The development of mathematical tools to simulate signaling dynamics is extremely important. Based on our previous experimental data and simulation results, we are convinced that to accurately and appropriately model signaling networks, we cannot neglect the spatial organization of the cell membrane (and ultimately the cytosol as well). We have extensive experience in developing spatially realistic simulations of the cell membrane and studied the initiation of signaling. However, while these simulations are extremely computationally intense, crucial aspects of cell signaling can only be correctly represented in the context of the entire cell membrane, or at least a significant portion of it. Th Fc?RI system presents an additional challenge relative to other commonly studied receptor systems (i.e. EGFR, VEGFR), in that the Fc?RI/IgE complex essentially behaves as a bivalent receptor, while the antigens are typically multivalent (valency from 3 to ?24). As a result, arbitrarily large aggregates may emerge through the multivalent cross-linking of FceRI/IgE complexes by the antigen. Due to the combinatorial explosion of the number of possible aggregate types, this problem is eminently suited for the rule-based approach to bio-molecular network modeling. Thus, the nature of the FceRI/IgE system requires the integration of the rule-based approach with a spatial modeling framework. We will develop a coarse grained methodology for integration of detailed (microscopic) and cell-level (mesoscopic) simulations and experimental results. This framework will help address both challenges described above, namely (a) integrate detailed, microscopic simulations and experimental data into a mesoscopic model that captures a significant portion of the cell membrane and (b) provide a mechanism to include spatial mobility and steric constraints for large molecular aggregates in a rule-based, stochastic model of the Fc epsilon RI system.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM104973-02
Application #
8503615
Study Section
Special Emphasis Panel (ZGM1-CBCB-5 (BM))
Program Officer
Chin, Jean
Project Start
2012-07-05
Project End
2016-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
2
Fiscal Year
2013
Total Cost
$325,404
Indirect Cost
$76,515
Name
University of New Mexico Health Sciences Center
Department
Genetics
Type
Schools of Medicine
DUNS #
829868723
City
Albuquerque
State
NM
Country
United States
Zip Code
87131
McCabe Pryor, Meghan; Steinkamp, Mara P; Halasz, Adam M et al. (2015) Orchestration of ErbB3 signaling through heterointeractions and homointeractions. Mol Biol Cell 26:4109-23
Yim, Hyung-Soon; Cho, Yun Sung; Guang, Xuanmin et al. (2014) Minke whale genome and aquatic adaptation in cetaceans. Nat Genet 46:88-92
Pryor, Meghan McCabe; Low-Nam, Shalini T; Halász, Adám M et al. (2013) Dynamic transition states of ErbB1 phosphorylation predicted by spatial stochastic modeling. Biophys J 105:1533-43
Halasz, Adam M; Lai, Hong-Jian; McCabe, Meghan M et al. (2013) Analytical Solution of Steady State Equations for Chemical Reaction Networks with Bilinear Rate Laws. IEEE/ACM Trans Comput Biol Bioinform :
Halász, Adám M; Lai, Hong-Jian; McCabe Pryor, Meghan et al. (2013) Analytical solution of steady-state equations for chemical reaction networks with bilinear rate laws. IEEE/ACM Trans Comput Biol Bioinform 10:957-69
Chen, Ye; Short, Christopher; Halász, Adám M et al. (2013) The impact of high density receptor clusters on VEGF signaling. Electron Proc Theor Comput Sci 2013:37-52
Costa, Michelle N; Radhakrishnan, Krishnan; Edwards, Jeremy S (2011) Monte Carlo simulations of plasma membrane corral-induced EGFR clustering. J Biotechnol 151:261-70