Improvements in technology often drive scientific discovery. Therefore, research requires sustained investment in the latest equipment and training for the researchers who are going to use it. Prioritising and administering infrastructure investment is challenging because future needs are difficult to predict. In the past, highly computationally demanding research was associated primarily with particle physics and astronomy experiments. However, as biology becomes more quantitative and bioscientists generate more and more data, their computational requirements may ultimately exceed those of physical scientists. Computation has always been central to bioinformatics, but now imaging experiments have rapidly growing data processing and storage requirements. There is also an urgent need for new modelling and simulation tools to provide insight and understanding of these biophysical experiments. Bioscience communities must work together to provide the software and skills training needed in their areas. Research-active institutions need to recognise that computation is now vital in many more areas of discovery and create an environment where it can be embraced. The public must also become aware of both the power and limitations of computing, particularly with respect to their health and personal data.

Rapid advances in experimental biophysical techniques are generating a wealth of information about the mechanical operation of the cellular cytoskeleton and its motors. However, each of these tools typically provides only a limited piece of a highly complex puzzle. There is a need to develop new computational tools that can integrate these data together into a central model. Here we discuss the experimental advances alongside the computational tools, and propose how these could be developed to successfully combine the emerging structural and dynamic experimental data on cytoskeletal motors. We consider examples of both single motors and arrays of motors within a biological cell.This article is categorized under: Structure and Mechanism > Molecular Structures Data Science > Computer Algorithms and Programming Data Science > Visualization

Do Research Software Engineers have research methods, and why today do we have increasingly more types of academic research institutions and organisations

We present a method for extracting high-resolution ordered features from localisation microscopy data by analysis of relative molecular positions in 2D or 3D. This approach allows pattern recognition at sub-1% protein detection efficiencies, in large and heterogeneous samples, and in 2D and 3D datasets. We used this method to infer ultrastructure of the nuclear pore, the cardiomyocyte Z-disk, DNA origami structures and the centriole.

The development of research software engineering as a profession: Dr Joanna Leng at the School of Computing explores Research Software Engineering (RSE) as an emerging profession, and how computing technology is core to many professions. Research Software Engineering is a young and emerging profession. This article looks at how it compares to other more mature professions and how all these professions handle two factors that are particularly important to the RSE profession, the stability of computing technologies and the adoption of these computing technologies by each profession.

Dr Joanna Leng, Senior Research Software Engineering Fellow at the University of Leeds, discusses software with repeated early innovation using the example of the FFEA software