Award details

Supra-molecular rules in signalling networks: A single molecule comparative study in cells and tissues

Reference	BB/G007160/1
Principal Investigator / Supervisor	Professor Peter Parker
Co-Investigators / Co-Supervisors	Professor Simon Ameer-Beg, Professor Anthony C.C. (Ton) Coolen, Professor Tony Ng, Professor George Santis
Institution	King's College London
Department	Cancer Studies
Funding type	Research
Value (£)	2,242,441
Status	Completed
Type	Research Grant
Start date	01/09/2009
End date	31/08/2015
Duration	72 months

Abstract

The proposed programme of work represents a major technological development because it employs a, thus far unavailable, pioneering technique combination that will, for the first time, provide high-resolution quantitative observations on the topology and composition of signalling complexes and the dynamics of the protein signalling network whilst at work in live cells and tissues. This will place us much closer towards a comprehensive understanding of ErbB signalling - from single molecule to the systems level. Pushing the frontiers of our understanding of signalling networks in the true physiological context requires the type of multidisciplinary approach here proposed. This will deliver in a comprehensive mathematical framework of systems modelling and prediction, integration of: wet-lab biology, several innovative optical/molecular detection techniques multidimensional single molecule microscopy, FLIM, hybrid single molecule-FLIM methods, challenging and novel data analysis algorithms, coarse-grained and molecular dynamics modelling. The methodologies to be employed are state-of-the-art and pioneering in Biology. The synergistic approach we propose will unleash the potential of each of the technologies in their application to the study of signalling networks by placing discrete groups of structure-function relationships in the context of the many thousands of possible combinatorial interactions. This is a step-change in the methods of exploitation of systems biology models because so far the only constraints available to develop systems models have arisen from putative interactions derived from high-throughput cell-free methods. Our approach is unique and will result in high-content conformational, stoichiometric, kinetic and dynamic information at the plasma membrane and the correlation with events inside the cell, being placed at the core of a mathematical systems analysis model that describes and predicts the behaviour of RTK signalling networks.Joint with BB/G006911/1

Summary

Signalling is the means by which proteins orchestrate basic intra-cellular activities and cell-to-cell communication, to regulate cell fate and to allow the development of multi-cellular organisms. To achieve a cohesive cell fate within a multi-cellular tissue, some proteins (receptors) are organised into groups at the cell surface (the plasma membrane) to function as antennas to detect extracellular chemical cues. From their position at the cell surface the receptors detect multiple inputs which they transduce across the plasma membrane to output signals in the cell interior. These signals are decoded, amplified and processed in the cell cytoplasm by intracellular signalling networks; some are subsequently transduced to the nucleus to initiate DNA transcription, replication. Then net effect is the determination of cell fate (growth, differentiation, etc.). Understanding how signal inputs and outputs are organised in protein signalling networks is one of the most fascinating questions in biology. The current dream is to derive methods that would allow the 'watching' of these network proteins in action and at atomic resolution to see details of their structure. This requires the addition of a 'time' dimension to structural biology so that the spatio-temporal parameters of all atoms in each protein can be described in detail. This is a huge challenge that in cell-free systems has begun to be partially addressed through dynamic experiments combined with molecular simulations. However, in cells, the functions of particular structural motifs are not just constraint by Brownian motions, energy landscapes and thermodynamics, but also by the local availability of partners in subcellular compartments and the boundary constraints imposed by cell environments, for example in the plasma membrane, with its 2D dimensionality, local curvature and electric fields. To understand protein function in cells observations have to be made in the only physiologically-relevant 'Laboratory', the cell. This adds many levels of complexity to an already vast challenge. Our programme of work is geared to understanding the intricate network signalling behaviour of cells in their physiological environments within tissues. We aim at describing the basic molecular ingredients, the signalling pathways and the supra-molecular structural and spatio-temporal rules regulating signalling outcomes. Our methods will be based on direct observation, 'watching' the multiple changes in the topology of interactions and its components with time, in conjunction with the modelling of behaviour at atomic resolution within a mathematical framework. Using molecular biology techniques in combination with optical methods, we can now annotate individual genes and gene products, screen for protein-protein, protein-DNA and small molecule interactions, and quantify dynamic changes. However, only single molecule-based imaging currently offers sensitive spatio-temporal detection in cells for low abundance protein interactions. This is beginning to bridge the gap between protein structure and function by allowing real-time quantitative observations of structural details, conformational intermediates, association and dissociation constants, diffusion rates, and rare events. Previous information on complex protein networks has been derived generally from high-throughput screens and/or single cell models using ensemble (averaged) technologies such as biochemical extraction followed by mass spectrometric analysis. The application of this information to understand at the molecular level the dynamic normal physiology in multi-cellular organisms and/or the pathogenetic basis of various disease states, among the heterogeneous human population is therefore limited. The approach we propose offers the means to understand and predict functional properties of cells from the changes in complex interactions between their microscopic molecular components and in response to perturbations.

Committee	Closed Committee - Biomolecular Sciences (BMS)
Research Topics	Systems Biology
Research Priority	X – Research Priority information not available
Research Initiative	Longer and Larger Grants (LoLas) [2007-2015]
Funding Scheme	X – not Funded via a specific Funding Scheme

Associated awards:

BB/G006911/1 Supra-molecular rules in signalling networks: A single molecule comparative study in cells and tissues

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