(636a) Molecular Systems Biology Via Multiscale Modeling And High-Performance Computing

Radhakrishnan, R., University of Pennsylvania
Shih, A., University of Pennsylvania
Purvis, J., University of Pennsylvania
Liu, Y., University of Pennsylvania
Agrawal, N., University of Pennsylvania

Recent biochemical and epidemiological studies have shown that the signaling through the epidermal growth factor receptor (EGFR) can be sensitive to various tyrosine kinase inhibitors (TKIs) depending on the receptor's expression level and whether or not the tyrosine kinase domain harbors any somatic mutations. The rationale behind the varied response is still unclear, i.e., why certain cell-lines are hyper-sensitive to TKI treatment or how these inhibitors affect cell-wide signaling patterns, in particular, the attenuation of the oncogenic growth signals. We recently developed a hierarchical multiscale computational approach based on molecular dynamics simulations, free energy based molecular docking simulations, deterministic network-based kinetic modeling, and hybrid discrete/continuum stochastic dynamics protocols to study the dimer-mediated receptor activation characteristics, signal transduction, and inhibition of the Erb family receptors, specifically the epidermal growth factor receptor (EGFR) [1]. Extending this previous work, here we predict the inhibitory effects on receptor phosphorylation and downstream signaling response [2]. Our results suggest that the increased drug sensitivity observed for a commonly mutated form of EGFR, L834R, can be attributed to its altered kinetic behavior during receptor phosphorylation. The also model predicts the kinase inhibitor to be more effective in the L834R system under over-expression of EGFR. Additionally, we find that the TKI erlotinib is more effective in inhibiting the Akt response. Our modeling results are compared and validated against ?multiscale? experiments ranging from crystallography, phosphorylation kinetics using time-of-flight mass spectroscopy, and cell-based immuno assays with overall qualitative agreement and quantitative agreement where possible. We believe that the multiscale computational framework described here is ideal for assessing mutation landscape on signal transduction. We also believe that our model driven approach will in the long-term significantly impact the optimization of future small molecule therapeutic inhibition strategies as well as the formulation of drug-resistance models.

Y. Liuµ, J. Purvisµ, A. Shih, J. Weinstein, N. Agrawal, R. Radhakrishnan, ?A multiscale computational approach to dissect early events in the Erb family receptor mediated activation, preferential signaling, and relevance to oncogenic transformations?, 2007, Annals of Biomedical Engineering, in press, DOI: 10.1007/s10439-006-9251-0; µ these authors contributed equally.

J. Purvis, Y. Liu, V. Ilango, and R. Radhakrishnan, Untangling the efficacy of tyrosine kinase inhibitors in the mutants of the epidermal growth factor receptor through a multiscale molecular/ systems model for phosphorylation and inhibition, submitted to IEE Sys Biol.