Genome-wide dynamical modelling differentially-regulated systems with application to the human T cell response


CALL: 2014

DOMAIN: BM - Translational Biomedical Research


LAST NAME: Gonçalves



HOST INSTITUTION: University of Luxembourg

KEYWORDS: control systems, time-series, dynamical systems, system identification, biochemical networks, causality, T cells

START: 2015-09-01

END: 2018-08-31


Submitted Abstract

Biomedical researchers have already made enormous advance in understanding the biological functions of individual genes, proteins and metabolites. However, the core question how different molecules interact each other to regulate certain physiological and pathological processes of different cellular types still largely remain unknown. Discovery of integrated molecular networks will guide us through personal medicine by pinpointing the exact genetic pathogenesis of diseases and suggestions of new therapies.To address this question, this project develops new mathematical tools derived from control systems to exploit time-series data and extract dynamical network information. Time-series data also include causality information, which will be modelled explicitly, relaying information on whom is regulating whom. In addition, the project develops tools to analyse differential regulatory systems, in contrast to the well established but limited tools on differential expression.The difference is that these new tools can pinpoint the exact location of system perturbations (such as gene or drug perturbations) or in between different systems (such as different cellular types). The mathematical tools are efficient to the point that they can be applied to the genome level.While the tools are general and can be applied to any organism and data, to illustrate the tool, this project will concentrate on understanding differences between the gene regulatory networks of two types of CD4 T cells: CD4+CD25highFOXP3+regulatory T cells (Tregs) and CD4+CD25- effective T cells (Teffs). While the role of each cellular type is well understood, the precise molecular mechanisms regulating their functions following activation still remain elusive. When these mechanisms are defective it can lead to various immune related diseases. In this project, we will provide insight into the understanding of the detailed causal regulatory networks, which might lead to the development of more effective drug targets against immune related diseases and improvement of therapeutic strategies.

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