The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.

Becoming Kalgoorlie : gold mining, brothels, and ‘skimpie’ bars

On our first day in Kalgoorlie, a local woman in her mid-thirties tells us that ‘Kal wouldn’t exist if it wasn’t for mining and prostitution’. In the ensuing days many others would tell us the same thing. More explicitly, in the words of another local resident, ‘The town was founded on brothels. [Without them] the men wouldn’t have been happy and they wouldn’t have got as much gold.’ These two phenomena – mining and prostitution – and their seemingly natural and straightforward connection to each other are also routinely invoked in tourist and popular culture depictions of Kalgoorlie. The Lonely Planet, for example, notes that ‘historically, mineworkers would come straight to town to spend disposable income at Kalgoorlie’s infamous brothels, or at pubs staffed by “skimpies” (scantily clad female bar staff)’.

Collaboration in Web N.0: Stigmergy and virtual pheromones

The aim of this project was to develop a general theory of stigmergy and a software design pattern to build collaborative websites. Stigmergy is a biological term used when describing some insect swarm-behaviour where 'food gathering' and 'nest building' activities demonstrate the emergence of self-organised societies achieved without an apparent management structure. The results of the project are an abstract model of stigmergy and a software design pattern for building Web 2.0 components exploiting this self-organizing phenomenon. A proof-of-concept implementation was also created demonstrating potential commercial viability for future website projects.