# Determinative Power of Nodes in a Family of Signal Transduction Networks

## Advisor Information

Dora Matache

## Location

UNO Criss Library, Room 231

## Presentation Type

Oral Presentation

## Start Date

4-3-2016 9:15 AM

## End Date

4-3-2016 9:30 AM

## Abstract

This study considers a recently introduced method for reducing the size of a logical network by considering its most determinative nodes, that is, nodes that yield the highest information gain in the network. The knowledge of the states of the most determinative nodes reduces the entropy or uncertainty of the overall network significantly. We find the most determinative nodes in a family of signal transduction networks available in the online database The Cell Collective (http://thecellcollective.org) to identify similarities and differences between the different networks. In general, signal transduction networks have very large sizes which makes the analysis of their dynamics prohibitive. Therefore, algorithms for network reduction have been the subject of recent research. For the networks under consideration we note that the reduction of entropy becomes insignificant once we select enough nodes with large determinative power. This allows us to generate an algorithm for the network reduction to a smaller sub-network, and to categorize the networks into two types: (I) nonlinear decrease in entropy, which leads to sub-networks of fairly small sizes; (II) almost linear decrease in entropy, which leads to sub-networks that have larger sizes in comparison to the whole network.

Determinative Power of Nodes in a Family of Signal Transduction Networks

UNO Criss Library, Room 231

This study considers a recently introduced method for reducing the size of a logical network by considering its most determinative nodes, that is, nodes that yield the highest information gain in the network. The knowledge of the states of the most determinative nodes reduces the entropy or uncertainty of the overall network significantly. We find the most determinative nodes in a family of signal transduction networks available in the online database The Cell Collective (http://thecellcollective.org) to identify similarities and differences between the different networks. In general, signal transduction networks have very large sizes which makes the analysis of their dynamics prohibitive. Therefore, algorithms for network reduction have been the subject of recent research. For the networks under consideration we note that the reduction of entropy becomes insignificant once we select enough nodes with large determinative power. This allows us to generate an algorithm for the network reduction to a smaller sub-network, and to categorize the networks into two types: (I) nonlinear decrease in entropy, which leads to sub-networks of fairly small sizes; (II) almost linear decrease in entropy, which leads to sub-networks that have larger sizes in comparison to the whole network.