Bioinformatics: Data driven biology – challenges and opportunities
Posted on 08/11/2016
Bioinformatics, an interdisciplinary field of science which develops methods and software tools for storing, retrieving, organising and analysing biological data
The explosion in data generation in the biological sciences, and its increasing complexity, is resulting in research moving from a hypothesis-driven to a data-driven approach characterised by big data. Data driven biology requires tools and computational approaches to enable to extract value and generate new biological understanding. Bioinformatics, an interdisciplinary field of science which develops methods and software tools for storing, retrieving, organising and analysing biological data, by combining computer science, statistics, mathematics and engineering to study and process biological data, will be a key enabler in realising the potential of data-driven biology.
Genomics is likely to play an increasingly important role in R&D in the synthetic biology, industrial and medical biotechnology, medical (in particular precision medicine) and pharmaceutical sectors, as is the increase in volume of “omics” (for example, genomics, proteomics or metabolomics) data in general. This makes data analysis increasingly arduous, thus creating increasing demand for data integration and management tools.
One of the key growth areas of bioinformatics includes systems biology modelling, driven by large-scale integration of data and processes across the R&D continuum, fuelled by the trend to move from a reductionist approach to whole system evaluation.
The need for predictability of both products and processes is likely to intensify demand for in-silico modelling and algorithms to predict: toxicity; enhance process understanding and control in manufacturing; and address the gaps in understanding of biological systems, applications and model organisms to guide engineering strategies.
As a consequence of the rapid evolution of biology and the rate at which new data is being generated there is a growing need to improve the methodology and toolkit required for gaining insight and understanding from complex biological data. Visualisation tools, in particular interactive ones that facilitate exploratory data analysis, are critical to generate new observations, extract biological meaning and generate hypotheses for experimental validation from complex biological data.
Open innovation, defined as the process of innovating with others for shared risk and reward to produce mutual benefits, is finding increasing traction motivated by the emergence of novel technologies and tools that underpin the entire sector. Data sharing and data standardisation are perceived as the main barriers to success in pre-competitive collaborations which will need to be addressed by industry.
The KTN has carried out a survey of key industry challenges and opportunities, the report of which can be found below.