Information Fragmentation, Encryption and Information Flow in Complex Biological Networks

TL;DR

This paper introduces an information-theoretic tool called 'information fragmentation analysis' to localize, quantify, and visualize information flow and encryption in complex biological networks, enhancing understanding of their information processing.

Contribution

The paper presents a novel method for analyzing information localization, fragmentation, and encryption in biological networks using full phenotypic data, providing deeper insights into their information processing.

Findings

Information fragmentation analysis localizes information in networks.

The method reveals how information is fragmented across nodes.

It quantifies the level of encryption and processing complexity.

Abstract

Assessing where and how information is stored in biological networks (such as neuronal and genetic networks) is a central task both in neuroscience and in molecular genetics, but most available tools focus on the network's structure as opposed to its function. Here we introduce a new information-theoretic tool: "information fragmentation analysis" that, given full phenotypic data, allows us to localize information in complex networks, determine how fragmented (across multiple nodes of the network) the information is, and assess the level of encryption of that information. Using information fragmentation matrices, we can also create information flow graphs that illustrate how information propagates through these networks. We illustrate the use of this tool by analyzing how artificial brains that evolved "in silico" solve particular tasks, and show how information fragmentation analysis provides deeper insights into how these brains process information and "think". The measures of information fragmentation and encryption that result from our methods also quantify complexity of information processing in these networks and how this processing complexity differs between primary exposure to sensory data (early in the lifetime) and later routine processing.