Information Surfaces in Systems Biology and Applications to Engineering Sustainable Agriculture

TL;DR

This paper introduces Information Surfaces, a novel multi-resolution analytical framework inspired by eigen-mode analysis, to explore complex gene network dynamics in plants, aiding sustainable agriculture development.

Contribution

It presents a new theory that organizes gene network relationships across scales, enabling discovery of functional properties before estimating optimal scales.

Findings

Organizes gene network relationships across multiple scales.

Allows investigation of complex dynamics prior to scale estimation.

Supports applications in crop development for sustainable agriculture.

Abstract

Systems biology of plants offers myriad opportunities and many challenges in modeling. A number of technical challenges stem from paucity of computational methods for discovery of the most fundamental properties of complex dynamical systems. In systems engineering, eigen-mode analysis have proved to be a powerful approach. Following this philosophy, we introduce a new theory that has the benefits of eigen-mode analysis, while it allows investigation of complex dynamics prior to estimation of optimal scales and resolutions. Information Surfaces organizes the many intricate relationships among "eigen-modes" of gene networks at multiple scales and via an adaptable multi-resolution analytic approach that permits discovery of the appropriate scale and resolution for discovery of functions of genes in the model plant Arabidopsis. Applications are many, and some pertain developments of crops that sustainable agriculture requires.