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Understand how cellular decisions are made at the molecular level.
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Elucidate
the structure and properties of molecular and control networks.
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Understand
the development of cell polarity.
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Explain
how cells sense and respond to their external environment.
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Determine
the mechanisms that give rise to large-scale cell migration and the
patterning of differentiation.
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Understand
how cell and extracellular matrix (ECM) properties interact with tissue
geometry to give rise to specific function.
Projects:
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:
Methodologies:
All
research combines three methodologies, which apply to all four projects:
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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.
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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.
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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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