Computational Modelling Group

A composite likelihood approach to genome-wide data analyses.

Andrew Collins (Investigator), Jane Gibson, Ioannis Politopoulos

We describe composite likelihood-based analysis of a genome-wide breast cancer case-control sample by determining genome regions of fixed size on a linkage disequilibrium map which delimit comparable levels of linkage disequilibrium. Analysis of findings suggests further validation in more samples from other cohorts as well as the exploitation of novel computationally-intensive methods such as next-generation sequencing.

A habitat suitability model for predicting coral reef distributions in the Galápagos Islands

Terence Dawson (Investigator)

As part of a wider project developing a conservation strategy for the marine environment of the Galapagos Islands, this research used multi-variate modelling techniques to develop a habitat suitability prediction model for coral reefs.

A Mathematical Analysis of the Driving Force of Perivascular Drainage in the Brain

Giles Richardson, Roxana-Octavia Carare (Investigators), Alexandra Diem

The observation that solute drainage in the brain occurs in the reverse direction of the blood flow has for a long time been puzzling for researchers. We developed a simple analytical model that can explain this reverse drainage of solutes and has potential implications for the development of treatment for Alzheimer's Disease.

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.

A spatially-explicit agent-based model of jaguar population dynamics

Jason Noble, Patrick Doncaster (Investigators), Angela Watkins

A single species spatially-explicit agent-based model has been developed that illustrates the role of simulation modelling, integrated with an adapted least-cost modelling approach and real-world geographical data, in exploring jaguar population dynamics.

Agent-based simulations of jaguar movements through conservation corridors

Jason Noble, Patrick Doncaster (Investigators), Angela Watkins

We present an agent-based model of jaguars (Panthera onca),
scaled for fragmented habitat in Belize where proposals already exist for creating a jaguar corridor. We use a least cost approach to simulate movement paths through alternative possible landscapes.

Amorphous Computation, Random Graphs and Complex Biological Networks

Seth Bullock (Investigator)

This interdisciplinary research collaboration arose within the Simple Models of Complex Networks research cluster funded by the EPSRC www.epsrca.ac.uk through the Novel Computation Initiative. Here, leading groups from the Universities of Leeds, Sheffield, Nottingham, Southampton, Royal Holloway and King’s College and industrial partners BT are brought together for the first time to develop novel amorphous computation methods based on the theory of random graphs.

Antimicrobial Peptide and E. coli Membrane Interactions

Syma Khalid (Investigator), Thomas Piggot, Nils Berglund

Antimicrobial peptides (AMPs) are known to disrupt the membranes of bacterial cells such as E. coli. I work on investigating the nature of these interactions using molecular dynamics (MD) simulations.

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.

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.

Bayesian Agent-Based Population Studies: Transforming Simulation Models of Human Migration

This is a cutting-edge project in demographic methodology, funded by the European Research Council (ERC), through the Consolidator Grant ERC-CoG-2016-725232. Its aim is to develop a ground-breaking simulation model of international migration, based on a population of intelligent, cognitive agents, their social networks and institutions, all interacting with one another. The project also aims to transform the study of migration – one of the most uncertain population processes and a top-priority policy area – by offering a step change in the way it can be understood, predicted, and managed.

Benchmarking methods of identifying intrinsically unstructured proteins for de novo prediction of Short Linear Motifs

Richard Edwards (Investigator), William Anderson

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.

Bioclimatic Architecture

Seth Bullock (Investigator), Nicholas Hill

This was a review report on bioclimatic architecture and how such architecture may be designed by agent-based models inspired by the building behaviour of insects.

Bioinformatic identification and physiological analysis of ethanol-related genes in C. elegans

Richard Edwards, Vincent O'Connor, Lindy Holden-Dye (Investigators), Ben Ient

Investigating the broad molecular, cellular and systems level impacts of acute and chronic ethanol in the nematode, Caenorhabditis elegans, as a model.

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

Can we calculate the pKa of new drugs, based on their structure alone?

Chris-Kriton Skylaris (Investigator), Chris Pittock, Jacek Dziedzic

The pKa of an active compound in a pharmaceutical drug affects how it is absorbed and distributed around the human body. While there are various computational methods to predict pKa using only molecular structure data, these tend to be specialised to only one class of drug - we aim to generate a more generalised prediction method using quantum mechanics.

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.

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.

Control and Prediction of the Organic Solid State

Richard Boardman

This project aims to produce a computer technology for the prediction of the crystal structure(s) of an organic molecule, that could be used even prior to the synthesis of the compound.

Such a computational study could be done relatively quickly to predict the dangers and opportunities of the solid phases of a molecule under development. Our project will develop the methods of experimental screening for polymorphs and their characterisation, and hence the combination will provide a major new technology for aiding industrial formulation.

Controlling Ant-Based Construction

Seth Bullock (Investigator), Lenka Pitonakova

This paper investigates dynamics of ant nest building and shows that algorithms capable of generating ant-like structures can also be used to create nests, shapes of which are imposed from outside of the system.

Coronary Artery Stent Design for Challenging Disease

Neil Bressloff (Investigator), Georgios Ragkousis

In this work, a method has been setup to (i) reconstruct diseased patient specific coronary artery segments; (ii) use the new supercomputer to run many simulations of this complex problem and (iii) assess the degree of stent malapposition. The aim now is to devise a stent delivery system that can mitigate this problem

Deep Optimisation

Jamie Caldwell

The project will develop the implementation and application of a new optimisation technique. 'Deep optimisation' combines deep learning techniques in neural networks with distributed optimisation methods to create a dynamically re-scalable optimisation process. This project will develop this technique to better-understand its capabilities and limitations and develop GPU implementations. The protein structure prediction problem will be used as the main test application.

DePuy Technology Partnership

Mark Taylor (Investigator), Adam Briscoe

This initiative concerns the transfer of knowledge between three key institutions (University of Southampton, University of Leeds and University of Hamburg) and DePuy International limited. The project is concerned with the ongoing advancement of technology used in orthopaedic devices.

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.

Development of wide-ranging functionality in ONETEP

Chris-Kriton Skylaris (Investigator), Jacek Dziedzic

ONETEP is at the cutting edge of developments in first principles calculations. However, while the fundamental difficulties of performing accurate first-principles calculations with linear-scaling cost have been solved, only a small core of functionality is currently available in ONETEP which prevents its wide application. In this collaborative project between three Universities, the original developers of ONETEP will lead an ambitious workplan whereby the functionality of the code will be rapidly and significantly enriched.

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.

Dispersion of Small Inertial Particles in Characteristic Atmospheric Boundary Layer Flows

Zheng-Tong Xie (Investigator), Thorsten Wittemeier

This project aims at improving the near-field accuracy of short term predictions of the dispersion of particulate matter in the atmospheric boundary layer. For this purpose a variety of LES and DNS modelling approaches is used.

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.

Dual resolution simulations of lipid membrane systems

Jonathan Essex (Investigator), Kieran Selvon

This project aims to shed light on cell membrane mechanisms which are difficult to probe experimentally, in particular drug permiation across the cell membrane. If one had a full understanding of this mechanism, drugs could be designed to easily cross the membrane, or target particular embedded proteins to improve their efficacy. A reliable and robust computational method to asses a molecules permeability would be invaluable in the field of drug design, we seek to perfect such a method.

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.

Electrostatic embedded energy calculations of proteins, using the ONETEP DFT code

Chris-Kriton Skylaris (Investigator), Stephen Fox, Chris Pittock

Calculating the energy of a biomolecule in solvent, using quantum mechanics (QM) is possible, but extremely challenging, even with linear-scaling QM methods like ONETEP. Using electrostatic embedding, a novel twist on the existing QM/MM method is used to calculate the binding energy of a small ligand to a solvated protein, increasing the accuracy and realism of our general project work.

EuroSat4PhenoChanges. Using MERIS for monitoring phenology in Europe.

Peter Atkinson (Investigator), Victor Rodriguez Galiano

Monitoring vegetation phenology at multiple scales in Europe from the GMES satellite sensor time-series: a special consideration to natura2000 areas

First Principles Simulation of Glycine Adsorption to Amorphous Silica

Chris-Kriton Skylaris (Investigator), Benjamin Lowe

Understanding the molecular interactions between silica and biomolecules is an important in the fields of Bionanotechnology, Biomimetic Material Science and Prebiotic Chemistry. DFT calculations were performed based on a literature study to better understand the interaction between silica and glycine.

Flow and sedimentation processes in submarine meandering channels

Stephen Darby (Investigator)

The overall aim of this project is to generate a step-change in our understanding of the interactions between flow,
morphology & sedimentology within an active submarine channel fed by saline density currents. This central aim will be addressed through a combination of field measurements and innovative numerical modelling of gravity current morphodynamics

Genetic studies to characterise the role of genetic factors in early-onset breast cancer

Andrew Collins (Investigator), Rosanna Upstill-Goddard

Breast cancer is a highly heterogeneous disease, with many distinct subtypes. In the majority of breast cancer cases the causative genetic component is poorly characterised. This study aims to explore both rare and common mutations in early-onset breast cancer patients and the contribution of such variants to disease using a variety of analytic approaches.

How far can we stretch the MARTINI?

Syma Khalid (Investigator), Ric Gillams

To date, coarse-grained lipid models have generally been parameterised to ensure the correct prediction of structural properties of membranes, such as the area per lipid and the bilayer thickness. The work described here explores the extent to which coarse-grained models are able to predict correctly bulk properties of lipids (phase behaviour) as well as the mechanical properties, such as lateral pressure profiles and stored elastic stress in bilayers. Such an evaluation is crucial for understanding the predictive capabilities of coarse-grained models.

Hybrid quantum and classical free energy methods in computational drug optimisation

Jonathan Essex, Chris-Kriton Skylaris (Investigators), Christopher Cave-Ayland

This work is based around the application of thermodynamics and quantum mechanics to the field of computational drug design and optimisation. Through the application of these theories the calculation of the physical properties of drug-like molecules is possible and hence some predictive power for their pharmaceutical activity in vivo can be obtained.

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.

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.

Identification of phage DNA, common insertion sites and their effect on genes within S.pneumoniae

Richard Edwards, Amy Dean

This study seeks to find if there are any common insertion sites across different strains of S.pneumoniae and discover genes that undergo frequent mutation due to phages and if these mutations can be linked to virulence of the strains.

Identifying factors required for DNA methylation using the imprinting control protein ZFP57

Deborah Mackay (Investigator)

Mutation of ZFP57 in humans is associated with widespread loss of DNA methylation at imprinted genes, and clinical features including congenital anomalies and developmental delay (Mackay et al, 08). This indicates that ZFP57 is required for DNA methylation of imprinted genes necessary for normal development.
We propose to identify the DNA sequences targeted by ZFP57, and its protein cofactors. This work will give insight into the biology of imprinting, indicate mechanisms of disease, and identify novel imprinted genes.

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.

Image Based Modelling of Fluid Flow through Lymph Nodes

Tiina Roose, Bharathram Ganapathisubramani, Geraldine Clough (Investigators), Laura Cooper

In this project we are using images of mouse lymph nodes to investigate the fluid transport pathways through it. The images of the nodes are taken using selective plane illumination microscopy, and synchrotron micro computed tomography. The fluid flow is modelled using Darcy's law in COMSOL Multiphysics and the models are run on the Iridis cluster.

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.

Impacts of Climate and Sea-Level Change on Coastal Gullies

Stephen Darby (Investigator), Chris Hackney, Julian Leyland

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Imprinting Disorders Finding Out Why

Karen Temple (Investigator)

We are conducting a research project to determine the cause and clinical impact of widespread imprinting aberrations in human development. We are recruiting patients with possible or definite imprinting disorders (due to methylation loss or gain at an imprinted loci)

including Silver Russell syndrome, Transient Neonatal diabetes, Beckwith Wiedemann syndrome, Angelman syndrome Prader Willi syndrome, UPD 14 syndromes and Pseudohypoparathyroidism.

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.

Integrating least-cost models with agent-based simulations: example hedgehog responses to fragmented landscapes

Jason Noble, Patrick Doncaster (Investigators), Angela Watkins

This study presents a novel analysis of an agent-based model of hedgehog movements integrated with a least-cost model of hedgehog dispersal and validated in landscapes with a varying degree of habitat fragmentation. A comparison of the fitness of individual agents reveals that incorporating a simple rule into
individual agents, to better mimic movement choices by real hedgehogs, dramatically affects the relationship between individual fitness and fragmentation.

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.

Investigation into the Interfacial Physics of Field Effect Biosensors

Nicolas Green, Chris-Kriton Skylaris (Investigators), Benjamin Lowe

This interdisciplinary research aims to improve understanding of Field Effect Transistor Biosensors (Bio-FETs) and to work towards a multiscale model which can be used to better understand and predict device response.

Investigations of Lymphatic Fluid Flow

Tiina Roose, Bharathram Ganapathisubramani, Geraldine Clough (Investigators), Laura Cooper

The lymphatic system performs three main roles returns interstitial fluid back into the blood stream to maintain tissue fluid homeostasis. The aim of this project is to increase our understanding of how the lymph flows through the system by creating three dimensional fluid structure interaction models of the secondary lymphatic valves and image based models of lymph nodes.

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.

Lagrangian modelling of ecosystem dynamics at the Bermuda Atlantic Time-series Study station

Tom Anderson, Seth Bullock (Investigators), Melissa Saeland

Focus in the marine ecosystem modelling community is starting to shift towards the use of Lagrangian, agent-based models as these are believed to produce more realistic results. The basic assumptions behind these models have not been thoroughly tested, and this project aims to undertake a detailed study of Lagrangian marine ecosystem models, before creating one to investigate the dynamics at the Bermuda Atlantic Time-series Study station (BATS).

Lyotropic phase transitions of lipids studied by CG MD simulation and experimental techniques

Syma Khalid (Investigator), Josephine Corsi

A study of the phase behaviour of cationic lipid - DNA complexes such as those used for transfection by coarse grained molecular dynamics simulation. Lipid systems studied include DOPE, DOPE/DNA and DOPE/DOTAP/DNA. Structural parameters and phase behaviour observed computationally have been compared with those gained using Small Angle X-ray Scattering (SAXS) and polarising light microscopy techniques.

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.

Mathematical modelling of plant nutrient uptake

Tiina Roose (Investigator)

In this project I will describe a model of plant water and nutrient uptake and how to translate this model and experimental data from the single root scale to the root branching structure scale.

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.

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.

Measuring biomolecules - improvements to the spectroscopic ruler

Pavlos Lagoudakis, Tom Brown (Investigators), Jan Junis Rindermann, James Richardson

The spectroscopic ruler is a technique to measure the geometry of biomolecules on the nm scale by labeling them with pairs of fluorescent markers and measuring distance dependent non-radiative energy transfer between them. The remaining uncertainty in the application of the technique originates from the unknown orientation between the optical dipole moments of the fluorescent markers, especially when the molecule undergoes thermal fluctuations in physiological conditions. Recently we introduced a simulation based method for the interpretation of the fluorescence decay dynamics of the markers that allows us to retrieve both the average orientation and the extent of directional fluctuations of the involved dipole moments.

Membrane-Protein Interactions: The Outer Membrane of Gram-Negative Bacteria

Syma Khalid (Investigator), Pin-Chia Hsu

The aim of the project is to looking for the interaction sites, which may responsible for turning on/off activity in outer membrane protein with gram-negative bacteria membrane using molecular dynamic (MD) approach.

Metagenomics: Understanding the impacts of environmental change on soil biodiversity

Richard Edwards, Gail Taylor (Investigators), Joseph Jenkins

Drought is expected to increase in prevalence by 2050. Similarly, the use of biochar (a charcoal based soil amendment) has been suggested as a method to sequester carbon and fertilize soils without need of mineral fertilizers, and its use is increasing. We are using next generation DNA sequencing technology and bioinformatics to determine bacterial genetic diversity from soil samples which have been subject to drought or biochar amendment, to further our understanding of the impacts of environmental change on microbial communities.

Microstructural modeling of skin mechanics

Georges Limbert (Investigator), Emanuele Zappia

Microstructural modeling of skin mechanics to gain a mechanistic insight into the biomechanics of the skin.

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 neuronal activity at the knee joint

Mark Taylor, Tiina Roose (Investigators), Gwen Palmer

The function of the knee joint is reliant on proprioception, which involves the response of nerve endings in the tissues at the joint. This project will be concentrating on the neuronal activity, caused by mechanical stimuli, of the more common receptors found at the knee (Ruffini, Paciniform, Golgi and Nociceptor).

There are three stages to this project:
1. Modelling the behaviour of each individual receptor, with the use of the Hodgkin-Huxley model [1].
2. These models will then be applied to the soft tissues around a knee, where a global deformation of the tissue will result in local stimulation of receptors.
3. The soft tissue models will then be applied to structures in the knee.

[1] - Hodgkin, A.L. and A.F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 1952. 117: p. 500-544.

Models of Avian Flocking

Edward Butler (Investigator)

This research review project explores current state of avian flock modelling research, exploring how ideas have developed since the earliest theories dating back to the 1930s.

Molecular Fragments in Inhibitor Design

Jonathan Essex (Investigator), Michael Bodnarchuk

Fragment-Based Drug Discovery (FBDD) has emerged as an important tool in the drug discovery process. Instead of screening entire drug molecules, FBDD screens molecular fragments; constituents which make up drug molecules. A computational approach to identifying fragment binding is currently being sought which also yield binding free energy estimation.

Multi-objective design optimisation of coronary stents

Neil Bressloff, Georges Limbert (Investigators), Sanjay Pant

Stents are tubular type scaffolds that are deployed (using an inflatable balloon on a catheter), most commonly to recover the shape of narrowed (diseased) arterial segments. Despite the widespread clinical use of stents in cardiovascular intervention, the presence of such devices can cause adverse responses leading to fatality or to the need for further treatment. The most common unwanted responses of inflammation are in-stent restenosis and thrombosis. Such adverse biological responses in a stented artery are influenced by many factors, including the design of the stent. This project aims at using multi-objective optimisation techniques to find an optimum family of coronary stents which are more resistant to the processes of in-stent restenosis (IR) and stent thrombosis (ST).

Multi-scale simulations of bacterial outer-membrane proteins

Syma Khalid (Investigator), Jamie Parkin

Using Iridis to run multiple simulations, I aim to simulate the outer membrane proteins of Pseudomonas aeruginosa, using X-ray crystal structures of proteins only recently resolved by Bert van den Berg, University of Massachusetts. By modelling the proteins in a realistic P. aeruginosa outer membrane, I aim to gain insight into the binding of these proteins to specific substrates and their function.

Multiscale modelling of biological membranes

Jonathan Essex (Investigator), Mario Orsi

Biological membranes are complex and fascinating systems, characterised by proteins floating in a sea of lipids. Biomembranes, besides being the fundamental structures employed by nature to encapsulate cells, play crucial roles in many phenomena indispensable for life, such as growth, energy storage, and in general information transduction via neural activity. In this project, we develop and apply multiscale computational models to simulate biological membranes and obtain molecular-level insights into fundamental structures and phenomena.

Multiscale Modelling of Cellular Calcium Signalling

Hans Fangohr, Jonathan Essex (Investigators), Dan Mason

Calcium ions play a vitally important role in signal transduction and are key to many cellular processes including muscle contraction and cell apoptosis (cell death). This importance has made calcium an active area in biomedical science and mathematical modelling.

Multiscale Modelling of Electrochemical Processes in Neurons

John Chad (Investigator), Stuart George

Using asymptotic expansions to determine how the signalling behaviour of neurons is related to their microstructure.

Multiscale models of magnetic materials at elevated temperatures

Denis Kramer (Investigator), Jonathon Waters

This project will develop and apply multi-scale modelling approaches to investigate thermal fluctuation effects in magnetic materials.

MXL Project

Mark Taylor, Junfen Shi (Investigators)

‘MXL’ is short for “Enhanced patient safety by computational Modelling from clinically available X-rays to minimise the risk of overload and instability for optimised function and Longevity”. This is an international EU-funded project which the Bioengineering Sciences Research Group at Southampton is involved in. For more information, visit http://www.m-x-l.eu

New Forest Cicada Project

Alexander Rogers, Geoff Merrett (Investigators), Davide Zilli, Oliver Parson

Rediscover the critically endangered New Forest cicada with crowdsourced smartphone biodiversity monitoring techniques.

OMSys Towards a system model of a bacterial outer membrane

Syma Khalid (Investigator)

Many bacteria have an outer membrane which is the interface between the cell and its environment. The components of this membrane are well studied at an individual level, but there is a need to model and understand the outer membrane as a whole. In this project we aim to develop such a model of a bacterial outer membrane, linking computer simulations of the component molecules through to a more "systems biology" approach to modelling the outer membrane as a whole. Such an approach to modelling an OM must be multi-scale i.e. it must embrace a number of levels ranging from atomistic level modelling of e.g. the component proteins through to higher level "agent-based" modelling of the interplay of multiple components within the outer membrane as a whole. The different levels of description will be integrated to enable predictive modelling in order to explore the roles of outer membrane changes in e.g. antibiotic resistance.

Optimisation of Acoustic Systems for Perceived Sound Quality

Jordan Cheer (Investigator), Daniel Wallace

Acoustic systems have traditionally been optimised on the basis of minimising an objective acoustic measure, such as sound pressure level. The project investigates the use of subjective measures of sound quality, such as "loudness", "harshness" etc. in optimisation algorithms.

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.

Predicting Relative Protein Abundance via Sequence-Based Information

Gregory Parkes (Investigator), Mahesan Niranjan

Understanding the complex interactions between transcriptome and proteome is essential in uncovering cellular mechanisms both in health and disease contexts. The limited correlations between corresponding transcript and protein abundance suggest that regulatory processes tightly govern information flow surrounding transcription and translation, and beyond.

In this study we adopt an approach which expands the feature scope that models the human proteome: we develop machine learning models that incorporate sequence-derived features (SDFs), sometimes in conjunction with corresponding mRNA levels. We develop a large resource of sequence-derived features which cover a significant proportion of the H. sapiens proteome, demonstrate which of these features are significant in prediction on multiple cell lines, and suggest insights into which biological processes can be explained using these features.

We reveal that (a) SDFs are significantly better at protein abundance prediction across multiple cell lines both in steady-state and dynamic contexts, (b) that SDFs can cover the domain of translation with relative efficiency but struggle with cell-line specific pathways and (c) provide a resource which can be plugged into many subsequent protein-centric analyses.

Prediction of Psychopathology by MRT data

We aim to predict psychopathological outcomes in adults by functional brain data using multilevel regression and crossvaligdation strategies.

Preventing Alzheimer's Disease: A Multiphysics Simulation Approach

Neil Bressloff, Giles Richardson, Roxana-Octavia Carare (Investigators), Alexandra Diem

Experimental research has identified the causes of many diseases, such as Alzheimer's Disease. However, finding an effective treatment is very cost- and time-intensive and sacrifices many animals and does not guarantee success. In this PhD project, we investigate the driving force of solute drainage in the brain using multiphysics simulations in order to identify possible ways of preventing dementia.

Probing the oligomeric state and interaction surface of Fukutin Transmembrane Domain in lipid bilayer via Molecular Dynamics simulations

Syma Khalid, Philip Williamson (Investigators), Daniel Holdbrook, Jamie Parkin, Nils Berglund, Yuk Leung

Fukutin Transmembrane Domain (FK1TMD) is localised to the endoplasmic reticulum or Golgi Apparatus within the cell where it is believed to function as a glycosyltransferase. Its localisation within the cell is thought to be mediated by the interaction of its N-terminal transmembrane domain with the lipid bilayers surrounding these compartments, each of which possess a distinctive lipid composition. Studies have revealed that the N-terminal transmembrane domain of FK1TMD exists as dimer within dilauroylphosphatidylcholine bilayers and this interaction is driven by interactions between a characteristic TXXSS motif. Furthermore residues close to the N-terminus that have previously been shown to play a key role in the clustering of lipids are shown to play a key role in anchoring the protein in the membrane.

Quantifying Collective Construction

Seth Bullock (Investigator), Nicholas Hill

This was an initial investigation into how best to develop quantifying and discriminating measures of both the processes and results of collective construction.

Respiratory mask modeling

Jacques Ernes

Abaqus modelling of repiratory masks, bioengineering, Health sciences

Sample tracking in whole-exome sequencing projects

Andrew Collins, Sarah Ennis (Investigators), Reuben Pengelly

Whole-exome sequencing is entering clinical use for genetic investigations, and it is therefore essential that robust quality control is utilised. As such we designed and validated a tool to allow for unambiguous tying of patient data to a patient, to identify, and thus prevent errors such as the switching of samples during processing.

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.

Sensitivity of the critical depth to the choice of particle movement rules in Lagrangian models and the consequences for the predicted timing of the spring bloom

Tom Anderson (Investigator), Melissa Saeland

Individual-based (Lagrangian) models lend themselves to the study of the controls of the spring bloom in the ocean, due to their ability to represent both the turbulence and the phytoplankton motion. Here, we use a Lagrangian phytoplankton model to test some of the most prevalent hypotheses (e.g. critical depth and critical turbulence).

Simulating Household Decision Making in Rural Malawi

James Dyke, Kate Schreckenberg (Investigators), Samantha Dobbie

A scoping exercise to determine whether data collection tools of the social sciences can be used effectively in the construction of empirical ABM. Focus fell upon simulating drought coping strategies of Malawian smallholders. Model implementation enabled inferences to be made concerning the impact of drought and input subsidies upon smallholder food security.

Simulating Sleeping Sickness: a two-host agent-based model

Jason Noble, Peter Atkinson (Investigators), Simon Alderton

Sleeping sickness is a vector-borne, parastic disease which affects millions of people across 36 sub-Saharan African countries. Using agent-based models, we aim to gain a greater understanding of the interactions between the tsetse fly vector and both animal and human hosts.

Building an accurate representation will allow the testing of local interventation scenarios including the closing of watering holes, and the selective spraying of cattle with insecticides.

Simulation modelling of habitat permeability for mammalian wildlife

Patrick Doncaster, Jason Noble (Investigators), Angela Watkins

Using and integrating least-cost models and agent-based simulations to explore the way in which mammals interact with, and hence move, through fragmented landscapes.

Simulation of biological systems at long length and distance scales

Jonathan Essex (Investigator), Kieran Selvon

This project aims to shed light on cell membrane mechanisms which are difficult to probe experimentally, in particular drug permiation across the cell membrane. If one had a full understanding of the mechanism, drugs could be designed to target particular embedded proteins to improve their efficacy, the viability of nano based medicines and materials could also be assessed, testing for toxicity etc.

Spatially Embedded Complex Systems Engineering

Seth Bullock (Investigator)

SECSE brought together an interdisciplinary team of scientists working on an ambitious three-and-a-half year project titled. The research cluster spanned neuroscience, artificial intelligence, geography, and complex systems in an attempt to understand the role of spatial organization and spatial processes in complex networks within the domains of neural control, geo-information systems and distributed IT systems such as those implicated in air-traffic control.

Statistical model of the knee

Mark Taylor (Investigator), Francis Galloway, Prasanth Nair

Development of methods for large scale computational testing of a tibial tray incorporating inter-patient variability.

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.

Sustainable domain-specific software generation tools for extremely parallel particle-based simulations

Chris-Kriton Skylaris (Investigator)

A range of particle based methods (PBM) are currently used to simulate materials in chemistry, engineering, physics and biophysics. The 4 types of PBM considered directly in the proposed are molecular dynamics (MD), the ONETEP quantum mechanics-based program, discrete element modelling (DEM), and smoothed particle hydrodynamics (SPH).
The overall research objective is to develop a sustainable tool that will deliver, in the future, cutting edge research applicable to applications ranging from dam engineering to atomistic drug design.

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.

TEDx: Closing the Loop: Entropy Accounting for a Sustainable World

Stuart Bartlett (Investigator)

This is a TEDx talk that I gave on some ideas I've had about the large-scale thermodynamic organisation of life on Earth. While these ideas probably aren't new, I believe they can teach us something about the way in which we think about energy and the 'consumption' of goods and energy.

Testing an interaction game on relationships.

Seth Bullock (Investigator), Anastasia Eleftheriou

The aim of this project is to examine how attractiveness is related to hypothetical risky sexual behaviour. The term `risky sexual behaviour' refers to having multiple sexual partners without the use of a condom. Data will be collected using questionnaires in order to investigate the influence of attractiveness on intentions towards engaging in unprotected sexual intercourse. A primary research question is whether perceived attractiveness of a potential partner affects the reported likelihood of having sex and/or using a condom.

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 application of next-generation sequencing to unresolved familial disease

Andrew Collins, Sarah Ennis (Investigators), Jane Gibson, Reuben Pengelly

Next-generation sequencing (NGS) allows us to sequence individual patients cost-effectively, allowing us to enter a new era of genomic medicine. The level of genetic detail that we can access through these methods is unprecedented making it suitable for clinical molecular diagnostics.

The autotransporter ? domain: insights into structure and function through multi scale molecular dynamics simulations

Syma Khalid (Investigator), Daniel Holdbrook, Thomas Piggot

We are performing a series of molecular dynamics simulations involving all autotransporters with known structure. We aim to identify key structural and dynamic properties in this family of proteins.

The hydrogen abstraction phase of the CYP-cyclohexene reaction, using large-scale DFT

Chris-Kriton Skylaris (Investigator), Chris Pittock, Karl Wilkinson

Studying the hydrogen-abstraction reaction between cyclohexene and the active site of cytochrome P450. This starts a series of reactions that eventually oxidise the small molecule to become either an epoxide or an alcohol.

Understanding the finer detail of this reaction can assist towards a model that will predict the breakdown of drugs in the human body.

The importance of timescales for the emergence of environmental self-regulation

Iain Weaver, James Dyke (Investigators)

Models which explore the possibilities of emergent self-regulation in the Earth system often assume the timescales associated with changes in various sub-systems to be predetermined. We analyse a classic model of environmental self-regulation, Daisyworld, and interpret the original equations for model temperature, changes in insolation, and self-organisation of the biota as an important separation of timescales.

The mysteries of Braun's Lipoprotein.

Syma Khalid (Investigator), Alister Boags

Despite Braun's lipoprotein being the most common form of protein present in E.Coli bacterial membranes, it remains unstudied in the field of computational study. This is remedied by the implementation of molecular dynamics simulations to study the interactions of this lipoprotein with a structure of infinite peptidoglycan (cell wall) that has previously not been used. Shown in the project is the interactions and physical properties of the lipoprotein in it's native environment of the crowded periplasm.

THE NORM MATE TRANSPORTER FROM N. GONORRHEAE: INSIGHTS INTO DRUG & ION BINDING FROM ATOMISTIC MOLECULAR DYNAMICS SIMULATIONS

Syma Khalid (Investigator), Daniel Holdbrook, Thomas Piggot, Yuk Leung

The multidrug and toxic compound extrusion (MATE) transporters extrude a wide variety of substrates out of both mammalian and bacterial cells via the electrochemical gradient of protons and cations across the membrane. Multiple atomistic simulation are performed on a MATE transporter, NorM from Neisseria gonorrheae (NorM_NG) and NorM from Vibrio cholera (NorM_VC). These simulations have allowed us to identify the nature of the drug-protein/ion-protein interactions, and secondly determine how these interactions contribute to the conformational rearrangements of the protein.

The ONETEP project

Chris-Kriton Skylaris (Investigator), Stephen Fox, Chris Pittock, Álvaro Ruiz-Serrano, Jacek Dziedzic

Program for large-scale quantum mechanical simulations of matter from first principles quantum mechanics. Based on theory and algorithms we have developed for linear-scaling density functional theory calculations on parallel computers.

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 response of the Bergmann glial cell to synaptic activity

Giles Richardson (Investigator), Stuart George

We model the potential changes induced in the Bergmann glial cell by synaptic activity in neighbouring neurons.

The Role of the Biota in the Carpenter Model on Lake Eutrophication

James Dyke (Investigator), Alexandra Diem

The Carpenter model is a useful and simple model to predict the eutrophication of shallow lakes via phosphorus input. This project aimed at resolving the function of the biota, which play a major role in the phosphorus dynamics, but are so far only implicitly modelled, and extending the model to explicitly represent them.

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.

The tarsal intersegmental reflex control system in the locust hind leg

David Simpson, Philip Newland (Investigators), Alicia Costalago Meruelo

Locomotion is vital for vertebrates and invertebrates to survive. Despite that feet are responsible for stability and agility in most animals, research on feet movements and their reflexes is scarce.
In this thesis, the tarsal reflex responses of locust will be studied and modelled with ANNs to achieve a deeper comprehension of how stability and agility is accomplished.
The choice of ANNs is linked to the applicability of the method into other fields, such as technological designs or medical treatment.

Tipping points in Complex Coupled Life-Environment Systems

Iain Weaver, James Dyke (Investigators)

System-level homeostasis has been demonstrated in a number of conceptual, artificial life, models which share the advantage of a thorough and transparent analysis. We reintroduce a general model for a coupled life-environment model, concentrating on a minimal set of assumptions, and explore the consequences of interaction between simple life elements and their shared, multidimensional environment.

Tissue Engineering

Tiina Roose (Investigator)

This project deals with applying mathematical and computational modelling techniques to answer questions that are useful for tissue engineering applications.

Towards design patterns for robot swarms

Richard Crowder, Seth Bullock (Investigators), Lenka Pitonakova

Swarm robotics is an inter-disciplinary field that seeks to design the behaviour of robots that can cooperate effectively on tasks like search and retrieval, reconnaissance, construction, etc. In this project, we are aiming towards a theoretical understanding of swarm intelligence and the development of design patterns for effective robot swarms.

Transgenerational inheritance of allergy in a multi generational cohort

John Holloway (Investigator)

The aim of this project is to determine the vertical transmission of DNA methylation by identification of CpG sites by microarray analysis of 450,000 CpG sites in 252 women of the IoW cohort that are associated with allergic sensitization and testing whether the identified methylation patterns are vertically transmitted to their offspring and whether modifiable environmental conditions during gestation affect DNA methylation.

Uncovering extensive post-translation regulation during human cell cycle progression by integrative multi-’omics analysis

Gregory Parkes (Investigator), Mahesan Niranjan

Analysis of high-throughput multi-’omics interactions across the hierarchy of expression has wide interest in making inferences with regard to biological function and biomarker discovery. Expression levels across different scales are determined by robust synthesis, regulation and degradation processes, and hence transcript (mRNA) measurements made by microarray/RNA-Seq only show modest correlation with corresponding protein levels.

In this work we are interested in quantitative modelling of correlation across such gene products. Building on recent work, we develop computational models spanning transcript, translation and protein levels at different stages of the H. sapiens cell cycle. We enhance this analysis by incorporating 25+ sequence-derived features which are likely determinants of cellular protein concentration and quantitatively select for relevant features, producing a vast dataset with thousands of genes. We reveal insights into the complex interplay between expression levels across time, using machine learning methods to highlight outliers with respect to such models as proteins associated with post-translationally regulated modes of action.

We uncover quantitative separation between modified and degraded proteins that have roles in cell cycle regulation, chromatin remodelling and protein catabolism according to Gene Ontology; and highlight the opportunities for providing biological insights in future model systems.

Understanding the Role of Recruitment in Robot Foraging

Seth Bullock, Richard Crowder (Investigators), Lenka Pitonakova

It is shown that recruitment among foraging robots is useful when resources are hard to find, but that the extra cost associated with such robots is not returned when there are many locations to gather from or simply when the relative gain from using communication is low.

Using Molecular Dynamics to Understand the Antibacterial Mechanisms of Daptomycin & Chlorhexidine to Target the Bacterial Membrane

This project aims to use molecular dynamics techniques to understand how antimicrobial peptides, daptomycin and chlorhexidine, disrupt both gram positive and negative cell membranes on an atomic level.

Using Molecular Dynamics to Understand the Antibacterial Mechanisms of Daptomycin & Chlorhexidine to Target the Bacterial Membrane

This project aims to use molecular dynamics techniques to understand how antimicrobial peptides, daptomycin and chlorhexidine, disrupt both gram positive and negative cell membranes on an atomic level.

Using Molecular Dynamics to Understand the Antibacterial Mechanisms of Daptomycin & Chlorhexidine to Target the Bacterial Membrane

Syma Khalid (Investigator), Eilish McBurnie

This project aims to use molecular dynamics techniques to understand how antimicrobial peptides, daptomycin and chlorhexidine, disrupt both gram positive and negative cell membranes on an atomic level.

Validation of a spatial-temporal soil water movement and plant water uptake model

Tiina Roose, Sevil Payvandi (Investigators), James Heppell

We develop a model that estimates the water saturation level within the soil at different depths, and the uptake of water by the root system. Data from Smethurst et al (2012) is used to validate our model and obtain a fully calibrated system for plant water uptake. When compared quantitatively to other models such as CROPWAT, our model achieves a better fit to the experimental data because of the simpler, first, second and third order terms present in the boundary condition, as opposed to complicated non-linear functions.

Water molecules in drug development: can we predict drug affinity when water molecules are involved?

Jonathan Essex (Investigator), Hannah Bruce Macdonald, Christopher Cave-Ayland

Water molecules are often found to be involved in drug-protein binding and can influence the effectiveness of a drug. We aim to aid drug design by calculating the energies involved with complexes of drugs, proteins and water molecules to predict the affinities of drug molecules.

Water Molecules in Protein Binding Sites

Jonathan Essex (Investigator), Michael Bodnarchuk

Water molecules are commonplace in protein binding sites, although the true location of them can often be hard to predict from crystallographic methods. We are developing tools which enable the location and affinity of water molecules to be found.

Whole exome sequencing identifies novel FLNA mutation in familial Ebstein's anomaly

Jane Gibson, Andrew Collins, Sarah Ennis (Investigators), Gaia Andreoletti

We describe the application of whole-exome sequencing in a family in which eight people in three generations presented Ebstein's anomaly.

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.