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 FcRI system presents an additional challenge relative to other commonly studied receptor systems (i.e. EGFR, VEGFR), in that the FcRI/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 #
5R01GM104973-04
Application #
8892207
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
2015-05-01
Budget End
2016-04-30
Support Year
4
Fiscal Year
2015
Total Cost
$311,873
Indirect Cost
$63,957
Name
University of New Mexico
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
868853094
City
Albuquerque
State
NM
Country
United States
Zip Code
87106
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
Valley, Christopher C; Arndt-Jovin, Donna J; Karedla, Narain et al. (2015) Enhanced dimerization drives ligand-independent activity of mutant epidermal growth factor receptor in lung cancer. Mol Biol Cell 26:4087-99
Yim, Hyung-Soon; Cho, Yun Sung; Guang, Xuanmin et al. (2014) Minke whale genome and aquatic adaptation in cetaceans. Nat Genet 46:88-92
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
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
Costa, Michelle N; Radhakrishnan, Krishnan; Edwards, Jeremy S (2011) Monte Carlo simulations of plasma membrane corral-induced EGFR clustering. J Biotechnol 151:261-70