Heterogeneity-driven cell signalling
Lead Research Organisation:
University of Nottingham
Department Name: Sch of Mathematical Sciences
Abstract
It is often thought that when cells are genetically identical, they behave in the same manner. However, recent experiments have convincingly demonstrated that this is far from the truth. For example, stimulating the same cell multiple times with the same stimulus may lead to very different responses. Given that these responses are crucial for healthy well-being (e.g. insulin release after eating cake) a key question in cell signalling is to understand and predict these heterogeneous cell dynamics.
In this project, we will use stochastic point-processes to describe such inhomogeneous cellular behaviour and use Bayesian methods to fit them to data. As a test case, we will investigate the dynamics of one of the most important cellular messengers: intracellular calcium. More precisely, we will study so-called calcium spike sequences and aim to ascertain what statistical features best describe the observed heterogeneity. The insights that we will gain will be both of practical and conceptual importance. As for the former, a key goal in computational cell physiology is to model cell behaviour in a computationally inexpensive, yet accurate manner. A Bayesian approach offers a natural way for doing this in conjunction with the quantification of uncertainty of the dynamics of the process. In terms of conceptual advances, the statistical details of calcium spike sequences may point towards the mechanisms that generate them. In turn, this is crucial information when we need to tailor signalling cascades for biomedical applications.
In this project, we will use stochastic point-processes to describe such inhomogeneous cellular behaviour and use Bayesian methods to fit them to data. As a test case, we will investigate the dynamics of one of the most important cellular messengers: intracellular calcium. More precisely, we will study so-called calcium spike sequences and aim to ascertain what statistical features best describe the observed heterogeneity. The insights that we will gain will be both of practical and conceptual importance. As for the former, a key goal in computational cell physiology is to model cell behaviour in a computationally inexpensive, yet accurate manner. A Bayesian approach offers a natural way for doing this in conjunction with the quantification of uncertainty of the dynamics of the process. In terms of conceptual advances, the statistical details of calcium spike sequences may point towards the mechanisms that generate them. In turn, this is crucial information when we need to tailor signalling cascades for biomedical applications.
Organisations
People |
ORCID iD |
Ruediger Thul (Primary Supervisor) | |
Jake Powell (Student) |
Publications
Powell J
(2020)
A Statistical View on Calcium Oscillations.
in Advances in experimental medicine and biology
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/N50970X/1 | 01/10/2016 | 30/09/2021 | |||
1947069 | Studentship | EP/N50970X/1 | 01/10/2017 | 31/03/2021 | Jake Powell |
Title | View Data |
Description | In this application you can input raw calcium concentration data (time series) and view a plot of how the concentration changes. On the sidebar you can then choose/change where to threshold the data to retrieve spike times. You can also remove periods of transience and linear trends in the data, where these changes are shown in the plot viewer. Then the resultant spike times can be downloaded and used on a local computer. |
Type Of Technology | Webtool/Application |
Year Produced | 2019 |
Impact | The application has only been used within the school, however we aim to provide it to experimentalists soon, where the application can be used by non-experts to easily view and change spike times corresponding to the raw data. |