Computational Modelling Group

A novel method for monitoring air pollution from satellites at very high resolution

Joanna Nield, Jason Noble, Edward Milton (Investigators), Robin Wilson

Developing methods to monitor the clarity of the atmosphere from satellites at 100,000 times the resolution of previous methods. This can then be used to monitor air pollution, correct satellite images and provide data for climate studies. Simulation is used to model the effects of atmospheric pollution on light passing through the atmosphere, and to test the method under 'synthetic atmospheres'.

An Evolutionary Economic Approach to the Household?

Jason Hilton

The household is a fundamental societal unit. In a huge array of contexts, our understanding of social behaviour relies on an interpretation of how decision are taken at the household level.This work aims to model individual decision-making and interactions between individuals explicitly within the framework of agent-based modelling, following the work of Potts (2000). Potts describes how economic problems can better be dealt with by considering how agents with incomplete, evolving preferences in the form of decision rules interact on a network, and how they cooperate and form ties to produce combinatorial technologies. Following the work of Gary Becker, he then considers how this ostensibly economic framework might hypothetically describe partnership search and household formation and dissolution.

Application of RNA-Seq for gene fusion identification in blood cancers

William Tapper (Investigator), Marcin Knut

Gene fusions are often the cause of different blood cancers. As such, accurate identification of them provides information on the underlying cause of a cancer, ensuring appropriate choice of treatment. However, due to shortcomings of the currently applied methods for gene fusion identification, some of them escape undetected. We are employing RNA-Seq, a cutting-edge method for sequencing RNA, the messenger of genetic information, to investigate gene fusions.

Automated Algorithmic Trading with Intelligent Execution

Frank McGroarty, Enrico Gerding (Investigators), Ash Booth

In this project, we introduce the first fully automated trading system for real-world stock trading that uses time-adaptive execution algorithm to minimise market impact while increasing profitability com- pared to benchmark strategies.

Automated selection of suitable atmospheric calibration sites for satellite imagery

Robin Wilson, Edward Milton (Investigators)

Ground calibration targets (GCTs) play a vital role in atmospheric correction of satellite sensor data in the optical region, but selecting suitable targets is a subjective and time- consuming task. This project is developing methods to automatically select suitable GCTs, using a combination of remotely sensed multispectral and topographic data.

Automated Trading with Performance Weighted Random Forests and Seasonality

Frank McGroarty, Enrico Gerding (Investigators), Ash Booth

This project proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy.

Bayesian Agents as Models for the Disclosure Behaviour of Pregnant Drinkers

Seth Bullock, Jakub Bijak (Investigators), Jonathan Gray

Examining the feasibility of signalling games, played by Bayesian decision theoretic agents as a model for the disclosure of drinking behaviour by pregnant women to their midwives.

Benchmarking the GOPHER orthologue prediction algorithm.

Richard Edwards, Shaun Maguire

Generation of Orthologous Proteins from High-throughput Evolutionary Relationships (GOPHER) is an orthologue prediction algorithm. This experiment aims to benchmark this algorithm.

BRECcIA - Building REsearch Capacity for sustainable water and food security In sub-saharan Africa

The BRECcIA project is aimed at developing research and researchers to understand water and food security challenges in sub-Saharan Africa

Care Life Cycle

Seth Bullock, Sally Brailsford, Jason Noble, Jakub Bijak (Investigators), Elisabeth zu-Erbach-Schoenberg, Jason Hilton, Jonathan Gray

This research programme brings together teams of researchers from social sciences, management science and complexity science to develop a suite of models representing the socio-economic and demographic processes and organisations implicated in the UK’s health and social care provision. Integral to the project is working with our partners in the public sector and communicating the results of these models to policymakers allowing them to effectively plan for the future.

Census 2022: Transforming Small Area Socio-Economic Indicators through Big Data

Patrick James, Ben Anderson (Investigators)

One of only 20 to be funded under the ESRC’s new ‘Transformative Social Science’ programme, this project will explore the feasibility of estimating small area (neighbourhood) census-like statistics from transactional ‘big data’ including large scale fine grained temporal energy monitoring data held by the Energy & Climate Change Division (FEE).

Centre for Doctoral Training in Next Generation Computational Modelling

Hans Fangohr, Ian Hawke, Peter Horak (Investigators), Susanne Ufermann Fangohr, Thorsten Wittemeier, Kieran Selvon, Alvaro Perez-Diaz, David Lusher, Ashley Setter, Emanuele Zappia, Hossam Ragheb, Ryan Pepper, Stephen Gow, Jan Kamenik, Paul Chambers, Robert Entwistle, Rory Brown, Joshua Greenhalgh, James Harrison, Jonathon Waters, Ioannis Begleris, Craig Rafter

The £10million Centre for Doctoral Training was launched in November 2013 and is jointly funded by EPSRC, the University of Southampton, and its partners.

The NGCM brings together world-class simulation modelling research activities from across the University of Southampton and hosts a 4-year doctoral training programme that is the first of its kind in the UK.

Characterisation of the Genomic Landscape in Splenic Marginal Zone Lymphoma

Sarah Ennis, Jane Gibson, Jon Strefford (Investigators), Carolina Jaramillo Oquendo, Helen Parker

This project aims to expand the catalogue of mutated genes in splenic marginal zone lymphoma (SMZL) and construct a detailed characterisation of the genetic landscape of this disease. Using next generation sequencing, we aim to identify somatic mutations in over 100 samples, and enrich clinical data with this information to improve patient treatment and prognosis.

Development and application of powerful methods for identifying selective sweeps

Andrew Collins, Reuben Pengelly, Timothy Sluckin, Sarah Ennis (Investigators), Clare Horscroft

This project is about detecting regions of the genome which have experienced selective pressure. To achieve this, mathematical models will be developed and applied to human genomic data sets, as well as to those of other species.

Effects of Sample Contamination on Alternate Allele Frequency

Jane Gibson (Investigator), Roshan Sood

Accurate calling of genetic variants is reliant on the purity of samples, contamination will reduce the accuracy of results. Currently there are few programs able to identify contamination in samples, potentially misinforming a researcher or clinician. To better understand the changes caused by sample contamination in
silico simulations were performed where a known percentage of DNA sequence reads from a contaminating
file were added. Understanding the changes will assist the development of a new method and program to
detect sample contamination.

Evolving Resilience to Leverage Based Crashes

Frank McGroarty, Enrico Gerding (Investigators), Ash Booth

This project analyses the maturation, initiation and evolution of crashes in the financial markets using an agent-based model.

Identification of Gene-Modules Associated to a Predisposition of Post-Traumatic Stress Disorder

Christopher Woelk (Investigator), Michael Breen

The predisposing genetic factors associated to Post-Traumatic Stress Disorder (PTSD) are altogether unknown. Since not all trauma-survivors later develop the PTSD, it has been hypothesized that transcript differentiation prior–to-trauma exposure could be associated to the risk and resilience of PTSD. We apply a systems-level approach to investigate changes in transcript abundance (gene expression profiles) in whole blood of U.S. Marines sampled prior-to-deployment to the battlefield and followed through-out a seven month deployment to obtain disorder related outcomes.

Identifying variants in next generation sequencing data of 61 paediatric Inflammatory Bowel Disease patients

Sarah Ennis (Investigator), Gaia Andreoletti

This study aims to characterise the mutations of genes known to predispose Inflammatory bowel disease in 61 paediatric patients using next generation sequencing analysis. Our aim is to identify the relative impact of known genes in individual case presentations of disease and correlate matches with clinical manifestation.

Integrated in silico prediction of protein-protein interaction motifs

Richard Edwards (Investigator), Nicolas Palopoli, Kieren Lythgow

Many vital protein-protein interactions are mediated by Short Linear Motifs (SLiMs) which are short proteins typically 5-15 amino acids long containing only a few positions crucial to function. This project integrates a number of leading computational techniques to predict novel SLiMs and add crucial detail to protein-protein interaction networks.

Interactome-wide prediction of short linear protein interaction motifs in humans

Richard Edwards (Investigator)

Short Linear Motifs (SLiMs) are important in many protein-protein interactions. In previous work, we have developed a computational tool, SLiMFinder, which places the interpretation of evidence for motifs within a statistical framework with high specificity, and subsequently enhanced sensitivity through application of conservation-based sequence masking. We are now applying these tools to a comprehensive set of human protein-protein interactions in order to predict novel human SLiMs in silico.

Is the decline in East African lesser flamingo population a natural concequence of soda ake dynamics?

Seth Bullock

An interdisciplinary approach using palaeoenvironmental data analysis and a modelling is being used investigate the dramatic fluctuations in conditions in the East African Rift Valley soda lakes, and how these changes may be impacting the lesser flamingo population.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Mahesan Niranjan, Andrew Collins, Reuben Pengelly (Investigators)

This iPhD project uses a Gaussian Bayesian Networks framework through Machine learning methods to predict which genes are involved in the development of different diseases.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Andrew Collins, Mahesan Niranjan, Reuben Pengelly (Investigators), Alejandra Vergara Lope

This iPhD project uses a Gaussian Bayesian Networks approaches framework through machine learning approach to predict which genes are involved in the development of different diseases.

Mathematical tools for analysis of genome function, linkage disequilibrium structure and disease gene prediction

Mahesan Niranjan, Andrew Collins, Reuben Pengelly (Investigators)

This PhD project uses a Monte Carlo molecular simulation processes approach to predict which genes are involved in the development of different diseases.

Modelling Macro-Nutrient Release & Fate Resulting from Sediment Resuspension in Shelf Seas

Chris Wood

This study involves adapting a previously-published model to take into account the effect resuspension events (both natural and anthropogenic) may have on nutrient dynamics at the sediment-water interface, and hence produce better estimates for the total nutrient budgets for shelf seas.

Modelling the Easterlin Effect

Jason Hilton

This project is an attempt to formalise the Easterlin hypothesis in a simulation model and test its plausibility.
The Easterlin Hypothesis, developed by economist Richard Easterlin, purports to describe a mechanism whereby the fertility decisions of a particular cohort of individuals are linked to population level conditions that held sway when they were born The empirical support for the theory is quite strong for the certain periods in the history of the United States, but elsewhere it is circumstantial and patchy. A simulation model may allows us to test under what conditions it may hold and not hold, and also might help inform more general theory building.

Network Analysis of Roman Transport Routes in the Imperial Roman Mediterranean

David Potts

This research is designed to explore the nature of the relationships between Portus, Rome, and other selected ports in the Mediterranean and to establish patterns and the changing nature of trading networks derived from the distribution of known Roman artefacts.

Origins of Evolvability

Richard Watson, Markus Brede (Investigators), William Hurndall

This project examined the putative evolvability of a Lipid World model of fissioning micelles. It was demonstrated that the model lacked evlovability due to poor heritability. Explicit structure for micelles was introduced along with a spatially localised form of catalysis which increased the strength of selection as coupling between potential chemical units of heredity were reduced.

Porous Media and Hydrothermal Circulation in Weakened Ocean Crust

Formation of oceanic crust is an interplay between magma and the cooling hydrothermal system above that its own heat drives. To understand this system we must understand where and how water circulates through the crust.

Ocean crust is riddled with faults and other permeable pathways along which water preferentially flows. We seek to use basic numerical models of circulation in porous media to understand how much of an influence on crust formation these anomalous features have, compared to the bulk, unfractured crust.

Predicting Available Energy in Energy Harvesting Wireless Sensor Networks

Geoff Merrett (Investigator), Davide Zilli

Is it possible to predict how much energy a sun-light or wind powered wireless sensor node can harvest and tune its sensing pattern accordingly?

Pushing the Envelope of Planetary Formation and Evolution Simulations

Peter Bartram

A full understanding of the formation and the early evolution of the Solar System and extrasolar planetary systems ranks among natural science's grand challenges, and at present, even the dominant processes responsible for generating the observed planetary architecture remain elusive.

Reforging the Wedding Ring: Exploring a Semi-Artificial Model of Population for the United Kingdom with Gaussian process emulators

Jason Hilton

Note: Jakub Bijak from Social Science is the lead author on this project, which is forthcoming in Demographic Research (http://www.demographic-research.org/)
Co-Authors: Eric Silverman, Viet Dung Cao

We extend the „Wedding Ring? agent-based model of marriage formation to include some empirical information on the natural population change for the United Kingdom together with behavioural explanations that drive the observed nuptiality trends.

Replication of Sayama Group Decision Model

Jason Noble (Investigator), Jonathan Gray

Replication of a model of group decision making from Sayama et al. (2011), with a revised methodology that alters a conclusion from the original paper.

SAVE: Solent Achieving Value through Efficiency

Patrick James, Ben Anderson (Investigators), Luke Blunden

Analysis of 15 minute electricity consumption and 10 second instantaneous power data from 4,000+ households in the Solent region collected over 3 years of a randomised control trial study.

Scalability of Energy Efficient Routing Algorithms in Wireless Sensor Networks

Geoff Merrett (Investigator), Davide Zilli

This project compares two broad classes of routing algorithms for Wireless Sensor Networks, message flooding and single path, by means of a simulation model. In particular, we want to understand how the two scale in terms of energy efficiency on large networks of sensors.

Simulating Human Expansion in the Early Pleistocene

Seth Bullock, Fraser Sturt (Investigators), Iza Romanowska

Using Agent-based modelling to investigate the first human dispersal almost 2 million years ago.

Software Sustainability Institute

Simon Hettrick (Investigator)

A national facility for cultivating world-class research through software

Software helps researchers to enhance their research, and improve the speed and accuracy of their results. The Software Sustainability Institute can help you introduce software into your research or improve the software you already use.

The Institute is based at the universities of Edinburgh, Manchester, Oxford and Southampton, and draws on a team of experts with a breadth of experience in software development, project and programme management, research facilitation, publicity and community engagement.

We help people build better software, and we work with researchers, developers, funders and infrastructure providers to identify key issues and best practice in scientific software.

Spatial variability of the atmosphere in southern England

Joanna Nield, Jason Noble, Edward Milton (Investigators), Robin Wilson

No-one really knows how variable key atmospheric parameters such as Aerosol Optical Thickness and Water Vapour content are over relatively small areas. This study aims to find out!

Structured low-rank approximation

Ivan Markovsky

Today's state-of-the-art methods for data processing are model based. We propose a fundamentally new approach that does not depend on an explicit model representation and can be used for model-free data processing. From a theoretical point of view, the prime advantage of the newly proposed paradigm is conceptual unification of existing methods. From a practical point of view, the proposed paradigm opens new possibilities for development of computational methods for data processing.

Studying microevolution in clinical isolates of Neisseria lactamica

Robert Read (Investigator), Jay Laver, Anish Pandey

We intranasally infected and successfully colonised six volunteers with Neisseria lactamica, a commensal species genetically similar to Neisseria meningitidis. A bioinformatics approach was then used to understand the microevolution of this bacterium and its adaptations to the nasopharynx.

Tag based transcriptome analysis of gene expression in a promising green algae

Richard Edwards (Investigator), Andreas Johansson

We use SuperSAGE in combination with next-generation sequencing to compare differences in gene expression between selected mutants and the wild type of a green algae. The data in the form of millions of 26 bp tags representing short stretches of expressed genes, will be analysed to find patterns of variation in gene expression under different conditions.

The application and critical assessment of protein-ligand binding affinities

Jonathan Essex (Investigator), Ioannis Haldoupis

A method that can accurately predict the binding affinity of small molecules to a protein target would be imperative to pharmaceutical development due to the time and resources that could be saved. A head-to-head comparison of such methodology, ranging from approximate methods to more rigorous methods, is performed in order to assess their accuracy and utility across a range of targets.

The Ca-geospeedometer - A tool for investigating the processes that generate oceanic crust

We quantify the errors inherent in the current methods of geospeedometry, which lets one obtain the cooling rate of a rock, using mineral trace element chemistry. Calcium-in-Olivine geospeedometry is useful for deep ocean crust.

We want to use this proxy to figure out how ocean crust actually accretes, it provides key evidence as to how magma chambers and hydrothermal systems interact to produce new lithosphere. But to do so we first have to determine how reliable the method is and therefore how much can be inferred from results.

Uncertainty quantification and propagation through complex chains of computational models

Dave Woods (Investigator), Stephen Gow

This project will explore how predictions can be made and assessed through complex chains of computer models.

Validation of GPS-derived water vapour estimates

Joanna Nield, Jason Noble, Edward Milton (Investigators), Robin Wilson

Measurements from GPS base stations can be processed to provide estimates of the water vapour content in the atmosphere. These are lots of these base stations across the world and they take measurements very frequently, making them perfect data sources for scientific use. However, we need to understand their accuracy - and this project aims to do this.