MRC is seeking to catalyse new collaborations, bringing together two or more groups with expertise in different research areas or approaches.
The aim is to build an integrated mechanistic understanding of human disease through the application of multiple and distinct research modalities that address biological problems across scales in novel and innovative ways.
With this pump-priming initiative, we seek to push the boundaries of the research that is currently being carried out and foster entirely new lines of enquiry at the interface of biomedical disciplines.
The total fund available is £5 million and we anticipate funding up to five collaborative projects over a funding period of up to three years.
We aim to explore opportunities for a larger initiative across councils to further build on this investment.
The initiative will support the development of novel research collaborations that connect two or more teams with expertise in different and complementary scales or modalities for the first time. We expect that investigators would not usually have published research papers or held grants together.
The initiative will allow investigators to expand their respective research beyond single or closely related scales or modalities into new territory, towards multi-modal and multi-scale approaches that can achieve a paradigm shift in the research questions being addressed.
We are inviting hypothesis-driven proposals that seek to develop a more holistic understanding of the complexity of human disease or dysfunction by studying the (dynamic) interplay of biological processes across a range of scales and modalities that would not be feasible for a single group.
This is expected to break new ground in the field and will involve integrating research approaches and data to address mechanisms that may reach across:
- scales, from molecules to cells, tissues, organs, physiological systems, the whole organisms, or population-based information
- modalities, including:
- structural analysis
- cell, tissue, organism imaging
- functional read-outs
- quantitative analysis, including:
- mathematical modelling
- machine learning or broader artificial intelligence approaches
- iteration between modelling and experimentation.
The initiative focuses on human disease and studies may be conducted in humans, use human cells, samples or data, or creative approaches to human models. For example:
- human tissue models, including organoids
- organs on a chip
- or in silico models.
We particularly encourage innovative, high-risk approaches that present the opportunity for a radical shift in current understanding.
This may involve the application of novel technologies to a range of new research questions, or making effective use of the richness of existing resources. For example, using epidemiological data to enhance mechanistic understanding.
Partnerships with industry are welcomed.
Illustrative examples include:
- integrating molecular and cellular approaches with higher organisational levels (tissue, organism) or correlative data such as ‘omics’ to understand human pathologies
- integrating imaging or analytical data from clinical or population cohorts with mechanistic experimental studies to gain insight into function
- improving mechanistic understanding of multi-morbidities, through integration of epidemiological or clinical data with experimental studies on models of human disease
- linking exposure data with molecular and physiological experimental analysis to understand causality.
We will not fund:
- studies focusing on ‘normal’ (non-disease) biology
- any research involving animal models
- studies conducted by individual groups or building on well-established collaborations, we expect that groups would not usually have published research papers or held grants together
- purely observational or correlative research that does not address an experimental hypothesis
- research solely focusing on integration of existing data and not involving experimentation
- substantial technology development that goes beyond the repurposing or adaptation of existing technologies to address new research questions.