Understanding information processing in biological systems, involving the development of novel computational techniques to model and analyse biological data and to model and analyse biological systems. This research area is highly interdisciplinary; it recognises novel technical computer science challenges in retrieving and analysing biological data and the computational modelling of biological systems, including systems and synthetic biology, and neuroinformatics and computational neuroscience.
The Biotechnology and Biological Sciences Research Council (BBSRC), Medical Research Council (MRC) and Natural Environment Research Council (NERC) support the wider field of bioinformatics research (Evidence source 1).
We aim to maintain the size of this research area as a proportion of the EPSRC portfolio. We will continue to support research into novel computational modelling, which enables information processing relevant to biological systems.
This area is inherently cross-disciplinary, with close links to Artificial Intelligence Technologies, Information Systems, Statistics and Applied Probability, and Synthetic Biology. (Evidence source 2) Projects must be driven by computer science and/or mathematics and statistics; no more than half of the intended work should be in the biological and/or biomedical application domains. Research on development/translation of bioinformatics tools is a lower priority for funding; we expect the development/translation of such tools to become increasingly integrated with novel biological science research.
By the end of the current Delivery Plan, we aim to have:
- A portfolio in this research area which supports the wider field of bioinformatics as supported by BBSRC, MRC and NERC and that researchers in this area can contribute to. Biological Informatics will be a successful cross-disciplinary area through collaboration with researchers from other areas
- Supported the novel computational modelling aspects of Biological Informatics, which are very important to systems biology research and complement/support the Synthetic Biology research area. The Synthetic Biology Roadmap demonstrates the importance of Biological Informatics to the UK (Evidence source 3)
- A portfolio in this research area which has contributed to the data science agenda, and the retrieval and analysis of data in biological systems, through the use of novel computational tools and models
- Continued to monitor what appears to be growing interest in exploiting advances in neuroinformatics for the design of neural computing, microelectronics, system architectures and autonomous systems, which may tie in to the developing area of human-like computing
Biological Informatics research will play a role in delivering the objectives of EPSRC's Data Enabled Decision Making cross-ICT priority. To maximise the impact of these contributions, researchers should link to other research areas (e.g. Artificial Intelligence Technologies, Information Systems, Mathematical Biology, Statistics and Applied Probability, and Synthetic Biology).Highlights:
This research area sits at the interface of BBSRC, MRC, NERC and EPSRC. The wider area of bioinformatics has had high-quality papers published in applied biology journals. (Evidence source 3,4)
Training is mainly being delivered by our Centres for Doctoral Training (CDTs) with relevance to Biological Informatics.
The UK has a strong biotechnology industry, with significant growth in medical technology (including technologies based on synthetic biology). Biological Informatics is key to supporting this growth.
As noted above, Biological Informatics can contribute to the data science agenda through retrieval and analysis of data in biological systems, by using novel computational tools and models. There are drivers for this type of analysis in the growth areas of systems biology and synthetic biology modelling (and in-silico modelling) for the biotech, medical and pharma sectors, and for the growing area of genomics science. (Evidence source 5,6)
There also appears to be increasing interest in exploiting advances in neuroinformatics for the design of neural computing, for example (see above). (Evidence source 2)
This research area will contribute in particular to Healthy and Productive Nation Outcomes. Ambitions of particular relevance include:
H1: Optimise diagnosis and treatment
This research area is expected to contribute by utilising genomic information to inform targeted interventions, and by developing therapeutic technologies utilising computational simulation and mathematical modelling approaches.
P1: Introduce the next generation of innovative and disruptive technologies
This research area is expected to contribute to realising the potential of Synthetic Biology.
Research area connections
This diagram shows the top 10 connections between Research Areas within the EPSRC research portfolio. The depth of the segment relates to value of grants and the width of the segment relates to the number of grants shared by those two Research Areas. Please click to see the related Research Area rationale.
Visualising our Portfolio (VoP)
Visualising our portfolio (VoP) is a tool for users to visually interact with the EPSRC portfolio and data relationships.
EPSRC support by research area in Biological informatics (GoW)
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