Protein molecular motors, which convert chemical energy into kinetic energy, are prime candidates for use in nanodevice in which active transport is required. To be able to design these devices it is essential that the properties of the cytoskeletal filaments propelled by the molecular motors are well established. Here we used micro-contact printed BSA to limit the amount of HMM that can adsorb creating a tightly confined pathway for the filaments to travel. Both the image and statistical analysis of the movement of the filaments through these structures have been used to new insights into the motility behaviour of actomyosin on topographically homogenous, but motor-heterogeneous planar systems. It will be shown that it is possible to determine the persistence length of the filaments and that it is related to the amount of locally adsorbed HMM. This provides a basis that can be used to optimize the design of future nanodevices incorporating the actomyosin system for the active transport.

Molecular motor

Persistence length

Microcontact Printing

10.1117/12.2175772

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Actin Filament Motility Induced Variation of Resonance Frequency and Rigidity of Polymer Surfaces Studied by Quartz Crystal Microbalance

Langmuir (2012)

Harm van Zalinge Jenny Aveyard Joanna Hajne Malin Persson Alf Månsson

This contribution reports on the quantification of the parameters of the motility assays for actomyosin system using a quartz crystal microbalance (QCM). In particular, we report on the difference in the observed resonance frequency and dissipation of a quartz crystal when actin filaments are stationary as opposed to when they are motile. The changes in QCM measurements were studied for various polymer-coated surfaces functionalized with heavy meromyosin (HMM). The results of the QCM experiments show that the HMM-induced sliding velocity of actin filaments is modulated by a combination of the viscoelastic properties of the polymer layer including the HMM motors.

Quartz Crystal Microbalance

Heavy meromyosin

10.1021/la302717y

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Citations (12)

Motility of Actin Filaments on Micro-Contact Printed Myosin Patterns

IEEE Transactions on NanoBioscience (2015)

Joanna Hajne Kristi L. Hanson Harm van Zalinge Dan V. Nicolau

Protein molecular motors, which convert, directly and efficiently, chemical energy into motion, are excellent candidates for integration in hybrid dynamic nanodevices. To integrate and use the full potential of molecular motors in these devices, their design requires a quantitative and precise prediction of the fundamental mechanical and physicochemical features of cytoskeletal proteins operating in artificial environments. In that regard, the behavior of protein molecular motors constructs in/on nano-confined spaces or nanostructured surfaces that aim to control their motility is of critical interest. Here, we used a standard gliding motility assay to study the actin filaments sliding on a surface comprising heavy mero myosin (HMM) micro- and nano-patterns. To print HMM, we used negative tone, micro contact printing of a blocking protein (bovine serum albumin, BSA) on a nitrocellulose surface, followed by specific adsorption of HMM on BSA-free surfaces. While the large BSA-free patterns allowed for selective confinement of actin filaments motility, the BSA-stamped areas displayed intricate nano-sized HMM patterns, which enabled a deeper analysis of the nano-mechanics of actomyosin motility in confined spaces.

Molecular motor

10.1109/tnb.2015.2393052

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The importance of statistical measure when describing phenotype

Joanna Hajne

Data collected in life sciences studies mostly include a genotype description of the organism, a phenotype characterisation of the organism, and experiment-specific covariates including a description of experimental procedures and laboratory (environmental) conditions. Here, phenotype measurements are taken for Neurospora crassa (wild type) growing on agar in the standard laboratory conditions. I define a phenotype as a set of traits including apical extension velocity, branching angle, and branching distance. I use the above measures (traits) to model (estimate) biologically complex filamentous fungi network as a simplified 'In Silico Fungus' consisting of series of straight lines. Phenotype data, under the central limit theorem, is often characterized by means and standard deviations. Subsequently, P values are used to show statistical validity. Here, I question whether making normality assumption based on the popularity of such approach is always justified. Therefore, I test three different scenarios by making different assumptions about the data collected. (1) Firstly, I use the most popular approach: I assume the phenotype data comes from the continuous, normal (Gauss) distribution. Thus, I predict the future measurement outcomes by using normal (Gauss) parametric approximation. (2) Secondly, I use the most intuitive approach: I do not make any assumptions about the data collected and use it to predict the future measurement outcomes by withdrawing values pseudo randomly from the actual, raw, and discrete dataset. (3) Finally, I use the strategy balanced between the previous two: I construct a customised, continuous, and non-parametric distribution based on the data collected. Thus, I predict the future measurement outcomes by using kernel density estimation method. Subsequently, I implement all of the strategies above: (1), (2), and (3) in the in silico fungus programme to compare the computer simulation outcomes. More specifically, I compare the surface coverage, expressed as the proportion of the surface occupied by the fungus. Obtained results show that the differences between different data regimes (1), (2), and (3) are significant. Therefore, I conclude that the correct assessment of the data normality is crucial for the correct interpretation and implementation of scientific observations. I suspect the described data classification process determines successful implementation of biological findings especially in the fields such as medicine and engineering.

Source

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Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach

JMIR Public Health and Surveillance (2018)

Danushka Bollegala Simon Maskell R. J. Sloane Joanna Hajne Munir Pirmohamed

Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality.This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal.To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug.Our proposed method obtains an ADR detection accuracy of 74% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions.By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction-related events.

Causality

Adverse drug reaction

Drug reaction

10.2196/publichealth.8214

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Citations (37)

Actin motility confinement on micro/nanostructured surfaces

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2013)

Jenny Aveyard Joanna Hajne Alf Månsson Malin Persson F.C.M.J.M. van Delft

In recent years there has been increasing interest in the use of molecular motors and cytoskeletal filaments in nanotechnological applications, particularly in the production of biomedical microdevices. In order for this to be possible it is important to exert a high level of control over the movement of the filaments. Chemical patterning techniques are often used to achieve this but these methods are often complex and the surface chemistry can be unstable. We investigated whether microfabricated silicon oxide lines of different widths with z-nanoscale heights of 20, 40 and 80 nm coated with heavy meromyosin (HMM) molecular motors could be used to control the motility of actin filaments by topographical means. Results demonstrated that filaments were confined by structures exceeding 20 nm in height regardless of the width of the channel indicating that topographical confinement offers a simple and possibly more cost-effective alternative to chemical patterning.

Heavy meromyosin

Molecular motor

10.1117/12.2010094

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