Robust Analysis of Signal Transduction Underlying Cellular Variability in Stem Cells
Lead Research Organisation:
Imperial College London
Department Name: Life Sciences
Abstract
At the molecular level no cell is like any other. Differences between genetically identical cells can in fact be quite pronounced, and these differences can have profound biological and biomedical implications. Stem cells divide and differentiate in response to physiological and environmental cues but exhibit considerable differences in how they do so; equally, tumour cells respond differently to drug treatment or radiation therapy. The mechanisms driving such variability among to all intents and purposes seemingly identical cells are only poorly understood. In order to understand these fundamental processes - which also have numerous biomedical and pharmacological implications - we have to overcome experimental and mathematical modelling challenges.
Here we develop the necessary experimental and theoretical frameworks, which allow us to probe single cells, measure the abundances of key signalling molecules, and infer mathematical mechanistic models for signal transduction. We will then analyze these mathematical models in order to understand better how the structure of the signal transduction network influences cell-to-cell variability. Most importantly, however, we will develop novel strategies which will allow us to guide cellular decision making processes and determine patterns of differences between cells. If we manage to reduce or otherwise affect such variability then we gain a novel way to guide the behaviour of cells.
Here we develop the necessary experimental and theoretical frameworks, which allow us to probe single cells, measure the abundances of key signalling molecules, and infer mathematical mechanistic models for signal transduction. We will then analyze these mathematical models in order to understand better how the structure of the signal transduction network influences cell-to-cell variability. Most importantly, however, we will develop novel strategies which will allow us to guide cellular decision making processes and determine patterns of differences between cells. If we manage to reduce or otherwise affect such variability then we gain a novel way to guide the behaviour of cells.
Technical Summary
The aims of this research are
- developing inferential and modelling tools to capture the complexities of signal transduction and cellular decision making at the level of single cells.
- Understand what causes cell-to-cell variability in cell-fate decision.
- Develop control mechanisms that manage to guide such cellular variability
- Apply these methods in the context of signal transduction pathways in stem cells.
I will address this problem - as appropriate for this fellowship - using a combination of mathematical and statistical modelling approaches. In particular I will look at single cell signal transduction data related to cell-fate decision as regards differentiation and proliferation. Sequential statistical approaches are ideally suited to studying such processes, especially for experimental techniques, which do not track cells over time but assay different cells at the different time-points. Moreover, for the purposes of control they offer perhaps the most flexible framework to direct interventions (e.g. administration of growth factors) in response to real-time dynamics.
The proposed research will provide new methodologies, which in turn will offer new insights into the molecular mechanisms underlying cell-to-cell variability in molecular and cell-fate decision phenotypes. I will then apply these tools to models and real data of signal transduction and cellular decision making processes in stem cells (especially haematopietic stem cells). Of particular interest is the development of novel design protocols for stimuli and therapeutic interventions that can interfere with the processes that principally determine the levels of cell-to-cell variability.
- developing inferential and modelling tools to capture the complexities of signal transduction and cellular decision making at the level of single cells.
- Understand what causes cell-to-cell variability in cell-fate decision.
- Develop control mechanisms that manage to guide such cellular variability
- Apply these methods in the context of signal transduction pathways in stem cells.
I will address this problem - as appropriate for this fellowship - using a combination of mathematical and statistical modelling approaches. In particular I will look at single cell signal transduction data related to cell-fate decision as regards differentiation and proliferation. Sequential statistical approaches are ideally suited to studying such processes, especially for experimental techniques, which do not track cells over time but assay different cells at the different time-points. Moreover, for the purposes of control they offer perhaps the most flexible framework to direct interventions (e.g. administration of growth factors) in response to real-time dynamics.
The proposed research will provide new methodologies, which in turn will offer new insights into the molecular mechanisms underlying cell-to-cell variability in molecular and cell-fate decision phenotypes. I will then apply these tools to models and real data of signal transduction and cellular decision making processes in stem cells (especially haematopietic stem cells). Of particular interest is the development of novel design protocols for stimuli and therapeutic interventions that can interfere with the processes that principally determine the levels of cell-to-cell variability.