#### Presentation Title

Boolean network modeling of edge-of-chaos in certain biochemical networks

#### Advisor Information

Dora Matache

#### Location

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

#### Presentation Type

Poster

#### Start Date

7-3-2014 9:00 AM

#### End Date

7-3-2014 12:00 PM

#### Abstract

Biochemical networks like intracellular signaling networks in cancer tumors can be modeled using mathematical Boolean networks (BN) in which the node/cell activity can be simplified to two states, ON or OFF. The networks are comprised of different classes of Boolean functions in various proportions, including canalizing functions of different types or degrees of canalization, or threshold functions in which the activity level of the inputs of a node has to surpass a given threshold in order to activate it. In this project, we use a BN model inspired by a signal transduction network in a generic fibroblast cell to determine the sensitivity of the network to small disturbances. That way we may be able to identify certain types of proteins whose mutations by drug therapy could lead to an ordered network. Biologically, this could mean, for example, stabilizing the growth of cancer cells. We use the model to create computer simulations which allow us to analyze the sensitivity of the network for particular parameter values. We also develop a strategy for investigation of the twenty-six parameters of the current model and perform simulations to investigate a sizable sampling of the numerous parameters of the model and form hypotheses about the data to guide further investigation. This data is compiled into both a large and comprehensive table, and condensed into a more compact summary capturing the most important features of the results. These results allow us to draw conclusions about the impact of the studied parameters on the network dynamics.

Boolean network modeling of edge-of-chaos in certain biochemical networks

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

Biochemical networks like intracellular signaling networks in cancer tumors can be modeled using mathematical Boolean networks (BN) in which the node/cell activity can be simplified to two states, ON or OFF. The networks are comprised of different classes of Boolean functions in various proportions, including canalizing functions of different types or degrees of canalization, or threshold functions in which the activity level of the inputs of a node has to surpass a given threshold in order to activate it. In this project, we use a BN model inspired by a signal transduction network in a generic fibroblast cell to determine the sensitivity of the network to small disturbances. That way we may be able to identify certain types of proteins whose mutations by drug therapy could lead to an ordered network. Biologically, this could mean, for example, stabilizing the growth of cancer cells. We use the model to create computer simulations which allow us to analyze the sensitivity of the network for particular parameter values. We also develop a strategy for investigation of the twenty-six parameters of the current model and perform simulations to investigate a sizable sampling of the numerous parameters of the model and form hypotheses about the data to guide further investigation. This data is compiled into both a large and comprehensive table, and condensed into a more compact summary capturing the most important features of the results. These results allow us to draw conclusions about the impact of the studied parameters on the network dynamics.