Characterizing Signal Transduction Networks and Biological Responses Using Computer Simulations and Machine Learning

The use of computer simulations in biology is often limited due to the lack of experimentally measured parameters. In these scenarios, parameter exploration can be used to probe biological systems and refine understanding of biological mechanisms. For systems with few unknown parameters, parameter sweeps that concurrently vary all unknown parameters are tractable. In complex systems with many unknown parameters, supervised machine learning algorithms can be used to discover parameters leading to targeted system responses. In this thesis, we study three biological problems in which we use parameter exploration methods to gain mechanistic insights. We first explore the role of altered metabolism in cancer cells that reside in heterogeneous tumor microenvironments. We use a multiscale, hybrid cellular automaton model to evaluate tumor progression while varying malignant cell traits using a systematic parameter sweep. The results reveal distinct growth regimes associated with varied malignant cell traits. We then study kinetic mechanisms governing fixed-topology signal transduction networks and use evolutionary algorithms to discover kinetic parameters that produce specified network responses. We analyze the growth-response network in Arabidopsis with this supervised machine learning approach. This allows us to identify constraints on kinetic parameters that govern the observed responses. The evolved parameters are used to calculate the responses of individual network components, which are used to generate hypotheses that can be tested in vivo to help determine the network topology. We finally apply a similar approach to redesign signal transduction networks. We demonstrate that the T cell receptor network and an oscillator network show remarkable flexibility in generating altered responses to input, and we further use a nonlinear clustering method to identify design criteria for the underlying kinetic parameters. For each project, observations produced from in silico simulations lead to the formation of hypotheses that are experimentally testable

The evolving relation between star-formation rate and stellar mass in the VIDEO Survey since z=3z=3

We investigate the star-formation rate (SFR) and stellar mass ($M_*$) relation of a star-forming (SF) galaxy sample in the XMM-LSS field to $z\sim 3.0$ using the near-infrared data from the VISTA Deep Extragalactic Observations (VIDEO) survey. Combining VIDEO with broad-band photometry, we use the SED fitting algorithm CIGALE to derive SFRs and $M_*$ and have adapted it to account for the full photometric redshift PDF uncertainty. Applying a SF selection using the D4000 index, we find evidence for strong evolution in the normalisation of the SFR-$M_*$ relation out to $z\sim 3$ and a roughly constant slope of (SFR $\propto M_*^{\alpha}$) $\alpha=0.69\pm0.02$ to $z\sim 1.7$. We find this increases close to unity toward $z\sim2.65$. Alternatively, if we apply a colour selection, we find a distinct turnover in the SFR-$M_*$ relation between $0.7\lesssim z\lesssim2.0$ at the high mass end, and suggest that this is due to an increased contamination from passive galaxies. We find evolution of the specific SFR $\propto(1+z)^{2.60}$ at $\log(M_*)\sim$10.5, out to $z\lesssim2.4$ with an observed flattening beyond $z\sim$ 2 with increased stellar mass. Comparing to a range of simulations we find the analytical scaling relation approaches, that invoke an equilibrium model, a good fit to our data, suggesting that a continual smooth accretion regulated by continual outflows may be a key driver in the overall growth of SFGs.Comment: 19 pages, 18 figures, accepted for publication in MNRA

Reliability modelling of dispensing processes in community pharmacy

Studies of error rates in community pharmacies have reported error rates of between 0.014% and 3.3% per item dispensed. This suggests up to 36 million items per year may contain errors in England. In addition, literature shows that patient satisfaction with services is directly related to waiting times. There is a need for a method to model pharmacy efficiency balancing safety and waiting times, ensuring that the reliability of the dispensing process is not compromised. In this paper a Coloured Petri Net (CPN) approach is proposed for analysing reliability and efficiency of community pharmacy. A pharmacy team work to complete dispensing and non-dispensing tasks, where non-dispensing tasks require staff to be temporarily removed from the dispensing process. The proposed approach is useful to investigate what affects the error rates and long waiting times, and provides modelling-based evidence to decision makers, looking to optimise staffing levels and improve the reliability of dispensing

The Experience of using Facebook as an Educational Tool

Social Networking Sites (SNS) such as Facebook are widely used by student populations and are increasingly used by the population generally. Researchers have considered the benefits of using SNS for educational purposes. This qualitative study involved interviews with seven academic members of staff at one UK university who currently use Facebook in their teaching. The study provides a unique insight into the views of teaching staff who use Facebook in their classroom, gaining an understanding of their experience and views of using SNS as part of their teaching

The Stripe 82 1-2 GHz Very Large Array Snapshot Survey: Multiwavelength Counterparts

We have combined spectrosopic and photometric data from the Sloan Digital Sky Survey (SDSS) with $1.4$ GHz radio observations, conducted as part of the Stripe 82 $1-2$ GHz Snapshot Survey using the Karl G. Jansky Very Large Array (VLA), which covers $\sim100$ sq degrees, to a flux limit of 88 $\mu$Jy rms. Cross-matching the $11\,768$ radio source components with optical data via visual inspection results in a final sample of $4\,795$ cross-matched objects, of which $1\,996$ have spectroscopic redshifts and $2\,799$ objects have photometric redshifts. Three previously undiscovered Giant Radio Galaxies (GRGs) were found during the cross-matching process, which would have been missed using automated techniques. For the objects with spectroscopy we separate radio-loud Active Galactic Nuclei (AGN) and star-forming galaxies (SFGs) using three diagnostics and then further divide our radio-loud AGN into the HERG and LERG populations. A control matched sample of HERGs and LERGs, matched on stellar mass, redshift and radio luminosity, reveals that the host galaxies of LERGs are redder and more concentrated than HERGs. By combining with near-infrared data, we demonstrate that LERGs also follow a tight $K-z$ relationship. These results imply the LERG population are hosted by population of massive, passively evolving early-type galaxies. We go on to show that HERGs, LERGs, QSOs and star-forming galaxies in our sample all reside in different regions of a WISE colour-colour diagram. This cross-matched sample bridges the gap between previous `wide but shallow' and `deep but narrow' samples and will be useful for a number of future investigations.Comment: 17 pages, 19 figures. Resubmitted to MNRAS after the initial comment

Reliability and efficiency evaluation of a community pharmacy dispensing process using a coloured Petri-net approach

© 2018 It has been estimated that European customers visit community pharmacies to access essential primary healthcare around 46 million times every day. Studies of dispensing error rates in community pharmacies have reported error rates of between 0.08% and 3.3% per item dispensed. While severe cases of dispensing inaccuracies often garner a high level of media coverage, less significant errors are also causing inefficiencies in primary healthcare delivery. If a variety of dispensing protocols and their consequences could be analysed using a modelling tool, the results would form the evidence for decisions on best practice guidelines in order to improve patient safety and pharmacy efficiency. This paper presents a Coloured Petri Net (CPN) modelling technique for analysing the reliability and efficiency of a community pharmacy dispensing process. The proposed approach is a novel method for considering reliability and efficiency in a single simulation based model. The CPN model represents how a team of practitioners work together to complete a set of tasks carried out in community pharmacies. It describes a close-to-reality dispensing process, which evaluates pharmacy performance over a number of key performance indicators of process reliability and efficiency, and records how staff distribute their time between tasks. Where possible, results are validated against published studies of community pharmacies

Feature Guided Training and Rotational Standardisation for the Morphological Classification of Radio Galaxies

State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the incoming data, which in turn led to the development of automated approaches for data processing tasks, such as morphological classification. Deep learning plays a crucial role in this automation process and it has been shown that convolutional neural networks (CNNs) can deliver good performance in the morphological classification of radio galaxies. This paper investigates two adaptations to the application of these CNNs for radio galaxy classification. The first adaptation consists of using principal component analysis (PCA) during preprocessing to align the galaxies' principal components with the axes of the coordinate system, which will normalize the orientation of the galaxies. This adaptation led to a significant improvement in the classification accuracy of the CNNs and decreased the average time required to train the models. The second adaptation consists of guiding the CNN to look for specific features within the samples in an attempt to utilize domain knowledge to improve the training process. It was found that this adaptation generally leads to a stabler training process and in certain instances reduced overfitting within the network, as well as the number of epochs required for training.Comment: 20 pages, 17 figures, this is a pre-copyedited, author-produced PDF of an article accepted for publication in the Monthly Notices of the Royal Astronomical Societ