Combined Experimental in addition to Computational Modeling Studies at the Example of ErbB

Combined Experimental in addition to Computational Modeling Studies at the Example of ErbB www.phwiki.com

Combined Experimental in addition to Computational Modeling Studies at the Example of ErbB

Wilson, Jean, Outdoor Columnist has reference to this Academic Journal, PHwiki organized this Journal Combined Experimental in addition to Computational Modeling Studies at the Example of ErbB Family Birgit Schoeberl How do perturbations affect the network A431

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A431 in addition to other tumor cell lines

Model focused on underst in addition to ing the quantitative contributions from homo- in addition to hetero-dimers of ErbB1,2,3, in addition to 4. Mechanistic model based on biochemical reactions in addition to relevant data, described by ordinary differential equations (ODE).

Facts about the Model Compartment model (plasma membrane, endosomes/cytosol) Based on elementary biochemical reactions -> automatic model generation ODEs with ~501 states in addition to up to 130 kinetic parameters describing the detailed biochemical reaction network Models need quantitative Biology Volume of the cells Receptor Numbers Protein Concentration Need as long as new methods: quantitative Westernblots high throughput assays (protein assays) Accurate high throughput analysis of signaling

Adapted from Yarden in addition to Sliwkowski 2001 Different Coexpression Patterns found in Non-Small Lung Cancer (NSCL) High ErbB1 High ErbB2 Low ErbB1 Low ErbB2 Low ErbB1 High ErbB2 High ErbB1 Low ErbB2 22% 18% 11% 49% Franklin et. Al., Seminars in Oncology, 2002 EGF Affinities Monomer: KD ErbB1 0.1-1nM Dimer: ErbB1:ErbB2 1-100nM ErbB2:ErbB3 20nM ErbB2:ErbB4 1-100nM

General Notion ErbB2 potentiates in addition to prolongs the output signal (ERK, AKT). (Graus-Prota:1997) ErbB1 expression is of no prognostic significance. (Franklin, Seminars in Oncology, 2002) It maybe important in clinical trials to quantitatively assess relative levels of both receptors to predict optimal responses to drugs in addition to biologic targeting RTK pathways. (Franklin, Seminars in Oncology) Training the Model A431: Model Validation: Simulation of ErbB1 – Inhibition

A431: Model Validation: ErbB1 – Inhibition Simulation + Experimental Validation A431: Model Validation: ErbB1 – Inhibition Simulation + Experimental Validation A431: Model Validation: Simulation of ErbB2 – Inhibition

A431: Model Validation: ErbB2 – Inhibition Simulation + Experimental Validation KI1 high affinity Effect of ErbB1, ErbB2 in addition to ErbB4 Inhibition on A431 cells KI1 low affinity ErbB1 inhibition most effective ! 100% ERK:P:P 0% ERK:P:P predictions verified in other tumor cells with different receptor setup Model predicts ERK:P:P as long as different cell lines

Wilson, Jean Yuma Daily Sun Outdoor Columnist www.phwiki.com

predictions verified in other tumor cells with different receptor setup Model predicts ERK:P:P as long as different cell lines Influence of ErbB2 receptor number as long as different cell lines 1e6 ErbB1 7e4 ErbB1

Maximal ERK activation as function of ErbB1 in addition to ErbB3 expression + ErbB2 Inhibitor ErbB2:3e5 ERK:P:P @ 5min Model trained as long as HRG in A431

Summary & Conclusions Different protein/receptor expression levels have large impact on signal response Tumor cells use alternative pathways to ensure their proliferative capacity: ErbB1 replaces / supports ErbB3 Tumor cells amplify the signal by using ErbB2 if the number of ErbB1 or ErbB3 receptors is small. ErbB2 is very important as long as HRG induced signaling. Inhibitor selection is dependent on receptor expression in addition to the lig in addition to (s) (concentration / type) -> Characterization of tumors is important Acknowledgements Ulrik B. Nielsen, Merrimack Pharmaceuticals Jack Beusmans, David DeGraaf, AstraZeneca Douglas Lauffenburger Peter Sorger

Wilson, Jean Outdoor Columnist

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