Determination of Functional Network Structure from Local Parameter Dependence Data

TL;DR

This paper introduces a method to infer the structure of functional networks from local parameter dependence data, particularly useful in cellular network applications, by analyzing measured sensitivities.

Contribution

It proposes a novel approach to determine network interaction structures from sensitivity data, addressing a key challenge in cellular network analysis.

Findings

Effective in reconstructing network graphs from local sensitivity measurements

Applicable to cellular network models and similar systems

Provides a new tool for network inference from experimental data

Abstract

In many applications, such as those arising from the field of cellular networks, it is often desired to determine the interaction (graph) structure of a set of differential equations, using as data measured sensitivities. This note proposes an approach to this problem.