Modelling transcriptional dynamics in human T-Cells using next generation sequencing data
Lead Research Organisation:
University College London
Department Name: Institute of Child Health
Abstract
We intend to develop integrated mathematical models which harness the power of cutting edge
technology, next generation sequencing (NGS), to explain how cells respond to different stresses. In
multicellular organisms, including humans, a system of checks and balance prevents damaged cells
from multiplying. Failure of these systems results in cancer, where cells proliferate unchecked. We
will study how cells ?decide? between life and death. The technology we will use allows us to
simultaneously measure, with great accuracy, the levels of all gene products in the genome. We will
analyse these measurements by developing mathematical models run on computers, which will lead
to a better understanding of how gene activity in stressed cells determines their fate. Because gene
activity is important for all healthy and diseased states, the techniques we will develop will be widely
applicable in other biomedical contexts. The approaches allow a much more efficient use of
experimental materials and a more accurate use of the complex data generated by the latest
technology. Ultimately these methods will greatly improve our understanding of diseases like genetic
disorders, complex diseases like heart disease and cancer.
technology, next generation sequencing (NGS), to explain how cells respond to different stresses. In
multicellular organisms, including humans, a system of checks and balance prevents damaged cells
from multiplying. Failure of these systems results in cancer, where cells proliferate unchecked. We
will study how cells ?decide? between life and death. The technology we will use allows us to
simultaneously measure, with great accuracy, the levels of all gene products in the genome. We will
analyse these measurements by developing mathematical models run on computers, which will lead
to a better understanding of how gene activity in stressed cells determines their fate. Because gene
activity is important for all healthy and diseased states, the techniques we will develop will be widely
applicable in other biomedical contexts. The approaches allow a much more efficient use of
experimental materials and a more accurate use of the complex data generated by the latest
technology. Ultimately these methods will greatly improve our understanding of diseases like genetic
disorders, complex diseases like heart disease and cancer.
Technical Summary
Background: Recent advances in sequencing technology (Next Generation Sequencing, or NGS) allow
us to measure with great precision and exhaustively, the transcriptional activity in cells. It is essential to
develop concomitant mathematical and bioinformatic techniques to deal with this abundance of data and
exploit it to its full potential. In this project, we will use as an example system the DNA-damage response
network in human T-cells. Understanding this system in a dynamical way is important since it comprises
p53, a (mostly) pro-apoptotic transcription factor often described as the ?guardian of the genome? that is
mutated in about half of human cancers.
Aim: to combine genome scale data, mathematical modelling and bioinformatics to generate essential for
translating the potential of the new genomics technologies into true medical advances.
Objectives: (1) To develop measurement error models for next generation sequencing data (RNA-Seq
and RNA-SAGE). (2) To develop accurate dynamic models of mRNA production and degradation
including non-linear models and multiple transcription factor dependency. (3) To develop procedures
allowing an assessment of the appropriate parametrisation of dynamic models.
In broad terms, the first objective is about understanding the level of reproducibility attached to NGS.
The second objective is about linking this data to models. It is the models that will provide intimate
understanding and quantification of the mechanisms behind the production and degradation of messenger
RNA. The third objective concentrates on the generation of effective and actionable models.
Design and methodology: Four time courses of DNA damage-stressed cells will be generated and
measured using NGS. This data will be analysed dynamically with ordinary differential equation models.
Scientific opportunity: This project is eminently interdisciplinary, bridging together the latest advances
in biotechnology with mathematical modelling. The chosen biological system is biomedically relevant
and the methods developed in the course of this project will be widely applicable.
us to measure with great precision and exhaustively, the transcriptional activity in cells. It is essential to
develop concomitant mathematical and bioinformatic techniques to deal with this abundance of data and
exploit it to its full potential. In this project, we will use as an example system the DNA-damage response
network in human T-cells. Understanding this system in a dynamical way is important since it comprises
p53, a (mostly) pro-apoptotic transcription factor often described as the ?guardian of the genome? that is
mutated in about half of human cancers.
Aim: to combine genome scale data, mathematical modelling and bioinformatics to generate essential for
translating the potential of the new genomics technologies into true medical advances.
Objectives: (1) To develop measurement error models for next generation sequencing data (RNA-Seq
and RNA-SAGE). (2) To develop accurate dynamic models of mRNA production and degradation
including non-linear models and multiple transcription factor dependency. (3) To develop procedures
allowing an assessment of the appropriate parametrisation of dynamic models.
In broad terms, the first objective is about understanding the level of reproducibility attached to NGS.
The second objective is about linking this data to models. It is the models that will provide intimate
understanding and quantification of the mechanisms behind the production and degradation of messenger
RNA. The third objective concentrates on the generation of effective and actionable models.
Design and methodology: Four time courses of DNA damage-stressed cells will be generated and
measured using NGS. This data will be analysed dynamically with ordinary differential equation models.
Scientific opportunity: This project is eminently interdisciplinary, bridging together the latest advances
in biotechnology with mathematical modelling. The chosen biological system is biomedically relevant
and the methods developed in the course of this project will be widely applicable.