Heather J. Ruskin

Comparison of evolutionary algorithms in gene regulatory network model inference

BackgroundThe evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.ResultsThis paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.ConclusionsPresented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.

With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter’s disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.

Modeling traffic flow at a single-lane urban roundabout

Abstract In this paper, we propose a new model to study traffic flow at a single-lane urban roundabout, using a multi-state cellular automata (CA) ring under the offside-priority rule (by which a vehicle entering gives way to one already on the roundabout). Each vehicle entering the roundabout is randomly characterized by a predetermined exit with specified probability. Driver behavior at the roundabout entrance is randomly grouped into four categories based on space required to enter the roundabout. Three aspects of roundabout performance in particular have been studied. The first looks at overall throughput (the number of vehicles that navigate the roundabout in a given time). This is considered for different geometries, turning and arrival rates (vehicles arrive at random with a Poisson distribution, with parameter λ ⩽0.5 in general for free flow). The second investigates changes in queue length, delay time and vehicle density (ratio of the number vehicles to the number of cells) for an individual road. The third considers the impact of driver choices on throughput and operation of the roundabout. We find that throughput is influenced by the topology of the roundabout and turning rates, but only incidentally by size. Throughput reaches a maximum for critical arrival rate on one or more roads. Driver behavior has considerable impact on overall performance, with rapid congestion resulting from reckless choices. Vehicles drive on the left in Ireland, but rules are generally applicable.

Modeling Traffic Flow at an Urban Unsignalized Intersection

This paper proposes a new way to study traffic flow at an urban unsignalised intersection, through detailed space considerations, using cellular automata (CA). Heterogeneity and inconsistency are simulated by incorporation of different categories of driver behaviour and reassignment of categories with given probabilities at each time step. The method is able to reproduce many features of urban traffic, for which gap-acceptance models are less appropriate. Capacities of the minor-stream in a TWSC intersection are found to depend on flow rates of major-streams, also changes with flow rate ratio (FRR= flow rate of near lane: flow rate of far lane). Hence flow rates corresponding to each stream must be distinguished. The relationship between the performance of intersections and other traffic flow parameters is also considered. Vehicle movements in this paper relate to left-side driving, such as found in UK/Ireland. However, rules are generally applicable.

Cross-correlation dynamics in financial time series

The dynamics of the equal-time cross-correlation matrix of multivariate financial time series is explored by examination of the eigenvalue spectrum over sliding time windows. Empirical results for the S&P 500 and the Dow Jones Euro Stoxx 50 indices reveal that the dynamics of the small eigenvalues of the cross-correlation matrix, over these time windows, oppose those of the largest eigenvalue. This behaviour is shown to be independent of the size of the time window and the number of stocks examined. A basic one-factor model is then proposed, which captures the main dynamical features of the eigenvalue spectrum of the empirical data. Through the addition of perturbations to the one-factor model, (leading to a ‘market plus sectors’ model), additional sectoral features are added, resulting in an Inverse Participation Ratio comparable to that found for empirical data. By partitioning the eigenvalue time series, we then show that negative index returns, (drawdowns), are associated with periods where the largest eigenvalue is greatest, while positive index returns, (drawups), are associated with periods where the largest eigenvalue is smallest. The study of correlation dynamics provides some insight on the collective behaviour of traders with varying strategies.

Modelling Traffic Flow At Multi-Lane Urban Roundabouts

This paper proposes Multi-stream Minimum Acceptable Space (MMAS) Cellular Automata (CA) models to study unsignalised multi-lane (two- or three-lane) urban roundabouts. Through detailed space considerations, using Cellular Automata (CA) and the Multi-stream Minimum Acceptable Space method, heterogeneity and inconsistency of driver behavior and interactions in cross traffic at entrances of roundabouts are simulated by incorporation of four different categories of driver behavior (i.e., conservative, moderate, urgent and radical), together with reassignment of categories with given probabilities at each time step. The method is able to reproduce many features of urban traffic, for which gap-acceptance models are not robust. Multi-lane roundabout models, in particular for two-lane roundabouts, are developed with different vehicle lane-allocation patterns. Various properties of multi-lane roundabout operations have been explored including throughput, turning rates, critical arrival rates and congestion. The operations of two- and three-lane roundabouts are compared in terms of throughputs. Vehicle movements in this paper relate to left-side driving, such as found in Ireland, New Zealand and the UK. However, results are generally applicable to the countries where the give-way rule is applied.

Random matrix theory and fund of funds portfolio optimisation

The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a Fund of Hedge Funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which induces noise. In this paper random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using hedge fund returns data. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to distinct groups of strategies that are applied by hedge fund managers. The Inverse Participation ratio is used to quantify the number of components that participate in each eigenvector. Finally, the correlation matrix is cleaned by separating the noisy part from the non-noisy part of C. This technique is found to greatly reduce the difference between the predicted and realised risk of a portfolio, leading to an improved risk profile for a fund of hedge funds.

Cross-platform microarray data normalisation for regulatory network inference.

Background Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences. Methods We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets. Conclusions Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.

Probabilistic models for drug dissolution. Part 1. Review of Monte Carlo and stochastic cellular automata approaches

Abstract Throughout the last decades, Monte Carlo (MC) techniques have been used in simulating various complex systems. In this paper, we investigate how MC-based methods are used in the field of Drug Delivery, indicating what aspects of the complex problems of drug dissolution and design can benefit from this particular approach. After introducing the area of modelling drug dissolution, with its different features and needs, we report and examine the existing Direct MC and Stochastic Cellular Automata modelling efforts used to simulate dissolution of pharmaceutical compacts or related phenomena. In Part 2, we enlarge on a description of our work on Direct MC, for the particular case of simulating a binary system consisting of poorly soluble drug dispersed in a matrix of highly-soluble acid excipient.

Interrelationships among international stock market indices: Europe, Asia and the Americas

In this paper, we investigate the price interdependence between seven international stock markets, namely Irish, UK, Portuguese, US, Brazilian, Japanese and Hong Kong, using a new testing method, based on the wavelet transform to reconstruct the data series, as suggested by Lee [11]. We find evidence of intra-European (Irish, UK and Portuguese) market co-movements with the US market also weakly influencing the Irish market. We also find co-movement between the US and Brazilian markets and similar intra-Asian co-movements (Japanese and Hong Kong). Finally, we conclude that the circle of impact is that of the European markets (Irish, UK and Portuguese) on both American markets (US and Brazilian), with these in turn impacting on the Asian markets (Japanese and Hong Kong) which in turn influence the European markets. In summary, we find evidence for intra-continental relationships and an increase in importance of international spillover effects since the mid 1990s, while the importance of historical transmissions has decreased since the beginning of this century.