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Hi, I'm Andreas Piehler and this is my personal website. I have a degree in physics from the University Vienna, where I also had my first research experience at the Molecular Systems Biology Group on "Flux Balance Analysis". I did my Bachelor Thesis at the IST Austria on "Optimality of Gene Expression Levels" under supervision of Tobias Bollenbach. Thereafter, I held a position as Marie Curie Fellow at the University of Warwick, where I resigned from soon after my Marie Curie Secondment, mostly spent in Matthias Heinemann's lab. After a visit in the research group of Ramon Grima, I started a research project together with Peter Swain and Ramon in terms of a MSc by Research in Cell and Molecular Biology at the University Edinburgh. We combine theory and experiment using the microfluidic platform ALCATRAS for single-cell time laps measurements of budding yeast and modelling of stochastic processes. This website contains some basic knowledge about physics and is thought to be a compendium summarizing topics of my main interests, which include Systems Biology, Biological Physics, Synthetic Biology, Complex Systems and Stochastic Processes.






























Interests

  • Understanding the structure and dynamics of metabolic control systems (MCSs), gene regulatory networks (GRNs), protein interaction networks (PINs)
  • How can we deal with small copy numbers of molecules and its effects (beakdown of: -the law of large numbers -law of diffusion -law of mass action, concept of equilibrium becomes shaky)? Can new concepts help us understand what determines cell fate?
  • How does the dynamics of a network change throughout the parameter space? What are evolutionary constraints that confine the parameter space? (see also network motifs, pareto front)
  • Can control theory be used to identify the network structure and parameters through experiments (Realization, System Identification, Pulse response)?
  • How can the concentration and interaction of metabolites, transcripts, proteins, ... be measured - how do we derive the network structure and dynamics efficiently? (microarrays, sequencing, qPCR, fluorescent reporters, FRET, Luciferase, 13C MFA, Mass Spectrometry, Chromatography, FACS, Surface Plasmon Resonance, ...)