Presentation Title

The Impact of Perturbations on Biochemical Signal Transduction Networks

Advisor Information

Tomas Helikar

Location

UNO Criss Library, Room 225

Presentation Type

Oral Presentation

Start Date

7-3-2014 1:30 PM

End Date

7-3-2014 1:45 PM

Abstract

Anomalies within signal transduction networks can greatly affect a cell’s function and result in disease. Approaching these anomalies from a systems perspective means studying the networks as a whole, rather than its constituents in isolation. Dynamical computer models of complex biological/biochemical processes can be simulated under thousands of environmental conditions, including diseased states, which can result in novel and improved drug therapies. Herein, we present a computational approach to study the systematic effects of various perturbations on a network as a whole using the Cell Collective (www.thecellcollective.org) platform, which allows laboratory scientists from all over the world to collaboratively build and simulate large models of different cell types. R statistical tool was used to analyze accumulated data from the Cell Collective of a large-scale dynamic model of signal transduction in fibroblast cells. Under death, growth, motility, quiescence, and random external conditions, we have identified proteins that have the most and least influence on the rest of the network, as well as proteins that are most and least susceptible to these perturbations. Also, we found proteins that are most and least sensitive to perturbations. We have also found the combination of protein properties (e.g., in-/out-degree, canalizing functions, etc.) is a better predictor for perturbation effects on the network than each individual property. Together, this supports the notion that dynamic, mechanism-based models allow for insight into potential identification of novel drug targets as well as the side effects of existing drugs.

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Mar 7th, 1:30 PM Mar 7th, 1:45 PM

The Impact of Perturbations on Biochemical Signal Transduction Networks

UNO Criss Library, Room 225

Anomalies within signal transduction networks can greatly affect a cell’s function and result in disease. Approaching these anomalies from a systems perspective means studying the networks as a whole, rather than its constituents in isolation. Dynamical computer models of complex biological/biochemical processes can be simulated under thousands of environmental conditions, including diseased states, which can result in novel and improved drug therapies. Herein, we present a computational approach to study the systematic effects of various perturbations on a network as a whole using the Cell Collective (www.thecellcollective.org) platform, which allows laboratory scientists from all over the world to collaboratively build and simulate large models of different cell types. R statistical tool was used to analyze accumulated data from the Cell Collective of a large-scale dynamic model of signal transduction in fibroblast cells. Under death, growth, motility, quiescence, and random external conditions, we have identified proteins that have the most and least influence on the rest of the network, as well as proteins that are most and least susceptible to these perturbations. Also, we found proteins that are most and least sensitive to perturbations. We have also found the combination of protein properties (e.g., in-/out-degree, canalizing functions, etc.) is a better predictor for perturbation effects on the network than each individual property. Together, this supports the notion that dynamic, mechanism-based models allow for insight into potential identification of novel drug targets as well as the side effects of existing drugs.