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Stochastic variation in biological systems

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  • Full or part time
    Dr Daniel Hebenstreit
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Biology is constantly subject to fluctuations, due largely to the probabilistic collisions of molecules, which form the basis of most cellular processes [1]. This is critical for the network of genes that regulate each other, as the magnitude of fluctuations can greatly increase upon passage through the system. As a consequence, cellular components, such as mRNAs and proteins, are expressed at widely differing levels among the cells of an otherwise identical population.

This biological ’noise’ has received much attention recently and has been recognized for its great impact on many systems. Several works demonstrate functional roles for noise [2, 3] while control theoretical considerations show that it is difficult to completely suppress it [4]. It is not well understood how cells can function reliably in light of these constant fluctuations and it is unknown what the most significant cellular sources of noise are.

In this project, we will apply stochastic modeling techniques [5-7] to understand and interpret datasets about mRNA expression levels in single mammalian cells. We will construct mathematical models that express biological mechanisms as testable hypotheses within a bayesian framework. For model comparison and parameter estimation, we will explore the models with markov chain monte carlo techniques [8].


Keywords: Stochastic modelling, transcription, mRNA, gene expression, bayesian inference, markov chain monte carlo

References


1. Kaern, M., et al., Nat Rev Genet, 2005. 6(6): p. 451.
2. Eldar, A., et al., Nature, 2010. 467(7312): p. 167.
3. Balazsi, G., et al., Cell, 2011. 144(6): p. 910.
4. Lestas, I., et al., Nature, 2010. 467(7312): p. 174.
5. Van Kampen, N.G., Stochastic processes in physics and chemistry. Vol. 1. 1992: Elsevier.
6. Gardiner, C., Stochastic methods. 2010: Springer.
7. Wilkinson, D.J., Nat Rev Genet, 2009. 10(2): p. 122.
8. Wilkinson, D.J., Stochastic modelling for systems biology. 2011: CRC press.

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