PhD position in computational systems biology

Updated: 4 months ago
Job Type: FullTime
Deadline: 19 Apr 2020

PhD position in computational systems biology

The Molecular Systems Biology group at the University of Groningen (Netherlands) has an opening for an enthusiastic and talented PhD student. The University of Groningen, located in the north of the Netherlands, enjoys an international reputation as one of the oldest and leading research universities in Europe.

The Molecular Systems Biology group aims at generating a systems-level understanding about the functioning of metabolism (Prof. Matthias Heinemann) and of growth regulation by TOR in budding yeast (Dr. Andreas Milias-Argeitis). Towards these goals, the group members combine classical and systems biology approaches exploiting latest state-of-the-art single cell technologies such as microfluidics and optogenetics. Together, the members of the international and interdisciplinary team (PhD students and postdocs with backgrounds in biology, engineering, physics and mathematics) create an inspiring and highly collaborative research atmosphere. The project description for the currently open position is provided below.

Project title : Parameter estimation and model selection for heterogeneous cell populations

Description: Uncertainty is encountered in every aspect of biochemical network modeling: intracellular noise leads to cellular heterogeneity, low-quality experimental data lead to parametric uncertainties, while incomplete mechanistic knowledge results in structural ambiguities in models. Consequently, model predictions typically display large variability that complicates further inference and analysis steps.

Our group is currently developing computationally efficient methods for carrying out uncertainty propagation and quantification in ODE-based systems, using a range of statistical approaches such as surrogate models (Gaussian processes) and moment-based techniques. At the same time, we are generating a wealth of single-cell, time-lapse microscopy data on the dynamics of the TORC1 signaling pathway in budding yeast upon various perturbations and during the cell cycle.

In this project, we will use our developed methods as a basis for the construction of parameter estimation and model selection algorithms for biochemical networks affected by cell-to-cell variability. The developed algorithms will be employed to evaluate the effects of different dynamic input perturbations, with the goal of determining experiments that can maximize the information on particular aspects of a given system. Our ultimate goal is to generate dynamical models for the regulation of key targets of the yeast TORC1 signaling pathway, making use of our rich single-cell microscopy data.

Tools and methods : Dynamical systems theory, probability theory, Bayesian inference, Monte Carlo methods

Supervisor: Dr. A. Milias-Argeitis

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