particle movement under the action of DEP in a simple chamber. The solution assumes the DEP force exhibits a hyperbolic spatial profile and can be extended to the case that assumes an exponential decay. This applies to planar arrays, such as, interdigitated electrodes. Time-dependent DEP particle collection and release (after the DEP is switched off) from a surface is evaluated for strong and weak DEP forces. Temporal DEP responses can be classified as state-transitions and perturbations, respectively.

Proceedings Article | 14 December 2006 Paper

Toward 'smart' DNA microarrays: algorithms for improving data quality and statistical inference

David J. Bakewell, Ernst Wit

Proceedings Volume 6416, 64160R (2006) https://doi.org/10.1117/12.705422

KEYWORDS: Data modeling, Statistical analysis, Scanners, Algorithm development, Monte Carlo methods, Statistical inference, Genetic algorithms, Detection and tracking algorithms, Statistical modeling, Organisms

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DNA microarrays are a laboratory tool for understanding biological processes at the molecular scale and future applications of this technology include healthcare, agriculture, and environment. Despite their usefulness, however, the information microarrays make available to the end-user is not used optimally, and the data is often noisy and of variable quality. This paper describes the use of hierarchical Maximum Likelihood Estimation (MLE) for generating algorithms that improve the quality of microarray data and enhance statistical inference about gene behavior. The paper describes examples of recent work that improves microarray performance, demonstrated using data from both Monte Carlo simulations and published experiments. One example looks at the variable quality of cDNA spots on a typical microarray surface. It is shown how algorithms, derived using MLE, are used to "weight" these spots according to their morphological quality, and subsequently lead to improved detection of gene activity. Another example, briefly discussed, addresses the "noisy data about too many genes" issue confronting many analysts who are also interested in the collective action of a group of genes, often organized as a pathway or complex. Preliminary work is described where MLE is used to "share" variance information across a pre-assigned group of genes of interest, leading to improved detection of gene activity.

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