Primary Supervisor: Dr Robert Dallmann, WMS
Secondary supervisor: Professor David Rand, Maths
PhD project title: Multi-parametric characterisation and quantifying of information flow between the circadian clock and the cell cycle
University of Registration: University of Warwick
Francis Crick argued it is better in biology to follow the flow of information than those of matter or energy. However, it is currently unclear what is the optimal method for characterising and quantifying cellular information. In fact, recent understanding about the high levels of stochasticity in regulatory and signalling systems raises difficult questions about how biological systems can function so reliably given the underlying noisy biochemistry and the ubiquity of stochastic perturbations from the internal and external environments of the cell [e.g., 1].
Developing an information theoretic approach is central to addressing these questions because the key higher-order processes have information transmission at their heart. In this project, we want to understand how to make high accuracy networks from noisy components. Specifically, how (often) does the circadian clock talk to the cell cycle?
In addition to its intrinsic importance of the circadian clock in cellular processes, this is an ideal exemplar to study the physical representation of information flow as the clock and cell cycle are dynamic and there are multiple well characterised links [e.g., 2], thus, enabling us to study how information transfer depends upon connection network structure. Moreover, it seems likely that understanding this will generalise to other clock-controlled processes and other coupled dynamical systems. Likely, cellular processes are using timing information from the clock to allocate resources or separate processes in time, e.g., through time encoded protein-protein interaction networks.
First, you will characterise and quantify the multivariate stochasticity in the circadian clock. To do this, you will generate a comprehensive set of protein-fusion reporters of clock components and use them to determine the stochasticity in the clock mechanism and determine key variables that characterise and determine the robust and accurate function of this system. We hypothesise that a key reason for the complexity of the clock is that this enables it to transmit more information to the systems it regulates using the correlations in its multidimensional state to overcome the stochasticity of its components.
In a second part, you will characterise and quantify information flow between the circadian clock and the cell cycle. This will enable us to develop a conceptual framework, mathematical ideas and practical tools for characterising and quantifying information in biological systems that will be of use across a broad range of coupled systems. We will develop new approaches to quantify the stochasticity of the corresponding systems both at the component and systemic level and aim to identify key organisational principles for the nature of interactions in such a stochastic context, including optimisation principles.
The results of this work will advance the understanding of the relationship between the circadian clock and the cell cycle, which should be of fundamental importance to understand the impact of circadian disruption on human health, e.g., the link between shift-work and higher cancer risk. Furthermore, we will develop concepts and tools that might prove useful to interrogate other complex interactions between dynamic signalling systems such as those seen in the inflammatory response.
- Precise developmental gene expression arises from globally stochastic transcriptional activity. Cell. 2013. 15;154:789-800. doi: 10.1016/j.cell.2013.07.025.  Molecular Cogs: Interplay between Circadian Clock and Cell Cycle. Trends Cell Biol. 2018. 28:368-379. doi: 10.1016/j.tcb.2018.01.006.
BBSRC Strategic Research Priority: Understanding the Rules of Life: Systems Biology
Techniques that will be undertaken during the project:
- Mammalian cell culture
- Molecular cloning
- Generation of transgenic cell lines using CRISPR/Cas9
- Flow cytometry including FACS
- High throughput live-cell imaging
- High content image analysis
- Time-series analysis
- Systems biology modelling
Contact: Dr Robert Dallmann, University of Warwick