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\n Bio
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“Mechanistic Modeling of Signal Transduction and Dynamic Cell
Morphologies”
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Dr. Meier-Schelle
rsheim’s research group has been developing tools for quantitative computa
tional image analysis and mechanistic modeling of cellular behavior for se
veral years now. They were able to develop computational methods and tools
that permit the simulation of cellular signaling networks embedded into d
ynamic\, realistic 3D morphologies to take into account the coupling betwe
en cellular biochemistry and morphological dynamics. The simulation platfo
rm Simmune was the first modeling software to permit this degree of realis
m and we have continuously been improving its capabilities with regard to
the size of multi-cellular systems that can be simulated and parameter sca
ns that can be performed on distributed computer systems. Simmune can mode
l cells that express receptors for chemosensing and adhesion on their surf
ace that react to receptor mediated stimuli by adjusting their geometry to
adhere to extracellular structures or by directed migration in response t
o chemotactic signals. Although the coupling between biochemistry and cell
motion based on Potts model rules is phenomenological applying such simul
ation techniques to explore the role cell-cell and cell-matrix interaction
s may play in complex 3D structures is a first step towards understanding
how chemical and mechanical cues regulate cellular migration. Dr. Meier-Sc
hellersheim’s group develops and applies quantitative image analysis tools
to extract the information needed for detailed spatially resolved simulat
ions directly in an unbiased and automated manner from image data. Having
been part of the Laboratory of Systems Biology for several years now\, his
group acquired considerable experience in coordinating interdisciplinary
work and in handling large heterogeneous data sets.
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In addition to
exploring the interplay between cell morphology and cellular responses to
wards stimuli\, Dr. Meier-Schellersheim’s group develops and applies tools
that can perform systematic analyses of the behavior of computational mod
els of cellular signaling pathways. These tools can identify which element
s of a pathway model are responsible for reproducing specific features in
the experimentally observed cellular behavior.
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To join t
he live event please request the link by emailing: icm@jhu.edu
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▶RECORDING
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\n Abstract
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\n“Mechanistic Modeling of Signal Transduction and D
ynamic Cell Morphologies”
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p>\n
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\nDetailed mechanistic models of cellular s
ignaling pathways have the advantage that the conclusions they allow us to
draw can be tested with little ambiguity. However\, building such models
typically requires more data and involves far more parameters than phenome
nological approaches do. Using examples from cytokine and growth factor si
gnaling\, I will discuss some recent progress in applying detailed modelin
g tools and describe what can be learned from models whose parameters cann
ot be uniquely determined. Then\, I will show how detailed models of intra
cellular biochemistry can be linked to a realistic treatment of morphologi
cal dynamics using a novel approach for representing cellular surfaces.
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\n▶RECORDING
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