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Specific Aims:
  • Understand how cellular decisions are made at the molecular level.
  • Elucidate the structure and properties of molecular and control networks.
  • Understand the development of cell polarity.
  • Explain how cells sense and respond to their external environment.
  • Determine the mechanisms that give rise to large-scale cell migration and the patterning of differentiation.
  • Understand how cell and extracellular matrix (ECM) properties interact with tissue geometry to give rise to specific function.


The following projects combine quantitative experiments and computer simulation and build on the mutually complementary strength of the researchers at Notre Dame, with support from our collaborators at other institutions:


All research combines three methodologies, which apply to all four projects:

  • Quantitative experimentation, especially tracing of gene activity and protein distribution during development, using quantitative PCR and immunolabeling, tracking of cell membrane fluctuations and cell migration during embryogenesis, using fluorescence labeling and two-photon confocal microscopy, determination of three dimensional tissue structures using CT and Magnetic Resonance Imaging (MRI), and measurements of mechanical properties at all scales using intracellular magnetic tweezers, optical tweezers, force microbalance, and rheometry.
  • Development of mathematical models, e.g. continuum reaction-diffusion models of diffusible morphogens, reaction-kinetic models of regulatory networks, and hybrid molecular dynamics/continuum models of cytoskeleton and ECM protein polymerization. Such modeling also requires more fundamental applied mathematical and statistical physics understanding of the emergent properties of complex networks, e.g. bifurcation analysis of coupled interacting regulatory modules.
  • Detailed computer simulation, e.g. finite element modeling of actin fibers or ECM, Potts model simulation of cell migration, and multiscale simulation of interrelated developmental processes. Models employ experimentally measurable parameters and make specific testable predictions of experimental phenomena, e.g. of gene knock out experiments, in vivo up or down regulation of gene expression, or in vitro reconstitution experiments. Simulation in turn suggests experimental measurements and helps to interpret complex experiments.

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Last Updated: Monday, June 10, 2013
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