Modelling and Analysis of Gene Expression Oscillations during Neural Development
Event details
| Date | 24.06.2016 |
| Hour | 11:00 |
| Speaker | Nick Phillips, Ph.D., Nancy Papalopulu lab, University of Manchester (UK) |
| Location | |
| Category | Conferences - Seminars |
BIOENGINEERING SEMINAR
Abstract:
The timing of differentiation underlies the development of any organ system. In neural development the expression of the transcription factor Hes1 has been shown to be oscillatory in neural progenitors, but it is expressed at a low steady state in differentiated neurons. We previously constructed a mathematical model to describe how oscillations of Hes1 are controlled and terminated in neural development using deterministic delay differential equations.
However, biochemical reactions are the result of random encounters between discrete numbers of molecules, and some of these molecules may be present at low numbers. The finite number of molecules interacting within the system leads to inherent randomness, and this is known as intrinsic stochasticity.
Firstly, I will show that stochasticity has the effect of systematically changing the timing of differentiation within the model. Secondly, I will introduce a new analysis method using Gaussian Processes to classify gene expression as oscillatory or non-oscillatory within single cell live imaging data generated within the lab. Finally, I will show that the combination of stochasticity, delay and nonlinearity leads to emergent dynamics that are not understood at a theoretical level. We develop a theory to explain these effects and apply it to a simple model of gene regulation.
Abstract:
The timing of differentiation underlies the development of any organ system. In neural development the expression of the transcription factor Hes1 has been shown to be oscillatory in neural progenitors, but it is expressed at a low steady state in differentiated neurons. We previously constructed a mathematical model to describe how oscillations of Hes1 are controlled and terminated in neural development using deterministic delay differential equations.
However, biochemical reactions are the result of random encounters between discrete numbers of molecules, and some of these molecules may be present at low numbers. The finite number of molecules interacting within the system leads to inherent randomness, and this is known as intrinsic stochasticity.
Firstly, I will show that stochasticity has the effect of systematically changing the timing of differentiation within the model. Secondly, I will introduce a new analysis method using Gaussian Processes to classify gene expression as oscillatory or non-oscillatory within single cell live imaging data generated within the lab. Finally, I will show that the combination of stochasticity, delay and nonlinearity leads to emergent dynamics that are not understood at a theoretical level. We develop a theory to explain these effects and apply it to a simple model of gene regulation.
Practical information
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- Free