Presentation Title

A Computational Framework to Identify Novel Applications for Existing Drugs

Advisor Information

Tomas Helikar

Location

Dr. C.C. and Mabel L. Criss Library

Presentation Type

Poster

Start Date

6-3-2015 11:00 AM

End Date

6-3-2015 12:30 PM

Abstract

Dynamical computational models have the potential to predict system’s critical components, as well as the wide range of effects by individual perturbations. Herein, the sensitivity and perturbation effects within a large-scale computational model (139 components) of a system of widely expressed signaling pathways have been analyzed. We identified a set of network components as most and least influential on the system when perturbed, as well as components that are most and least sensitive to other perturbations. Several of the identified influential components we found to already serve as drug targets. We used this analysis to define a ranked profile of effects that these drug targets have on the rest of the network. In addition, several other network components were predicted as potential new drug targets as evidenced by their highly influential role in our perturbation analysis. These components include: beta-arrestin-1, TNF receptor-associated factor 1, TNF receptor-associated factor 2, dual specificity mitogen-activated protein kinase kinase 2, and ADP-ribosylation factor GTPase-activating protein 1. Of these proteins, beta-arrestin-1, and dual specificity mitogen-activated protein kinase kinase 2 were also identified as highly sensitive in our perturbation analyses. These sensitive and influential components offer a potential means of influencing largescale effects in the cell with minimal alteration or stabilization, and are therefore potentially viable drug targets.

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Mar 6th, 11:00 AM Mar 6th, 12:30 PM

A Computational Framework to Identify Novel Applications for Existing Drugs

Dr. C.C. and Mabel L. Criss Library

Dynamical computational models have the potential to predict system’s critical components, as well as the wide range of effects by individual perturbations. Herein, the sensitivity and perturbation effects within a large-scale computational model (139 components) of a system of widely expressed signaling pathways have been analyzed. We identified a set of network components as most and least influential on the system when perturbed, as well as components that are most and least sensitive to other perturbations. Several of the identified influential components we found to already serve as drug targets. We used this analysis to define a ranked profile of effects that these drug targets have on the rest of the network. In addition, several other network components were predicted as potential new drug targets as evidenced by their highly influential role in our perturbation analysis. These components include: beta-arrestin-1, TNF receptor-associated factor 1, TNF receptor-associated factor 2, dual specificity mitogen-activated protein kinase kinase 2, and ADP-ribosylation factor GTPase-activating protein 1. Of these proteins, beta-arrestin-1, and dual specificity mitogen-activated protein kinase kinase 2 were also identified as highly sensitive in our perturbation analyses. These sensitive and influential components offer a potential means of influencing largescale effects in the cell with minimal alteration or stabilization, and are therefore potentially viable drug targets.