Computational Modelling Group

A novel approach to analysing fixed points in complex systems

James Dyke (Investigator), Iain Weaver

This work aims to contribute to our understanding of the relationship between complexity and stability. By describing an abstract coupled life-environment model, we are able to employ novel analytical, and computational techniques to shed light on the properties of such a system.

Associative learning in ecosystems: Network level adaptation as an emergent propery of local selection

Richard Watson, James Dyke (Investigators), Daniel Power

Ecosystems may exhibit collective adaptive properties that arise from natural selection operating on their component species. These properties include the ability of the ecosystem to return to specific configurations of species, in a manner highly analogous to mechanisms of associative learning in neural networks.

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.

Cellular Automata Modelling of Membrane Formation and Protocell Evolution

Seth Bullock (Investigator), Stuart Bartlett

We simulated the meso-level behaviour of lipid-like particles in a range of chemical and physical environments. Self-organised protocellular structures can be shown to emerge spontaneously in systems with random, homogeneous initial conditions. Introducing an additional 'toxic' particle species and an associated set of synthesis reactions produced a new set of ecological behaviours compared to the original model of Ono and Ikegami.

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.

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.

Differences between sexual genetic algorithms and a compositional cooperative co-evolutionary algorithm

Richard Watson (Investigator), Daniel Power

Different algorithms are representative of different genetic processes. This work explores how algorithms representing sexual recombination can solve certain problems that hill climbers cannot.

Do the adaptive dynamics of host-parasite systems catalyse or constrain sympatric speciation?

Richard Watson (Investigator), Daniel Power

Coevolutionary dynamics affect both parties evolutionary trajectories. When might these affect speciation? This project uses a simulation model to explore the issue.

Identification of novel Crustacean Pathogen Receptor Proteins

Richard Edwards, Chris Hauton, Timothy Elliott (Investigators), Oyindamola Lawal, Lloyd Mushambadzi

We are mining EST libraries (sequence fragments of expressed genes) for novel proteins that might play a role in the immune response of crustaceans.

Immunotherapy Research: Modelling MHC Class I Complex Assembly

Timothy Elliott, Jorn Werner (Investigators), Alistair Bailey

This project uses mathematical modelling and simulation to investigate mechanisms by which our cells process and present biological information that is used by our immune system to distinguish between healthy and diseased cells.

Impact of reciprocal feedbacks between evolution and ecology

Richard Watson, Patrick Doncaster, James Dyke (Investigators), Daniel Power

How do organism's activities affect their evolutionary trajectories? This project uses simulation techniques to evaluate the effects of this feedback.

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.

Mass Spec identification of proteins utilising EST libraries

Richard Edwards, Maria Debora Iglesias-Rodriguez (Investigators), Bethan Jones

Expressed Sequence Tag (EST) data presents a particular challenge for the identification of proteins using mass spectrometry (MS): it is often redundant (multiple copies of the same gene), consists primarily of short fragments of coding sequence, contains many sequencing errors and is generally poorly annotated. We are developing computational pipelines to maximise robust protein identifications from EST data despite these challenges.

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.

Perceived Attractiveness as a Factor Affecting Condomless Sex

Anastasia Eleftheriou, Seth Bullock

The objectives of this project are to better understand the relationship between perceived attractiveness and condom use intentions and to gain insight into the relationship between perceived attractiveness and potential sexual risk behaviours.

Selection pressure for language and theory-of-mind in monkeys

Jason Noble (Investigator)

To what extent are the alarm calls of putty-nosed monkeys likely to be a good model for human language evolution? Simulation is used to classify evolutionary trajectories as either plausible or implausible, and to put lower bounds on the cognitive complexity required to perform particular behaviours.

The Origins of Communication Revisited

Jason Noble (Investigator), Jordi Arranz

Quinn (2001) sought to demonstrate that communication be- tween simulated agents could be evolved without pre-defined communication channels. Quinn’s work was exciting because it showed the potential for ALife models to look at the real origin of communication; however, the work has never been replicated. In order to test the generality of Quinn’s result we use a similar task but a completely different agent architecture. We find that qualitatively similar behaviours emerge, but it is not clear whether they are genuinely communicative. We extend Quinn’s work by adding perceptual noise and internal state to the agents in order to promote ritualization of the nascent signal. Results were inconclusive; philosophical implications are discussed.

The Social-cognitive Niche: An Exploration of the Co-evolutionary Relationship between Human Mind and language, with a Particular Focus of the Self-organisational properties of the Emergence of Symbolic Representation.

Jason Noble, Glyn Hicks (Investigators), Lewys Brace

This work explored the relationship between the origin and subsequent evolution of the human mind and language; a relationship that is believed to be symbiotic in nature. This piece aimed to achieve two objectives. Firstly, it set out a theoretical framework, using the principles of complexity theory and self-organisation, which attempts to explain this relationship from a holistic perspective.

Secondly, it presented an agent-based model of a vervet monkey social group, which sought to investigate the variables that were perceived to underpin the emergence of symbolic representation within a population of language users.

The belief here was that, by understanding the influence of these variables, one would be able to better understand the genesis of the aforementioned relationship.

µ-VIS Computed Tomography Centre

Ian Sinclair, Richard Boardman, Dmitry Grinev, Philipp Thurner, Simon Cox, Jeremy Frey, Mark Spearing, Kenji Takeda (Investigators)

A dedicated centre for computed tomography (CT) at Southampton, providing complete support for 3D imaging science, serving Engineering, Biomedical, Environmental and Archaeological Sciences. The centre encompasses five complementary scanning systems supporting resolutions down to 200nm and imaging volumes in excess of one metre: from a matchstick to a tree trunk, from an ant's wing to a gas turbine blade.