Computational Modelling Group

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.

Optical Characterisation of Black Silicon for Photovoltaics Using the Finite Element Method

Jack Tyson (Investigator)

Here we present a novel method of simulating the reflectance spectra of black silicon solar cells using the finite element method. Designed in COMSOL Multiphysics is a new set of algorithm-controlled-geometries rendering a vast array of different structural permutations of silicon nanowires. Our model focused on the variation of this geometry within customisable predefined conditions in large output quantities, collated and averaged to reliably determine the reflectance of an entire black silicon solar cell.

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.

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.

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.