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<sub>A</sub> and Ratio<sub>M</sub> - 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<sup>-1</sup> 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<sup>-1</sup>. 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.
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.
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.